Applied Science, Faculty of
Biomedical Engineering, School of
DSpace
UBCV
Modara, Gelareh
Master of Applied Science - MASc
University of British Columbia
Touchscreen-based cognitive testing has become a popular tool for assessing a wide spectrum of neuropsychological functions in both human and animal models. Specifically, rodent touchscreen paradigms were pioneered in the 1990s and have undergone significant refinement, with modern commercial systems—such as the Bussey-Saksida system—offering advanced features and greater standardization. Commercially available rodent touchscreen chambers have become very popular tools, benefiting from consistent hardware, software, and training protocols. These systems offer many advantages, including automated data collection, high throughput, strong translatability, and improved standardization across studies—contributing to more replicable and reliable outcomes. However, their high cost presents a significant financial barrier to widespread adoption. Recent open-source initiatives address these limitations by leveraging affordable components (e.g., Raspberry Pi, Arduino) and publicly shared designs and software. Projects such as the ArduiPod Box, the Raspberry Pi touchscreen operant chamber, and Visiomode illustrate different approaches to creating cost-efficient, customizable systems. Despite offering substantial financial and design flexibility, these setups often face their own challenges, including limited user interfaces, data handling constraints, and incomplete hardware documentation. Consequently, there remains a need for an open-source solution that combines user-friendliness, robustness, scalability, and aesthetic appeal while maintaining affordability. Therefore, we developed a customizable, cost-efficient (approximately $500) cognitive touchscreen chamber called NC4Touch for mouse and rat behavioral experiments. It features capacitive touchscreens alongside a feeding port, allowing straightforward training for tasks such as visual pairwise discrimination. Moreover, the user-friendly graphical interface provides real-time control and customization of tasks, supports video recording, and includes robust data management features. We validated the chamber’s operational efficiency and refined training protocols for pairwise visual discrimination tasks in multiple cohorts of rodents. Constructed using readily available microcontrollers, hobbyist-grade hardware, and a custom-built housing, this open-source apparatus offers an accessible solution comparable in performance to commercial systems. Its semi-modular design allows users to modify both hardware and software for various tasks, ranging from basic stimulus-response assays to complex visual discrimination paradigms.
NC4TOUCH: AN OPEN-SOURCE PLATFORM FOR RODENT BEHAVIOURAL TRAINING by Gelareh Modara B.Sc., Simon Fraser University, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Biomedical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2025 © Gelareh Modara, 2025 The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: NC4Touch: An Open-Source Platform for Rodent behavioral Training submitted by Gelareh Modara in partial fulfilment of the requirements for the degree of Master of Applied Science in Biomedical Engineering Examining Committee: Dr. Manu Madhav, Assistant Professor, Biomedical Engineering, UBC Co-supervisor Dr. Jason Snyder, Associate Professor, Psychology, UBC Co-supervisor Dr. Kurt Haas, Professor, Cellular & Physiological Sciences, UBC Supervisory Committee Member Additional Supervisory Committee Members: Dr. Catherine Winstanley, Professor, Psychology, UBC Supervisory Committee Member ii Abstract Touchscreen-based cognitive testing has become a popular tool for assessing a wide spectrum of neuropsychological functions in both human and animal models. Specifically, rodent touchscreen paradigms were pioneered in the 1990s and have undergone significant refinement, with modern commercial systems—such as the Bussey-Saksida system—offering advanced features and greater standardization. Commercially available rodent touchscreen chambers have become very popular tools, benefiting from consistent hardware, software, and training protocols. These systems offer many advantages, including automated data collection, high throughput, strong translatability, and improved standardization across studies—contributing to more replicable and reliable outcomes. However, their high cost presents a significant financial barrier to widespread adoption. Recent open-source initiatives address these limitations by leveraging affordable components (e.g., Raspberry Pi, Arduino) and publicly shared designs and software. Projects such as the ArduiPod Box, the Raspberry Pi touchscreen operant chamber, and Visiomode illustrate different approaches to creating cost-efficient, customizable systems. Despite offering substantial financial and design flexibility, these setups often face their own challenges, including limited user interfaces, data handling constraints, and incomplete hardware documentation. Consequently, there remains a need for an open-source solution that combines user-friendliness, robustness, scalability, and aesthetic appeal while maintaining affordability. Therefore, we developed a customizable, cost-efficient (approximately $500) cognitive touchscreen chamber called NC4Touch for mouse and rat behavioral experiments. It features iii capacitive touchscreens alongside a feeding port, allowing straightforward training for tasks such as visual pairwise discrimination. Moreover, the user-friendly graphical interface provides real-time control and customization of tasks, supports video recording, and includes robust data management features. We validated the chamber’s operational efficiency and refined training protocols for pairwise visual discrimination tasks in multiple cohorts of rodents. Constructed using readily available microcontrollers, hobbyist-grade hardware, and a custom-built housing, this open-source apparatus offers an accessible solution comparable in performance to commercial systems. Its semi-modular design allows users to modify both hardware and software for various tasks, ranging from basic stimulus-response assays to complex visual discrimination paradigms. iv Lay Summary In recent years, rodent touchscreen-based paradigms have gained popularity for their flexible task design, improved standardization, and automated data collection that minimizes experimenter intervention and contributes to more replicable and reliable research. However, the high costs associated with commercially available systems present a financial barrier for smaller labs or researchers with limited resources. To address this issue, we developed “NC4Touch”, a custom-built, scalable, modular testing setup. It features three independent touchscreens and a feeding port for easy training in tasks such as pairwise discrimination or delayed match-to-sample paradigms. The system includes a user-friendly graphical interface that offers real-time control, task customization, video recording, and data management. This work highlights the importance of open-source innovations in expanding the scope of scientific research, allowing researchers to overcome traditional financial barriers and creating a more collaborative community. v Preface This thesis includes unpublished data. The initial experimental ideas were developed collaboratively by G. Modara, M.S. Madhav, and J.S. Snyder. G. Modara, M.S. Madhav, and J.S. Snyder designed the experiment. G. Modara and M.S. Madhav completed the software and hardware components of the project A. Lester completed the 3D design of the final version (v1.0), while I. Schwein completed the 3D design of earlier versions (v0.1 and v0.2) with inputs from G. Modara. F. Bolanos generated the honeycomb visual stimuli. G. Modara and I. Schwein conducted cohort 1 animal experiments. G. Modara conducted cohort 2 animal experiments. G. Modara and O. Soliman conducted cohort 3 animal experiments. G. Modara, L. Sid and J. Zhang conducted cohort 4 animal experiments. G. Modara, I. Schwein and J. Zhang conducted cohort 5 animal experiments. G. Modara and L. Sid conducted cohort 6 animal experiments. G. Modara performed the data analysis and wrote this manuscript with input from M.S. Madhav and J.S. Snyder. This manuscript is currently unpublished, and no part of this research has been published elsewhere. All animal testing was conducted in accordance with ethical guidelines set by the Canadian Council for Animal Care and was approved by the University of British Columbia Animal Care Committee under certificate numbers A20-0290, A21-0005 and A21-0061. Generative AI was used to correct grammar and spelling in preparation for this thesis. vi We acknowledge that the land on which we have conducted this research is the traditional, ancestral, and unceded territory of the xwməθkwəy̓ əm (Musqueam) People. vii Table of Contents Abstract..........................................................................................................................................iii Lay Summary............................................................................................................................................... v Preface............................................................................................................................................vi List of Tables.................................................................................................................................xii List of Figures..............................................................................................................................xiii List of Abbreviations....................................................................................................................xv Acknowledgements......................................................................................................................xvi 1. Introduction............................................................................................................................1 1.1 Overview........................................................................................................................1 1.2 Rodent Touchscreen Paradigms.....................................................................................1 1.2.1 Applications and Key Paradigms..........................................................................3 1.2.2 Advantages of Commercialized Touchscreen Platforms...................................... 5 1.2.3 Limitations of Commercialized Touchscreen Platforms.......................................6 1.2.4 Open-Source Touchscreens..................................................................................8 1.2.4.1 ArduiPod Box.............................................................................................. 9 1.2.4.2 Raspberry Pi touchscreen operant chamber...............................................10 1.2.4.3 Visiomode.................................................................................................. 12 1.2.5 Rationale for a New Platform............................................................................. 13 1.3 Custom Exposure Paradigm.........................................................................................14 1.4 Objectives.................................................................................................................... 14 viii 2. Methods................................................................................................................................15 2.1 Hardware Components.................................................................................................15 2.1.1 Communication interfaces.................................................................................. 15 2.1.2 Displays and Touch Detection ........................................................................... 16 2.1.3 Reward Delivery ............................................................................................... 17 2.1.4 Feedback Indicators............................................................................................ 18 2.1.5 Video Monitoring Components...........................................................................19 2.2 Exposure Paradigm...................................................................................................... 20 2.2.1 Physical Design...................................................................................................20 2.2.2 Hardware Components........................................................................................21 2.2.3 Software and Firmware.......................................................................................23 2.3 Customized Touch Screen Chamber............................................................................26 2.3.1 Version 0.1: Simple Two Screen Displays..........................................................26 2.3.1.1 Physical Design..........................................................................................26 2.3.1.2 Hardware Components...............................................................................27 2.3.1.3 Software and Firmware..............................................................................29 2.3.2 Version 0.2: A Three Screen Setup for Match-to-Sample Task..........................31 2.3.2.1 Physical Design..........................................................................................31 2.3.2.2 Hardware Components...............................................................................32 2.3.2.3 Software and Firmware..............................................................................33 ix 2.4.3 Version 1.0: Capacitive Touchscreens and Raspberry Pi ...................................35 2.4.3.1 Physical Design..........................................................................................35 2.4.3.2 Hardware Components...............................................................................38 2.4.3.3 Software and Firmware..............................................................................39 2.4 Animal behavioral Training Protocol.......................................................................... 41 2.5 Animals .....................................................................................................................52 2.6 Feeding Restriction...................................................................................................... 52 2.7 Citric Acid Water Restriction.......................................................................................53 3. Results..................................................................................................................................55 3.1 Cohort 1: Testing Version 0.1...................................................................................... 55 3.2 Cohort 2: Testing Version 0.2...................................................................................... 56 3.3 Cohort 3: Testing Version 0.2 and Exposure Apparatus.............................................. 58 3.4 Cohort 4: Testing Version 0.2...................................................................................... 61 3.5 Cohort 5: Testing Version 1.0...................................................................................... 66 3.6 Cohort 6: Testing Version 1.0...................................................................................... 69 4. Discussion...........................................................................................................................72 5. Conclusion.......................................................................................................................... 76 Bibliography................................................................................................................................. 77 Appendix.......................................................................................................................................82 A...................................................................................................................................82 ix B...................................................................................................................................85 C...................................................................................................................................86 D...................................................................................................................................87 E................................................................................................................................................88 ix List of Tables Table 1: Comparison of standard touchscreen-based behavioral tasks used in non-human primates (NHP) and humans……………………………………………………………………… 5 Table 2: Summary of each training stage protocol………………………………………………51 xii List of Figures Figure 1: Commercial touchscreen-based cognitive testing systems for rodents…………………3 Figure 2: ArduiPod Box………………………………………………………………………….10 Figure 3: Raspberry Pi touchscreen operant chamber…………………………………………...12 Figure 4: Visiomode platform……………………………………………………………………13 Figure 5: Communication interfaces……………………………………………………………..17 Figure 6: Touch detection and display screen…………………………………………………...18 Figure 7: Reward delivery components………………………………………………………….19 Figure 8: Feedback indicators……………………………………………………………………20 Figure 9: Video Monitoring Components………………………………………………………..21 Figure 10: 3-D rendering of the Exposure cage………………………………………………….22 Figure 11: Exposure Cage………………………………………………………………………..22 Figure 12: Hardware system schematic of the exposure set up………………………………….23 Figure 13: Example of real-time data logging using ThingSpeak……………………………….25 Figure 14: 3-D Renderings of the Version 0.1 touchscreen chamber…………………………...27 Figure 15: Version 0.1 of the touchscreen chamber…………………………………………..…28 Figure 16. Graphical User Interface of the Version 0.1 touchscreen chamber…………………..30 Figure 17. 3-D Rendering of the Version 0.2 touchscreen chamber……………………….…….31 Figure 18. Version 0.2 of the touchscreen chamber set up………………………………………33 Figure 19. Graphical User Interface of the Version 0.2 touchscreen chamber…………………..34 Figure 20. 3-D Rendering of the Version 1.0 touchscreen chamber……………………………..37 Figure 21. Graphical User Interface of the Version 1.0 touchscreen chamber…………………..41 xiii Figure 22. Habituation 2: Reward Port Familiarization sequence of events schematic…………43 Figure 23. Initial Touch sequence of events schematic………………………………………….44 Figure 24. Mandatory Image Touch sequence of events schematic……………………………..45 Figure 25. Must Initiate sequence of events schematic………………………………………….46 Figure 26. Incorrect Choice Punishment sequence of events schematic………………………...47 Figure 27. Simple Pairwise Discrimination sequence of events schematic……………………...49 Figure 28. Novel Pairwise Discrimination sequence of events schematic………………………50 Figure 29. Weights of the animals (cohort 5) plotted over the experiment……………………...53 Figure 30. Normalized Mouse Weight on CA Water……………………………………………54 Figure 31. Diagram of the match to sample task………………………………………………...55 Figure 32. Cohort 3 training timeline……………………………………………………………58 Figure 33. Cohort 4 training timeline……………………………………………………………62 Figure 34. Daily mean percent correct across all cohort 4 mice…………………………………63 Figure 35. Side bias over training sessions for each mouse in cohort 4…………………………64 Figure 36. Cohort 5 training timeline……………………………………………………………66 Figure 37.Cohort 5 learning curves……………………………………………………………...67 Figure 38. Side bias over training sessions for each animal in cohort 5…………………………67 Figure 39. Cohort 6 training timeline……………………………………………………………68 Figure 40. Cohort 6 learning curves……………………………………………………………..69 Figure 41. Side bias over training sessions for each animal in cohort 6…………………………70 Figure A.Summary of one-sample t-tests for cohort 4 side bias during each training stage per mouse with Benjamini–Hochberg correction for multiple comparisons……………………………………….84 Figure B. Percentage of left and right screen touches across training days for each mouse……………85 xiv Figure C. Percentage of left and right screen touches across training days for each rat………………..86 Figure D. Summary of one-sample t-tests for cohort 5 side bias during each training stage per rat, with Benjamini–Hochberg correction for multiple comparisons……………………………………………87 Figure E. Summary of one-sample t-tests for side bias during each training stage per mouse, with Benjamini–Hochberg correction for multiple comparisons. …………………………………………..88 xv List of Abbreviations 5-CSRT – 5-Choice Serial Reaction Time CSV – Comma Separated Value CANTAB – Cambridge Automated Neuropsychological Test Associated Battery CT – Correction Trial DIY – Do-It-Yourself GPIO – General-Purpose Input/Output IDLE – Integrated Development and Learning Environment IDE – Integrated Development Environment IR – Infrared I²C – Inter-Integrated Circuit ITI – Intertrial-interval LD – Location Discrimination NHP – Non-human primates PAL – Paired Associate Learning TUNL – Trial-Unique Non-matching to Location xv Acknowledgements Thank you Dr. Manu Madhav and Dr. Jason Snyder for mentoring me and helping me become a better problem solver. Our data meetings, with all of our back-and-forth discussions, and bouncing ideas around, have been some of my favorite memories. Thank you to my committee members, Dr. Kurt Haas and Dr. Catherine Winstanley, for your invaluable advice and ongoing support. I'm thankful for all of my amazing lab mates in the Madhav and Snyder labs, who have been fantastic teammates especially Guilia and Afsoon for lending their ears to me whenever I needed. Special shout out to Isaac Schwein for helping me kickstart this project, and to Dr. Adam Lester for his patience with my constant questions. And Dr. Federico Bolanos for gracefully guiding me and giving me advice. And a heartfelt thanks to our wonderful research assistants, Leticia Sid, Jocelyn Zhang, and Omar Soliman, for their help and dedication in helping me train our animals. To my partner, Daniel, and my brother, Aria— thank you both for always having my back and supporting me through everything. Lastly, immense appreciation to my parents, family and friends —you've made my life richer, funner, and undeniably better in countless ways. Thank you. xvi To Zarineh and Farhad, Farzaneh and Kiumars xvii 1. Introduction 1.1 Overview Rodent touchscreen paradigms have emerged as powerful tools for investigating cognitive function and its underlying neural mechanisms over the past few decades. By adapting touchscreen-based methods initially developed for human neuropsychological testing, these paradigms have allowed researchers to bridge the gap between clinical studies and preclinical rodent models. This introduction provides (i) a historical background on rodent touchscreen paradigms, (ii) an overview of key tasks and applications, (iii) a discussion of the advantages and limitations of commercialized systems, (iv) a review of open-source alternatives (v), and a novel custom built visual exposure paradigm. Finally, it describes the motivation for developing a cost-efficient, and flexible open-source system to further expand accessibility in cognitive and behavioral research. 1.2 Rodent Touchscreen Paradigms In the late 1980s, Trevor Robbins, Barbara Sahakian and their colleagues developed one of the earliest touchscreen-based cognitive testing systems. This led to the creation of the Cambridge Automated Neuropsychological Test Associated Battery (CANTAB), which utilizes an infrared-based touchscreen system to present neuropsychological tests for humans (B. Sahakian et al., 1988); (B. J. Sahakian & Owen, 1992). CANTAB is used to assess different cognitive functions for a variety of domains such as attention, memory, executive function, decision making, etc. across various neurological and psychiatric conditions. The system consists of two-dimensional visual images displayed on an infrared-based computer screen and subjects are to respond to the visual images by touching the screen and are provided feedback for correct and incorrect responses. The success of CANTAB in human cognitive assessment inspired 1 researchers to develop similar testing methods for animal models, particularly rodents, thereby enhancing translational potential between basic/preclinical research and clinical populations. Early work by Bussey and colleagues in the 1990s pioneered the first touchscreen-based learning tasks for rats (Bussey et al., 1997) which was later adapted for use in mice (Bussey et al., 2001); (Morton et al., 2006). Follow-up studies described standardized procedures for tasks testing learning, memory, working memory, and pattern separation (Mar et al., 2013) (Horner et al., 2013). These contributions improved standardized procedures and training protocols for tasks, making it easier for new labs to adopt the technology. Initially developed in academic settings, the technology was later commercialized. Around 2009, a partnership with Campden/Lafayette Instruments helped produce the Bussey-Saksida Touch Screen Chamber, translating the academic prototypes into a marketable commercialized product (Fig. 1). Since then the usage has expanded worldwide - by the 2020s, over 300 research groups in more than 25 countries are using the Bussey Saksida Chambers (Dumont et al., 2021). In addition, researchers at Western University, supported by BrainsCAN, also introduced “MouseBytes”—an open-access platform for sharing and analyzing rodent touchscreen data (Dumont et al., 2021); (Beraldo et al., 2019). This platform promotes data transparency and allows researchers to share and analyze the touchscreen community’s data. Together, these efforts highlighted touchscreen-based methods as a valuable standard for cognitive assessment in rodents. 2 Figure 1. Commercial touchscreen-based cognitive testing systems for rodents. a) Bussey-Saksida Commercial Mouse Touchscreen Chamber b) Top-down view of the chamber showing a rodent engaging with the touchscreen and approaching the stimulus display area. c) Side view sketch of the touchscreen apparatus with a rodent performing a task. All images are reproduced from Lafayette Instrument. 1.2.1 Applications and Key Paradigms One major strength of rodent touchscreen testing is its flexibility: a range of cognitive tasks can be run on the same apparatus, each probing different domains. For example, working memory and pattern separation can be examined using Trial-Unique Non-matching to Location and Location Discrimination tasks (Oomen et al., 2013). Executive function can be tested using the Reversal, Extinction, and 5-Choice Serial Reaction Time tasks (Mar et al., 2013), while visual discrimination abilities can be assessed via tasks such as Object-location Paired-Associates Learning, Visuomotor Conditional Learning, and Autoshaping (Horner et al., 2013) (see Table 1 for comparison of some of the standard touchscreen-based behavioral tasks). All these tasks could be carried out in a single standardized apparatus by only changing the software, resulting in high throughput and consistent research. By simply modifying the software and on-screen visual stimuli, researchers can easily use different tasks, and increase research throughput. 3 Furthermore, due to their flexibility, these touch screens products have been widely used in disease modeling research- for example to detect cognitive deficits in rodent models of Alzheimer’s disease, Schizophrenia, Huntington’s disease, and others (Romberg et al., 2013); (Nithianantharajah et al., 2013); (Morton et al., 2006). Impairments in touchscreen tasks such as 5-choice serial reaction time test (used for testing attention and response control) have been shown in transgenic Alzheimer’s mouse models, comparable to the memory deficits seen in human patients (Romberg et al., 2011). In addition, touchscreens could also be used to investigate neural circuit functions by combining tasks with targeted lesions, optogenetics, and electrophysiology to link specific brain regions to task performance. They have also been used in pharmacological studies to evaluate cognitive effects of drugs (Talpos et al., 2009); (Bartko et al., 2011); (Chen et al., 2009). Overall, the touchscreen paradigm has become a useful tool to assess learning, memory, attention and executive functions in rodents, with broad applications from basic neuroscience to preclinical drug testing. NHP/Human Standard Tasks Example Neural Systems Involved Clinical Area Showing Impairment Human/NHP Pretraining to touch an image and initiate a trial (SCST) Prefrontal Cortex, Perirhinal Cortex, Anterior Cingulate, Striatum, Hippocampus, Cholinergic Huntington’s, Schizophrenia, Alzheimer’s, PD Human/NHP Pairwise / Visual Discrimination and Reversal (SDR), (SRT), (PRT) Prefrontal Cortex, Striatum, Basal Forebrain, DA, 5HT, Ach, GABA Huntington’s, Schizophrenia, OCD, PD Human/NHP Set-shifting / Extradimensional / Intradimensional (ED/ID) tasks Prefrontal Cortex, Posterior Parietal Cortex, Striatum, DA, 5HT, Ach Schizophrenia, OCD Human/NHP Serial Reaction Time (SRT) task Dorsal Striatum, Posterior Cingulate Cortex Parkinson’s, Huntington’s Human/NHP Continuous Performance Test (CPT) Prefrontal Cortex, Attention, Vigilance, NMDA Receptors, DA, NE Alzheimer’s, Schizophrenia, ADHD 4 Human/NHP Working Memory / Delayed Match to Sample (DMTS) tasks Hippocampus, Prefrontal Cortex, DA, Ach Schizophrenia, ADHD, OCD Human/NHP 5 Choice Serial Reaction Time Task (5-CSRTT) Dopamine, Serotonin, Cholinergic, Parietal, Muscarinic Attention, ADHD, OCD Human/NHP Progressive Ratio Task Dopamine, Ventral Striatum Motivation, Depression, MDD Human/NHP Conditioned Avoidance (active / passive) Cholinergic system, Anterior Cingulate Cortex Bipolar Disorder, General Anxiety Disorder, Depression Human/NHP Delay Discounting (Impulsivity) tasks Orbitofrontal Cortex, Amygdala, DA, 5HT Schizophrenia, ADHD, OCD Human/NHP Probabilistic Reversal Learning (PRL) Serotonin Impulsivity, ADHD Table 1. Comparison of standard touchscreen-based behavioral tasks used in non-human primates (NHP) and humans. Highlighting key neural systems involved and clinical conditions associated with impairments. Table adapted from Lafayette Instruments. 1.2.2 Advantages of Commercialized Touchscreen Platforms Commercial rodent touchscreen chambers offer several benefits that streamline cognitive testing, enhance reproducibility, and increase translational relevance: 1) Automation and High Throughput Computerized controls reduce experimenter intervention and enable multiple chambers to run concurrently, increasing throughput while maintaining consistency (Bussey et al., 2008). Reinforcement delivery and data recording are fully automated, helping minimize confounding factors such as human scent or sight, or handling stress (Wahlsten et al., 2003). Moreover, task parameters (stimulus timing, size, positions, etc.) are controlled and can be kept the same across experiments and laboratories , allowing for data to be more comparable across different studies. 2) Multiple Cognitive Domains Touchscreen testing enables running a variety of cognitive tasks within the same apparatus and probing a wide range of cognitive functions. They allow for investigating 5 different aspects of cognition under the same condition, using the same type of stimuli, responses and rewards. Researchers can easily switch between testing memory, attention, cognitive flexibility, etc., by simply changing the software protocol and on-screen stimuli, rather than building new mazes or apparatus for each task. 3) Translational Value A major advantage of touchscreens is their translational relevance. The rodent touchscreens tasks were modeled after human assessments and designed to match human touchscreen neuropsychological tests allowing for direct comparisons (Hvoslef-Eide et al., 2016). For example, the Paired Associate Learning (PAL) task for rodents was inspired by the CANTAB PAL test used in humans. Both tasks require subjects to learn associations between objects and locations, and they are sensitive to cognitive impairments associated with conditions like Alzheimer's disease and schizophrenia. The rodent version has been adapted to assess similar cognitive functions in mice and rats, with the goal of translating findings between species (Kim et al., 2015); (Bartko et al., 2011). 4) Low Stress Touchscreens typically use positive reinforcements and avoid very aversive punishments. These platforms are considered low stress compared to paradigms like water mazes or shock-based learning. The low stress factor is important as stress has been shown to confound cognitive performance (Moreira et al., 2016). 1.2.3 Limitations of Commercialized Touchscreen Platforms Despite their advantages, commercial systems can present several challenges: 1) High Cost 6 Commercially available touchscreen apparatuses are costly (>$10,000 USD), making them difficult to acquire for labs with limited budgets or those requiring multiple apparatuses. In addition, maintenance contracts can add further expense. 2) Training Demand Rodents must learn to associate a visual stimulus with a touchscreen response and reward, which can require days to months of shaping and task-specific training depending on task complexity. New users also face a learning curve in operating the hardware and software. 3) Technical Constraints Just like any other hardware, touchscreens need to be calibrated and maintained. For example, the reward tube has to be cleaned thoroughly and tested periodically to make sure rewards are being reliably pumped. In addition, rodents have coarser visual acuity than humans, so stimuli must be designed with appropriate size and contrast (Schnell et al., 2023); (Bussey et al., 2008) . If the stimuli are too small or if the animal observes the screen from a distance, it may have difficulty distinguishing them, which could potentially complicate the results. Additionally, because rodents can move freely in the chamber, the exact viewing distance to the screen can vary, making it harder to control visual angles precisely (Crijns & Op de Beeck, 2019). These factors mean that task design (stimulus shape, size, brightness) often needs optimization for the species and strain (e.g. using larger, high-contrast stimuli for mice or albino rats). 4) Task Specific Limitations Certain cognitive functions (e.g. large-scale spatial navigation) cannot be easily modeled on a flat screen. In addition, researchers must be mindful that rodents might solve 7 touchscreen tasks using different strategies than humans. For example, rodents might develop a side bias (always touching one screen location) or use peripheral cues from the apparatus, which requires control measures to prevent (such as counterbalancing stimulus positions and including correction trials) (Bennett et al., 2023). Therefore, while touchscreens cover many cognitive domains, they are not a complete replacement for all behavioral tests. 1.2.4 Open-Source Touchscreens Given the high cost of commercial touchscreen systems, there has been growing interest in affordable open-source, do-it-yourself (DIY) touchscreen alternatives that can offer similar functionality as commercialized touchscreens, without the significant expense. Over the past decade, several groups have developed their own rodent touchscreen chambers using affordable components, sharing designs and software with the community. (Pineño, 2014); (O’Leary et al., 2018); (Eleftheriou et al., 2023). These DIY platforms offer flexibility and customization. Typically the system could be modified if desired and researchers can program new task variants or tweak parameters as needed. Hardware modifications are also possible; for instance, one can swap in a larger touchscreen, integrate additional sensors (cameras, IR beams, lick detectors). This allows scientists to tailor the apparatus to their experimental needs. On the other hand, one must consider that building a DIY chamber requires at least a moderate level of technical proficiency in electronics and programming, as well as access to the necessary tools (e.g., laser cutters or 3D printers for enclosure fabrication). This section reviews three recent open-source platforms—ArduiPod Box, the Raspberry Pi touchscreen operant chamber, and Visiomode—to illustrate their strengths, limitations, and overall suitability for rodent cognitive testing. 8 1.2.4.1 ArduiPod Box Developed by Pineno (2014), this under $300 USD (reported at the time of manuscript publication) system features an iPod touch (Apple, Inc., CA USA), running an iOS application called Shaping, an Arduino Uno microcontroller (Arduino, Italy) that drives a servomotor to dispense rewards, and a simple enclosure to house the rodent (Fig 1. a, b). With the iPod serving as both the display and the primary processing unit, experimenters configure visual stimuli and reinforcement schedules directly through the Shaping app (Fig. d). A second application , ArduiPodChart, connects wirelessly to the Shaping app and displays real time graphs, summary of performance and training parameters (Fig. c). Although the system is relatively inexpensive and straightforward to assemble, it has certain limitations. Because it is designed around a single iPod Touch, it primarily supports only one chamber and typically accommodates simpler, lower-complexity tasks. Over time, the hardware and software have also become outdated; the original codebase and documentation no longer appear to be available, making it difficult for new users to adopt and extend the platform. Despite these challenges, the ArduiPod Box introduced a promising concept by illustrating that basic touchscreen paradigms could be implemented inexpensively without sacrificing core functionality. 9 Figure 2. ArduiPod Box. a) Electronics Components b) Initial prototype c) Shaping app during the experimental condition d) Sharing app settings configuration. Images reproduced from (Pineño, 2014). 1.2.4.2 Raspberry Pi touchscreen operant chamber Building on the idea of low-cost rodent testing platforms, O’Leary and colleagues (2018) proposed a setup featuring a Raspberry Pi single-board computer paired with a 7-inch touchscreen display, an Arduino Uno to manage peripherals, and a Perspex (acrylic) enclosure. The estimated cost was approximately £160 (about $200 USD at the time of publication), making it more affordable than commercial chambers. In this design, the Raspberry Pi runs custom Python scripts using Pygame—a set of Python modules designed for writing video games—to present visual stimuli and record touchscreen responses, while RPi.GPIO—a Python module that allows control of the Raspberry Pi’s GPIO pins—manages devices such as reward 10 dispensers, an LED for signaling reward delivery, a buzzer to indicate food pellet delivery, and an IR beam to detect the collection of the food pellet. Although this approach offers greater flexibility than the ArduiPod Box—since experimenters can create and modify Python scripts for tasks that integrate custom stimuli or additional modules like cameras—it lacks a dedicated graphical user interface (GUI). Instead, each training protocol must be run using the Integrated Development and Learning Environment (IDLE), which can be difficult for users without programming experience. The set up also supports one chamber. Furthermore, the authors noted occasional pellet misalignment in the reward receptacle, raising concerns about inconsistent reward delivery. They suggested adding improvements such as measuring response and reward collection latencies, recording screen touches during the intertrial interval (ITI), and mapping touch locations. While the design’s overall adaptability, low cost, and openness to hardware customization are appealing, reliance on IDLE scripting, the absence of a built-in GUI, and the limited default data visualization have all constrained the system’s broader adoption. 11 Figure 3. Raspberry Pi touchscreen operant chamber. a) Side view of the chamber b) Top view of the chamber. Images from (O’Leary et al., 2018). 1.2.4.3 Visiomode A more recent contribution from Eleftheriou and colleagues (2023) is the Visiomode platform, designed to run touchscreen-based assays through a user-friendly web-based interface (Fig 4.). It is developed in Python and runs on a Raspberry Pi computer. It uses graphic libraries such as PyGame and PySDL to generate and display visual stimuli, while interfacing with external hardware—such as pumps, LEDs, and other peripherals—via plug-and-play. Users can monitor sessions in real time, adjust settings through the web-based interface, and export data in formats such as CSV or JSON. Although the main focus was on software, the team also developed a hardware interface that supports external components like water reward systems (controlled by a solenoid valve and Arduino Nano) and other USB-connected devices. Despite its user-friendly features, Visiomode has a few limitations. Because it relies on a web interface, a stable local network connection is needed—any internet issue can interrupt the experiment. In addition, while the software documentation is detailed, instructions for building the physical enclosure and reward delivery system are limited. As a result, users must design these parts themselves or rely on other sources. Finally, although Visiomode’s modular design supports future expansion, the current documentation focuses mainly on the software, leaving users to independently manage other hardware integrations. 12 Figure 4. Visiomode platform. a) Mouse behavioral chamber featuring an interactive touchscreen interface, managed through Visiomode b) Schematic representation of Visiomode’s wireless communication with a Raspberry Pi and its USB-connected components. Images from (O’Leary et al., 2018) 1.2.5 Rationale for a New Platform The described open-source platforms offer cost efficiency and flexibility, allowing researchers with limited resources to overcome the financial barriers of using commercialized touchscreens. However, each system comes with its own challenges and tradeoffs. The ArduiPod Box presents an innovative concept and is quick to assemble, but it supports only one display and is limited in task complexity. Additionally, its open-source code is no longer available, likely due to outdated hardware. The Raspberry Pi touchscreen operant chamber is more advanced and cost-effective than the ArduiPod, yet it has technical barriers for non-coders because it relies on an IDLE-dependent interface, lacks a user-friendly GUI, exhibits inconsistent pellet delivery, and has limited data handling capabilities. Both of these setups are relatively easy to assemble but fall short in aesthetics and are limited to single-chamber operation. Visiomode addresses usability with its web-based user interface, though its hardware modularity remains 13 underdeveloped compared to its software innovations. While it offers high scalability, any web disconnection can disrupt experiments, and although instructions are provided on building the enclosure, information on the reward mechanism and other hardware components is missing. Motivated by these limitations, we aimed to develop a design that is not only cost-efficient and flexible, but also scalable, user-friendly, and aesthetically robust. Our design addresses the shortcomings of earlier platforms by supporting multi-chamber configurations, improving physical design quality, and enabling modular integration with various peripherals. 1.3 Custom Exposure Paradigm In section 2.2, we describe the development of a custom exposure setup designed to passively expose mice to visual stimuli in their home cage. This setup was developed for a project that tests whether early-life passive exposure to visual stimuli alters learning in adulthood. 1.4 Objectives Recognizing the advantages and shortcomings of both commercial and DIY touchscreen platforms, the aim of this work is to develop a new open-source rodent touchscreen system that is: 1) Cost-Efficient: Significantly less expensive than commercial alternatives. 2) Scalable and User-Friendly: Supports multi-chamber configurations, minimizes the user’s coding burden, and offers a straightforward interface. 3) Physically Robust: Features an aesthetically refined, easily maintainable design. Additionally, we introduce a modular exposure paradigm that is cost-efficient, easy to assemble and facilitates real-time behavioral monitoring, enabling researchers to passively expose mice to visual stimuli in their home cage. 14 2. Methods We developed two distinct experimental systems: (1) an Exposure Paradigm that allows modular display units to be placed around a rodent’s home cage for passive presentation of visual stimuli, and (2) a customized Touchscreen Chamber designed for interactive behavioral tasks such as pairwise visual discrimination and match-to-sample. This chapter details the iterative development of both systems’ physical design, hardware, and software components, culminating in the final version (v1.0) validated in multiple cohorts of rodents. Since most core hardware elements—such as microcontrollers, display modules, and reward delivery systems—are shared across different versions, we begin with an overall hardware overview. We then describe each version’s specific configuration, focusing on key differences. 2.1 Hardware Components 2.1.1 Communication interfaces Three main computing platforms were used over the course of development: ● FireBeetle 2 M0 Development Board (M0) (DFRobot, Shanghai, China) The M0 microcontroller features the Microchip ATSAMD21G18 processor and a built-in 12 MB flash storage for storing images. It supports standard communication interfaces (I²C, UART, SPI) and operates on low power. ● FireBeetle 2 ESP32-E IoT Microcontroller (ESP32-E) (DFRobot, Shanghai, China) The ESP32-E supports Wi-Fi and allows real-time data transfer. Like the M0, it supports multiple communication interfaces. Its low cost and wireless connectivity makes it especially suitable for setups requiring quick, remote data logging. In our set up it uses I²C to communicate with the M0s. 15 Figure 5. Communication interfaces a) Firebeetle 2 M0 Development Board. Image reproduced from DFRobot. b) FireBeetle 2 ESP32-E IoT Microcontroller. Image reproduced from DFRobot. c) Raspberry Pi 4 Model B. Image reproduced from Adafruit. ● Raspberry Pi 4 Model B (Raspberry Pi, Cambridge, UK) The final iteration incorporated a Raspberry Pi 4, a single-board computer that runs a Linux operating system, which provided expanded I/O and more processing power. Because the Pi can function as a USB host, it can support multiple serial peripherals at once, making it well-suited for controlling multiple chambers in parallel. In our set up the Raspberry Pi 4 uses a USB-based serial connection when interacting with the M0 boards. 2.1.2 Displays and Touch Detection Over the course of development, we used two primary approaches to combine visual presentation with touch detection: ● Neonode zForce AIR Touch Sensor (zForce) ( Neonode Inc., Stockholm, Sweden) Early versions relied on Neonode zForce AIR Touch Sensor (90°) for infrared-based touch detection. Product discontinuation prompted a shift away from this method in later versions. ● Gravity 3.5" TFT Display capacitive LCD screens (DFRobot, Shanghai, China) 16 In the final version, we used the DFRobot Gravity 3.5" TFT Display capacitive LCD screens. It has a resolution of 480×320, driven by the ILI9488 controller over a four-line SPI (Serial Peripheral Interface). Multi-touch functionality is enabled by the onboard GT911 touch controller, which communicates via I²C (Inter-Integrated Circuit). Figure 6. Touch detection and display screen. a) DFRobot Gravity 3.5" TFT Display capacitive Touch Screen. Image reproduced from DFRobot. b) Neonode zForce AIR Touch Sensor (90°). Image reproduced from Digikey.. 2.1.3 Reward Delivery ● Ring Pump RP-CIII Series ( Takasago Fluidic Systems, Midori-ku Nagoya, Japan) The Ring Pump RP-CIII Series peristaltic pump features a 1.6 ml/min discharge rate at 3 volts of direct current, using silicone tubing for reward delivery. ● MD1.3 2A Dual Motor Controller (DFRobot, Shanghai, China) The MD1.3 2A Dual Motor Controller was used for regulating and adjusting the speed and direction of the pump. 17 Figure 7. Reward delivery components. a) Ring Pump (RP-CIII Series). Image reproduced from Takasago Fluidic Systems. b) MD1.3 Dual Motor Controller used for liquid reward delivery. Image reproduced from DFRobot. 2.1.4 Feedback Indicators ● Gravity Digital Buzzer (DFRobot, Shanghai, China) The gravity buzzer is powered at 3.3 volts of direct current and produces a consistent tone. To control loudness, we applied PWM (Pulse Width Modulation)—a technique that rapidly switches the buzzer’s power on and off at a high frequency—adjusting the ‘on’ proportion (duty cycle) to regulate volume. ● Infrared (IR) Beam Break Sensor (Adafruit, NewYork, US) This IR sensor is comprised of an emitter and receiver that create an invisible infrared beam up to 50 cm apart. When an object passes between them and interrupts the beam, the sensor output changes, making it easy to detect events such as entries or exits. Powered at 3.3 V from the master controller, the emitter continuously sends out IR light while the receiver monitors for any interruption in the beam. ● Light Emitting Diode (LED) Panel Mount (Dialight, London, United Kingdom) This is a multi-coloured LED panel mount indicator that emits red, green, and blue lights allowing for a wide range of color combinations. 18 Figure 8. Feedback indicators. a) Gravity Digital Buzzer Module. Image reproduced from DFRobot. b) IR Break Beam Sensor. Image reproduced from Adafruit. c) LED Panel Mount. Image reproduced from Mouser. 2.1.5 Video Monitoring Components ● 8 Megapixels USB Camera with Microphone (DFRobot, Shanghai, China) This USB camera used in the touchscreen set up features an 8-megapixel Sony IMX179 image sensor and an integrated microphone. It supports plug-and-play operation via USB 2.0, with up to 3264*2448 resolution. ● 2 Megapixels USB Night Camera with Microphone (DFRobot, Shanghai, China) This night camera features a 2 megapixel Sony IMX291 sensor and an integrated microphone. It supports plug-and-play operation via USC 2.0, with up to 1920 x 1080 resolution. In our exposure paradigm, it was used to monitor behavior in dark conditions. 19 Figure 9. Video Monitoring Components. a) 8 Megapixels USB Camera with Microphone. Image reproduced from DFRobot. b) 2 Megapixels USB Night Camera with Microphone. Image reproduced from DFRobot. In the sections that follow, we describe how these hardware elements were assembled in each version. 2.2 Exposure Paradigm 2.2.1 Physical Design The Exposure Paradigm was created to display visual stimuli in a rodent home cage without interfering with routine animal activity.To achieve this, we 3D-printed compact enclosures that each house an LCD screen and a microcontroller (the “display unit”), as well as a support rail from which to hang the display unit (Fig. 10). For easy installation and maintenance, each support rail and its attached display unit is secured to the exterior of the cage with Velcro strips.The modular nature of this design gives researchers the flexibility to add or remove displays depending on the specific needs of each experiment, thereby reducing costs and setup complexity. 20 Figure 10. 3-D rendering of the Exposure cage. a) Isometric view. The screen holders can be easily hanged and removed from the support rails attached to the cage via velcro strips b) Side view 2.2.2 Hardware Components Each display unit consists of a screen paired with an M0 microcontroller. The M0 receives commands via a communication protocol—I²C when communicating with an ESP32-E—to determine which images to display. I²C is a two-wire protocol with a single “master” issuing commands to multiple peripheral devices. These units are daisy-chained together so that a single master controller can coordinate image presentation across multiple displays (Fig. 11). Each M0 stores Bitmap (BMP) image files in its internal flash memory, and displays them on the LCD upon receiving a command specifying which image to load and how long to show it. By isolating the microcontroller and display functionality within each unit, the system remains modular, as each unit functions independently once given the correct instructions from ESP 32-E. Two configurations are available for the master controller, offering researchers flexibility in both budget and functionality. In the more cost-effective arrangement, an ESP32-E handles all scheduling duties and sends display commands to each M0 (Fig. 12a). Researchers who require more advanced functionality can use a Raspberry Pi 4 instead of the ESP32-E, which makes it possible to directly stream video to YouTube and store recorded footage on a dedicated hard drive (Fig. 12b). An optional night camera can be mounted on the cage exterior for continuous 21 (24/7) video recording of rodent activity. When using the Raspberry Pi 4, this footage is saved to the hard drive and can be streamed live to a private streaming (e.g. YouTube) link if needed. Researchers who do not require video can use the ESP32-E-based system alone and omit the camera module entirely. Figure 11. Exposure Cage a) Interior view of the home cage during stimulus presentation. LCD screens are mounted along the cage walls to display visual stimuli. b) Two home cages equipped with modular exposure units running in parallel. c) Exterior view of an exposure cage with labeled hardware components. 22 Figure 12. Hardware system schematic of the exposure set up. a) Configuration using an ESP32-E to communicate with multiple M0s and LCD screens. b) Configuration using a Raspberry Pi, suitable for recording behavioral videos 2.2.3 Software and Firmware The exposure system features two primary sets of software. One set runs on the M0 boards, and the other set runs either on ESP32-E or on a Raspberry Pi 4, depending on which device is chosen to serve as the master controller. Although there are slight variations between the ESP32-E and the Pi implementations—particularly in the programming languages used (Arduino 23 C/C++ for the ESP32-E vs. Python for the Pi)—the core concepts are similar in each case: a master device coordinates when images are displayed, sends commands over an I²C bus to the M0 boards, and logs each event. On the M0 side, the firmware is written in Arduino (C++). It continuously waits for incoming commands from the master controller, which specify an image ID to load or a command to blank the screen. After receiving a command from the master, the M0 constructs a filename (for example, “A015.BMP”) and attempts to load the corresponding file from the local flash memory. The ESP32-E, also programmed in Arduino (C++), performs a scheduling role that is almost identical to what the Raspberry Pi 4 does with Python scripts. In both scenarios, the master controller begins by scanning the I²C bus for all available M0 boards, each identified by its unique numerical I²C address. We use three general-purpose input/output (GPIO) pins on each M0 board to create its address on the I²C bus. Each pin can be either “high” (powered) or “low” (grounded), which the firmware interprets as a zero or one. Because there are three pins, we can form up to eight different addresses (0 through 7). If a project ever requires more than eight addresses, we can simply add extra pins to expand the number of possible combinations. The I²C protocol itself can accommodate many more devices, but our current design uses three pins for simplicity, giving enough options for most small-scale experiments. Once these addresses are known, the master follows a user-defined schedule to determine when to send image commands. During active display hours (for instance, 7:00 a.m. to 7:00 p.m.), it calculates which image ID should appear next, sends that ID to each M0, and waits for an acknowledgment that the image has been displayed. Outside those hours, the master transmits a command that instructs the M0 boards to show a black screen. Regardless of whether this logic is 24 implemented in Arduino on the ESP32-E or in Python on the Raspberry Pi 4, the basic flow remains the same: scan, schedule, send image ID, and wait for acknowledgment. Both the ESP32-E and the Raspberry Pi also log the data. As soon as the M0 boards acknowledge a new image command, the master controller records the image ID, the M0’s address, and a timestamp—and forwards these details to a service called ThingSpeak if Wi-Fi is available (Fig. 13). Because ThingSpeak is cloud-based, researchers can access real-time logs of which images were displayed and when. When the system is running on the Raspberry Pi, there is an additional Python script that can manage video recording and streaming if a camera module is present. This script uses OpenCV, an open-source library for image and video processing, to capture frames from the camera in real time. Then those frames are passed to FFmpeg - an open source software used for recording, converting and streaming audio and video - which either writes them to local storage or streams them live to a private YouTube link. Figure 13. Example of real-time data logging using ThingSpeak. a) Example of a custom ThingSpeak channel recording M0 board address and image ID. b) A snapshot of the recorded log entries. 25 2.3 Customized Touch Screen Chamber 2.3.1 Version 0.1: Simple Two Screen Displays 2.3.1.1 Physical Design Version 0.1 was designed as a trapezoidal chamber, inspired by the Bussey-Saksida design, with a wider front (touchscreen) and a narrower rear (reward port), encouraging the rodent to face the screens. The chamber walls were composed of 3 mm opaque acrylic, cut via laser to create a trapezoid that could be easily assembled and cleaned. The reward port was positioned on the back wall; this placement discouraged rodents from lingering in front of the screens between trials and reduced unintended touches and impulsive choices once a new trial began. Assembly involved combining laser-cut acrylic panels with 3D-printed brackets that held the walls together. The front panel held two LCD displays and mounting points for microcontrollers, while the back panel supported the reward repository. Various 3D-printed parts were created in SolidWorks and printed with either an SLA printer (for fine detail) or an FDM printer (for less intricate components). Key printed parts included: ● Screen Holder: Secured both LCD displays and provided mounting points for the microcontrollers. ● Reward Repository: Enclosed the peristaltic pump externally, with a stainless steel spout extending inside for fluid delivery. ● IR Sensor Holder: Positioned the zForce IR touch sensor below the LCD screens to capture touch inputs. ● Brackets, Mounts, and Side Supports: Used to secure the screens, microcontrollers, and walls together. 26 The 3D-printed screen holder and hardware (e.g., buzzer, LEDs, etc.) were then screwed onto the front panel. During assembly, the 3D-printed brackets were used to attach the laser-cut walls together, forming the basic enclosure. The reward repository slid onto the reward wall, placing the peristaltic pump and controller externally with the spout directed inside the chamber. The zForce IR sensor holder was mounted underneath the front panel’s screen cutouts. Figure 14. 3-D Renderings of the Version 0.1 touchscreen chamber. 2.3.1.2 Hardware Components Four main components drove the hardware configuration: ● Reward Delivery: A peristaltic pump (driven by a motor driver and powered by a 4.5 V source) dispensed liquid reward. The spout was placed on the rear wall to minimize disruption near the screens. ● Displays and Touch Sensor: Two LCD screens (positioned side by side) presented visual stimuli, while a zForce AIR Touch Sensor (90°) detected the rodents’ touches. The IR sensor communicated with an ESP32-E controller via I²C protocol. 27 ● Microcontrollers: Three microcontrollers were used. Two M0 boards each drove an individual LCD, while an ESP32-E served as the master. This master controller orchestrated trials, processed touch data, evaluated correct/incorrect choices, and triggered outcomes (reward, LED, buzzer). ● Feedback Indicators: A small buzzer and a green LED were mounted above the screens. Correct responses illuminated the LED and sounded the buzzer; incorrect choices received no additional feedback. This initial setup established the fundamental design principles later refined in subsequent versions. Figure 15. Version 0.1 of the touchscreen chamber. a) Hardware system schematic b) Isometric view of the chamber with labeled hardware components.c) Top view of the chamber 28 2.3.1.3 Software and Firmware All firmware was written in Arduino C/C++ and distributed across the three microcontrollers: ● ESP32-E (Master): Coordinated trial events, evaluated correctness, dispensed rewards, and controlled the feedback signals (buzzer, LED). ● M0 Boards (2×): Each rendered images on its respective LCD using the DFRobot_GDL library, which was modified to increase image presentation speed. Images were stored in onboard flash memory. The M0 boards communicated with the ESP32-E via I²C (using the Arduino Wire library). The zForce IR touch sensor also communicated with the ESP32-E via I²C. During each trial, the master controller tracked touch coordinates reported by the sensor, mapped them to screen regions, and determined whether the rodent’s response was correct or incorrect. To accommodate different training stages, three separate firmware routines (one for each training stage) were developed for the ESP32-E. These sketches were manually uploaded to the master microcontroller using the Arduino IDE. By contrast, the M0 boards used a single firmware across all stages and did not require re-uploading. To maintain accurate timestamps for all events (touches, buzzer activations, reward dispenses), the ESP32-E periodically synchronized its internal clock with a Network Time Protocol server, which provides accurate time information over the internet. When an M0 board received a command from the ESP32-E—such as an instruction to load or clear a particular image—it updated the LCD and sent a short acknowledgment in response. 29 Although the ESP32-E handled most of the experimental logic, each M0 performed basic local checks (e.g., confirming image file integrity) and reported errors to the master as needed. To manage the experimental sessions, a GUI was developed using PyQt, a Python toolkit based on the Qt framework (a cross-platform library for building advanced graphical applications). Researchers entered rodent IDs, started or stopped training, and manually activated hardware functions (e.g., priming the reward pump, sounding the buzzer). Meanwhile, the ESP32-E sent event data (touch timestamps, reward dispenses, buzzer activations) back to the GUI, which logged these events in real time. With single button click, the GUI generated a comma-separated values (CSV) file containing all relevant performance data (stimulus names, touch responses, feedback cue timestamps, outcomes) for data export and later review allowing for further analysis of performance metrics such as accuracy. Figure 16. Graphical User Interface of the Version 0.1 touchscreen chamber. 30 2.3.2 Version 0.2: A Three Screen Setup for Match-to-Sample Task 2.3.2.1 Physical Design Version 0.2 builds on the principles of Version 0.1 but introduces a third LCD screen—positioned above the original two—to enable match-to-sample and other more complex behavioral tasks. As before, the basic enclosure uses laser-cut walls joined by 3D-printed brackets, but the front panel is now clear acrylic (3 mm). In this version, the entire front panel, including the screen holder, was laser-cut rather than using a separate 3D-printed holder. (By contrast, Version 0.1 used a laser-cut front panel with a 3D-printed screen holder that was screwed on). We initially tried 3D printing a larger triple-screen holder, however, our available printers could not accommodate the necessary dimensions reliably. To facilitate overhead video recording, a mini T-slotted framing stand was assembled and fitted with a laser-cut acrylic camera mount. Researchers could adjust the stand’s height and angle for optimized monitoring. A new hole in the sidewall allowed for external placement of a beam-break sensor, which monitored the rodent’s approach to the reward port. The assembly process for Version 0.2 followed the same general steps as Version 0.1, with laser-cut walls and 3D-printed brackets forming the basic enclosure. Figure 17. 3-D Rendering of the Version 0.2 touchscreen chamber. 31 2.3.2.2 Hardware Components Version 0.2 introduced: ● Third LCD Screen: Positioned above the two horizontal screens to support match-to-sample or delayed match-to-sample protocols. ● Dual LED Feedback: A “punishment” LED (blue) mounted at the front and a “reward” LED (green) at the rear. Correct trials illuminated the rear LED; incorrect trials triggered the front LED to indicate a penalty. ● Beam-Break Sensor: Installed near the reward port to detect final approach and confirm reward collection, and, as will be discussed in the training protocol, the initiation of trials. ● Camera Setup: A portable overhead camera was added for real-time video monitoring and subsequent behavioral recording. The core microcontroller architecture remained largely the same (two M0 boards plus an ESP32-E over I²C). However, additional I/O lines were added for the beam-break sensor and the second LED. Each LCD screen was still powered by an M0 Board, with a ESP32-E acting as the master controller. This continuity minimized rewriting of communication protocols and preserved the modular, I²C-based architecture established in v0.1. With these hardware upgrades, version 0.2 was better equipped to run the extended training protocols required for more complex discrimination tasks, including match-to-sample trials that require the rodent to hold information in working memory before making a choice. 32 Figure 18. Version 0.2 of the touchscreen chamber set up. 2.3.2.3 Software and Firmware Similar to Version 0.1, firmware was distributed across three microcontrollers, with the ESP32-E as the master controller and each M0 board dedicated to one LCD screen. However, the ESP32-E now included six distinct firmware routines to support a new, more advanced training protocol (details of these training stages are provided in Section 2.4). TThe code was also made more modular and efficient, improving data handling routines and accelerating image presentation on the LCDs. Data logging was enhanced to automatically save session data in CSV format—while still allowing for manual exports—thereby supporting both continuous and on-demand record-keeping. Throughout these improvements, all microcontrollers continued to communicate over the I²C bus, preserving the modular architecture established in Version 0.1. The PyQt-based GUI gained several new features to support the extended behavioral tasks. Key features included: ● Video Capture Integration: An option to activate the overhead camera, enabling simultaneous real-time recording and observation. 33 ● Automatic Session Logging: Each trial’s outcome (correct, incorrect, or no touch)—along with timestamps and chosen stimuli—was automatically saved to a CSV file on the desktop. ● Real-Time Performance Metrics: A simple chart displayed metrics such as percent correct, providing an at-a-glance overview of the rodent’s progress. ● Built-In Serial Monitor: A dedicated window displayed microcontroller communications in real time, including ESP32-E commands and their timestamps. ● Workflow Enhancements: The GUI tracked the number of trials conducted and total elapsed time, and it required a valid rodent ID before starting. This prevented accidental runs with incomplete data and further streamlined the overall workflow. Figure 19. Graphical User Interface of the Version 0.2 touchscreen chamber. 34 2.4.3 Version 1.0: Capacitive Touchscreens and Raspberry Pi 2.4.3.1 Physical Design Version 1.0 was scaled up for rat experiments and introduced capacitive touchscreens arranged vertically. Three capacitive screens were mounted side by side in a robust acrylic front panel. In this iteration, both the reward mechanism and electronics were fully enclosed to discourage chewing or tampering. A stable, grounded floor plate further reduced “ghost” touch activations by providing a consistent reference ground. To ensure reliable and repeatable video capture, the camera was mounted in a fixed position above the chamber. This setup was standardized so that each recording captured the same top-down view. The front panel electronics and rear reward-port electronics were modularized and separated to simplify maintenance and improve overall organization. Instead of placing beam-break sensors externally on the side walls (as in earlier versions), a 3D-printed reward tray was integrated to enclose the beam-break hardware. This design requires the rodent to nose-poke inside the tray to break the beam, preventing the issue experienced in previous versions where smaller rodents or rodents approaching at certain angles did not always interrupt the beam as intended. Construction of Version 1.0 also employed a Miniature T-Slotted Framing Single Rail (10 mm × 10 mm) for structural support. Acetal (1/8 inch) was used for all the walls, and 3/16 inch clear acrylic served as the top and floor. Wiring was more efficiently managed to minimize external cables, enclosing most electronics to prevent rodent interference. The LEDs were aligned flush with the interior walls, addressing past problems of rodents climbing or fighting with protruding hardware. Additionally, common 35 fasteners were employed wherever possible to reduce complexity and standardize the build process. Finally, Version 1.0 aimed to streamline the design by minimizing the number of fabricated parts, standardizing hardware components, and refining the layout for more efficient laser cutting and 3D printing. 36 Figure 20. 3-D Rendering of the Version 1.0 touchscreen chamber. a) Isometric view of the entire chamber. b) Side view highlighting the screens, fasteners, and punishment LED. c) Front view of the reward electronics module, showing the pump, liquid container, and motor driver. d) Front view of the front panel electronics module, housing the M0 boards, Raspberry Pi, punishment LED, and buzzer. e) Close‐up of the reward tray, illustrating the beam‐break sensors and feeding spout. 37 2.4.3.2 Hardware Components In the final version, we shifted from the IR-based touch sensor used in earlier iterations to capacitive touchscreens, primarily due to the discontinuation of the product line. Version 1.0 introduced capacitive LCD touchscreens. These screens detected rodent touches without separate external sensors, simplifying the hardware layout. However, each screen required fine-tuning of its sensitivity threshold to ensure it could detect subtle touches (e.g., from a mouse’ nose or paw) while preventing false triggers caused by static or electromagnetic noise. Initially, the thresholds that worked well for mice caused phantom touches from external interference. Grounding the chamber’s floor resolved this by providing a stable reference point and mitigating unintended activations. Another major upgrade was replacing the ESP32-E microcontroller with a Raspberry Pi 4 as the master controller. In the earlier versions, the ESP32-E had to be connected to a laptop running the PyQt GUI, whereas in Version 1.0, the GUI runs locally on the Raspberry Pi itself (requiring only a monitor, mouse, and keyboard). This switch provided multiple advantages: 1. Increased Computing Power: The Pi can handle real-time processing for multiple chambers, as well as more flexible networking options (e.g., remote monitoring or data uploads). 2. Faster Communications: Commands and data are now exchanged with M0 boards via a serial link rather than a shared I²C bus, enabling higher throughput and robust transfers (e.g., uploading images directly from raspberry pi to M0s). 3. Simplified Workflow: Because the Pi runs its own operating system and GUI, there is no need to connect the system to a separate laptop, streamlining the entire experimental setup. 38 The switch to serial communication from I²C was driven by the need for higher data throughput and more reliable point-to-point communication. This is important for tasks such as uploading images to M0s directly from the Raspberry Pi, eliminating manual uploads. While I²C suffices for simple sensor readings or commands, its shared-bus design can limit bulk data transfers. The Raspberry Pi 4 offers 2 hardware I²C buses by default, with options to enable more, but managing multiple buses can complicate wiring. Serial connections support higher baud rates with fewer signal integrity issues and require only one wire per M0, making them ideal for efficient data transfer in our setup. 2.4.3.3 Software and Firmware The M0 firmware remains largely the same as in earlier Arduino-based versions, focusing on loading images from flash storage, displaying them on the capacitive touchscreens, and detecting a single touch event per “show” cycle. During startup, each M0 board determines its own unique address by reading three pins, then initializes the display, touch controller, and flash interface so it can draw BMP images when requested. Communication with the Raspberry Pi occurs over a straightforward serial link: if the Pi sends a “WHOAREYOU?” query, the M0 replies with its ID (e.g., “ID:M0_0”). When commanded to load an image, the M0 firmware draws the file but initially keeps the backlight off. Another command activates the backlight; from that point on, the board listens for exactly one touch, reports its X–Y coordinates over serial, and finally switches the screen off. On the Raspberry Pi side, the entire set of training protocols is consolidated into a single Python program rather than requiring separate code uploads for each stage. A core class called “MultiPhaseTraining” defines multiple training stages—Habituation, Must Touch, Punish Incorrect, and so on. Once the user selects a stage from the GUI, the system automatically loads 39 the associated routine, initializes all necessary hardware components (LEDs, pumps, beam-break sensors, etc.), and proceeds through a sequence of trials using an uploaded CSV file of trial definitions. Each trial’s result (correct, incorrect, or no touch) is recorded immediately to a CSV log, capturing timestamps, displayed images, and any reward or punishment actions. Researchers can also export the trial data to CSV manually at any point. The Pi discovers and controls each M0 board, searches for valid serial ports, sends simple text commands, and collects the touch messages from the firmware. Meanwhile, the Pi coordinates all higher-level decisions: for instance, if a touch is correct, it activates the reward mechanism; if incorrect, it triggers a brief buzzer and punishment LED. These hardware actions are managed through small helper classes—“LED”, “Buzzer”, “Reward”, and “BeamBreak”—each of which uses the “pigpio”library to manipulate the Raspberry Pi’s GPIO pins (Pigpio is a C-based daemon and Python library designed for control of a Raspberry Pi’s general-purpose I/O pins). Earlier versions (v0.1 and v0.2) used a buzzer and reward LED together to signal a correct choice and reward delivery. However, because the peristaltic pump itself generates an audible cue when dispensing liquid, the buzzer was repurposed in Version 1.0 to serve as a punishment cue. 40 Figure 21. Graphical User Interface of the Version 1.0 touchscreen chamber. 2.4 Animal behavioral Training Protocol In the initial version (v.01), we developed a pilot training protocol consisting of three stages. The first phase involved associating the buzzer sound (6 KHz, over 2 seconds) with a green LED and a liquid reward. The second stage linked any touch on the screen with a reward. The third stage paired the correct stimulus with a reward. Following the third stage, rodents progressed to a pairwise discrimination task using two new stimuli. Later, we used a more refined training protocol adapted from the 2-Choice Pairwise Visual Discrimination Task, originally developed by the Prado Lab and later modified by the BrainsCAN Rodent Cognition Core. Our version builds on these foundational protocols by incorporating elements from previously established paradigms, including the touchscreen-based task described by (Wang et al., 2022). We introduced several refinements tailored to our specific 41 experimental goals. The final training sequence, described below, reflects these modifications and was optimized through iterative testing across multiple pilot studies. Habituation 1: Free Exploration Rodent is placed in the chamber for three consecutive days to allow them to explore and become comfortable in the apparatus, beginning with a 10-minute session on the first day, followed by 15 minutes on the second day, and 20 minutes on the third. Before each session, a small amount of liquid reward is placed in the reward tray to encourage exploration. This training stage allows rodents to explore and become comfortable in the touchscreen chamber. Criterion: None Habituation 2: Reward Port Familiarization Initially, the food tray is primed with a liquid reward (100 μL over 4,000 ms for rats; 26 μl over 1000 ms for mice), and the food tray light is turned on. After the rodent enters the reward port, the tray light is turned off. Once the rodent consumes the reward and leaves the port, a 10-second inter-trial interval (ITI) begins. If the rodent remains in the port at the end of that 10-second ITI, 1 second of delay is added for each additional second the rodent stays in the port. After the ITI (once the rodent has left the port), the tray light is turned on again, and a reward (78 μL over 3,000 ms for rats; 13 μl over 500 ms for mice) is delivered. The procedure repeats 30 times. This training stage teaches rodents to associate the reward port and its light with receiving a liquid reward. Criterion: Completion of 30 trials within 30 minutes. 42 Figure 22. Habituation 2: Reward Port Familiarization sequence of events schematic. Initial Touch Initially, the food tray is primed with liquid reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice). One image (e.g., a flower) appears randomly on one of the screens, while the opposite side remains blank. A touch on the image triggers immediate delivery of reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice) with the reward light turning on. A touch on the blank side results in a smaller reward (78 μl over 3,000 ms for rats; 13 μl over 500 ms for mice) and the reward light turning on. After the rodent enters the reward port, the tray light is turned off. Once the rodent consumes the reward and leaves the port, a 10 s ITI begins. The position of the images must be chosen pseudo randomly when creating the sequence file , such that the stimulus does not be displayed in the same position more than 3 times in a row. This training stage encourages rodents to touch the touchscreens and associate screen contact with a reward. 43 Criterion: Completion of 30 trials within 60 minutes. Figure 23. Initial Touch sequence of events schematic. Mandatory Image Touch Initially, the food tray is primed with liquid reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice). One image (e.g., a flower) appears randomly on one of the screens, while the opposite side remains blank. A touch on the blank side results in no response. The stimuli stay on the screen until the image is touched. Only a touch on the image triggers immediate delivery of reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice) with the reward light turning on. After the rodent enters the reward port, the tray light is turned off. Once the rodent consumes the reward and leaves the port, a 10 s ITI begins. After the ITI, screens display the stimulus ( a new trial begins).This training stage reinforces that only touching the image results in a reward, and that blank touches do not yield any reward. Criterion: Completion of 30 trials within 60 minutes. 44 Figure 24. Mandatory Image Touch sequence of events schematic. Must Initiate This stage trains the rodent to initiate a trial after an ITI. Initially, the food tray is primed with liquid reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice). One image (e.g., a flower) appears randomly on one of the screens, while the opposite side remains blank. A touch on the blank side results in no response. The stimuli stay on the screen until the image is touched. Only a touch on the image triggers immediate delivery of reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice) with the reward light turning on. After the rodent enters the reward port, the tray light is turned off. Once the rodent consumes the reward and leaves the port, a 10 s ITI begins. After the ITI finishes, the food tray light turns on, requiring the rodent to nose poke the reward tray and then exit it before an image is displayed. 45 This training stage teaches rodents to nose poke the reward port before each trial begins, establishing a clear trial initiation signal. Criterion: Completion of 30 trials within 60 minutes. Figure 25. Must Initiate sequence of events schematic. Incorrect Choice Punishment This stage follows the same procedure used in the Must Initiate stage, except that touches on the blank side trigger a 5-second punishment light timeout and a 1-second buzzer sound, with no reward delivered. Initially, the food tray is primed with liquid reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice). One image (e.g., a flower) appears randomly on one of the screens, while the opposite side remains blank. A touch on the blank side results in a 5-second punishment light timeout and a 1-second buzzer sound. Once punishment is over, a 10s ITI begins. On the other hand, a touch on the image triggers immediate delivery of reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice) with the reward light turning on. After the rodent enters the reward port, the tray light is turned off. Once the rodent consumes the reward 46 and leaves the port, a 10 s ITI begins. After the ITI finishes, the food tray light turns on, requiring the rodent to nose poke the reward tray and then exit it before an image is displayed. This stage increases discrimination accuracy by introducing a punishment for selecting the blank side. Criterion: Achieve at least 23 correct trials out of 30 (77%) within 60 minutes over two consecutive days. Figure 26. Incorrect Choice Punishment sequence of events schematic. Simple Pairwise Discrimination This stage follows the same procedure used in the Incorrect Choice Punishment stage, except that instead of a blank screen, an alternative stimulus (e.g., a cross) is presented. Just as in the Incorrect Choice Punishment stage, initially, the food tray is primed with liquid reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice). A previously trained image (e.g., a flower) appears randomly on one of the screens, while the opposite side displays a novel image (e.g. a 47 cross). Touching the novel stimulus (cross) results in a 5-second punishment light timeout and a 1-second buzzer sound. Once punishment is over, a 10s ITI begins. On the other hand, a touch on the old image (flower) triggers immediate delivery of reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice) with the reward light turning on. After the rodent enters the reward port, the tray light is turned off. Once the rodent consumes the reward and leaves the port, a 10 s ITI begins. After the ITI finishes, the food tray light turns on, requiring the rodent to nose poke the reward tray and then exit it before an image is displayed. Correction trials are also introduced here: if the incorrect stimulus is chosen, then following punishment and the ITI, the next round will repeat the same trial. The maximum number of correction trials can be adjusted based on the researcher’s preference. By implementing correction trials, this stage helps reduce side bias development. Criterion: Achieve at least 23 correct trials out of 30 (77%) within 60 minutes over two consecutive days. 48 Figure 27. Simple Pairwise Discrimination sequence of events schematic. Novel Pairwise Discrimination This stage follows the same procedure as the Simple Pairwise Discrimination stage, except that two new stimuli (e.g., a circle and three stripes) are used instead of the previous pair. Initially, the food tray is primed with liquid reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice). A novel image (e.g., a circle) appears randomly on one of the screens, while the opposite side displays another novel image (e.g. a stripes). Touching the stripes results in a 5-second punishment light timeout and a 1-second buzzer sound. Once punishment is over, a 10s ITI begins. On the other hand, a touch on the circle triggers immediate delivery of reward (100 μl over 4,000 ms for rats; 20 μl over 800 ms for mice) with the reward light turning on. After the 49 rodent enters the reward port, the tray light is turned off. Once the rodent consumes the reward and leaves the port, a 10 s ITI begins. After the ITI finishes, the food tray light turns on, requiring the rodent to nose poke the reward tray and then exit it before an image is displayed. Correction trials are also implemented in this stage. Criterion: Achieve at least 23 correct trials out of 30 (77%) within 60 minutes for two consecutive days. Figure 28. Novel Pairwise Discrimination sequence of events schematic. 50 Table 2. Summary of each training stage protocol. 51 2.5 Animals All procedures were approved by the Animal Care Committee at the University of British Columbia and conducted in accordance with guidelines set by the Canadian Council on Animal Care. AsclCre mice obtained from The Jackson Laboratory were crossed with Hm4Di mice to produce double transgenic mice in-house at the Centre for Disease Modeling at the University of British Columbia used for three of the experiments.. Additionally, C57BL/6NTac mice obtained from The Jackson Laboratory were crossed with B129S6 mice at the Centre for Disease Modeling at the University of British Columbia and used for one of the experiments. All mice were group-housed under a 12-hour light/dark cycle with lights on at 7 PM. They had ad libitum access to food and citric acid water. Long Evans rats were also obtained from Charles River Laboratory. Rats were single-housed before experiments began, provided with ad libitum water access, and food-restricted. 2.6 Feeding Restriction In our rat experiment (experiment 6), all rats were subjected to food restriction, maintaining their body weight at approximately 90% of their ad libitum weight. The weight management was closely monitored in accordance with a Charles River growth chart. The specific weights of each animal as recorded throughout the experiment, are detailed in Figure 29. 52 Figure 29. Weights of the animals (cohort 5) plotted over the experiment. a, b) Animals under food restriction maintained their body weight at approximately 90% of their ad libitum weight. 2.7 Citric Acid Water Restriction Throughout our experiments, mice were placed on a modified citric acid (CA) water regimen rather than standard water restriction (Urai et al., 2021). We tested the CA water restriction protocol in four 5‐month‐old mice (3 males and 1 female). First, the mice spent five consecutive days on plain water to establish baseline weights. Each day’s weight was normalized to the average baseline weight (Fig. 30). Then, for 11 days, their home‐cage water was switched to 1% CA. After that, the CA concentration was raised to 2%. During this time, each mouse was also trained daily on a touchscreen habituation task, receiving about 1,050 μl of water per session. The graph plots the mean normalized weight across the three mice, with error bars indicating the standard deviation of the mean. 53 Figure 30. Mice exhibit reduced weight and water intake in response to CA water, remaining within healthy limits. a) Normalized body weight (mean ± SD) plotted across days relative to the onset of water restriction (Day 0) in mice receiving plain water (black), 1% CA (red), or 2% CA (blue). Weights were normalized to each animal’s baseline average (collected from Days –5 to –1). Error bars represent inter-animal variability (standard deviation across the four mice per group). Mice provided with CA water exhibited a progressive reduction in body weight over time, with more pronounced weight loss observed in the 2% CA group. b) Daily water consumption (mL/day) measured during the treatment period for each group. Box plots show the median, interquartile range, and full range of individual data points. Mice receiving 1% CA (red) and 2% CA (blue) consumed substantially less water than the plain water group (black). 54 3. Results Six experiments were conducted to evaluate and refine different NC4Touch designs and the associated training protocols. Statistical analyses and visualizations were performed using MATLAB (version R2022b), and each experiment informed iterative improvements to both hardware and software. 3.1 Cohort 1: Testing Version 0.1 This initial experiment was conducted to assess the basic functionality of chamber Version 0.1 and identify potential hardware or software issues. The cohort included three male and three female Hm4Di mice, all six months old at the start of training and euthanized at eleven months. Mice underwent citric acid water restriction. As the primary goal of this experiment was debugging and optimization, no formal behavioral or statistical comparisons were made. Instead, the data served as diagnostic feedback to inform iterative refinements of the system. Key focus areas included: ● Hardware Testing: Adjustments were made to LED brightness, IR sensor sensitivity, buzzer volume, and reward volume to optimize responsiveness and animal engagement. ● Software Debugging: Trial events were logged and reviewed to verify that touches and rewards were recorded accurately and in sync with behavioral responses. ● Training Protocol Refinement: Mock trials were used to observe how mice interacted with the chamber, and task structure was adjusted accordingly. No structured dataset was compiled for formal analysis in this phase; instead, issues were identified and resolved on an iterative basis. 55 3.2 Cohort 2: Testing Version 0.2 The second experiment aimed to validate chamber Version 0.2 using a three-step pilot training protocol for a match-to-sample task. A cohort of three male and four female Hm4Di mice (all seven months old at onset, euthanized at nine months) was used. Stimuli included radial grating and small circles, both generated in MATLAB. The training proceeded as follows: 1) Locate the reward repository 2) Associate screen touch with reward 3) Match-to-sample task Figure 31. Diagram of the match to sample task. As an additional modification, a sound cue was introduced at the beginning of each trial to signal the mice that a new trial was starting. Additionally, there was no fixed ITI implemented for the match to sample task . Instead, for incorrect responses, a five-second delay was added before the next image was displayed. For correct responses, the next trial's images appeared one second 56 after the reward was delivered. In the match-to-sample task, a sample image of small circles was displayed on the top screen, and after 1.5 seconds, the same image appeared on one of the two bottom screens alongside a distractor, the radial grating. The stimuli remained on the screens until the mice made a selection by touching one of the bottom screens. A correct choice, touching the small circles, resulted in a reward of 28 µl of sucrose water, while an incorrect choice, touching the radial grating, resulted in a five-second delay before the next trial’s stimuli were displayed. Despite 19 days of training, the mice continued to perform at chance levels and did not reach the learning criterion (70% correct across two consecutive days). Several factors may have contributed: ● Absence of beam break detection: Without trial initiation logic, mice often remained near the screens between trials. Some incorrect responses were likely caused by accidental touches, such as body contact or tail movement. ● Complex Stimulus Presentation: In training stage 3 (match-to-sample task), different variations of the small circles and radial gratings were presented in each trial. Although the mice had learned how to interact with the chamber, the increased variability in stimulus appearance may have introduced additional difficulty, making the discrimination too complex for early training stages. Additionally, the correct stimulus was not counterbalanced across sides, which may have encouraged side-biased behavior. ● Slow image presentation: Images loaded progressively from one screen edge, prompting some mice to touch the partially rendered stimulus prematurely. Because the system only recognized a touch after the complete image was displayed, these early touches went unregistered. Consequently, a mouse might make what would otherwise be a correct 57 response prematurely, receive no reward, and then move to the other screen—which, by that time, might be fully rendered and potentially the incorrect stimulus. This mismatch between rodent behavior and system feedback sometimes resulted in an unintended penalty, creating confusion and potentially diminishing the animal’s motivation. ● Mild Punishment: A five-second delay for incorrect responses may not have been aversive enough to discourage errors. ● Ghost Touches: False positives and false negatives occurred intermittently, particularly during extended training sessions. These ghost touches became more frequent after a high number of trials. Further inspection suggested a memory leak—a software issue in which a program continuously allocates memory without releasing it, gradually reducing system performance. In this case, the IR sensor failed to clear stored touch data after each trial, which caused cumulative memory usage that eventually led to inconsistent or incorrect touch detection. After modifying the code to better manage memory allocation and clearing, the frequency of ghost touches was significantly reduced. However, occasional ghost touches continued to appear sporadically. While the cohort did not meet the learning criterion, this experiment provided critical feedback for refining task structure, stimulus presentation, reward/punishment protocols, and system stability in future versions. 3.3 Cohort 3: Testing Version 0.2 and Exposure Apparatus This experiment further evaluated chamber Version 0.2 under a refined training protocol (see section 2.4. Animal behavioral Training Protocol) and tested the exposure apparatus. A total of five male and four female AsclCre-Hm4Di double transgenic mice, aged 23 days at onset, were divided into two groups: 58 ● Experimental Group (5 males): Exposed to 30 variations of honeycomb stimuli (generated by Dr. Federico Bolaños (Bolaños et al., 2024)) from postnatal day 23 (P23) to P83 in the exposure apparatus. ● Control Group (4 females): Exposed to 30 variations of white noise stimuli over the same period. After two months of exposure, the screens were removed for an additional two months. Subsequently, from P143 to P191, all mice were tested in Version 0.2 on a pairwise visual discrimination. task. Figure 32. Cohort 3 training timeline. a) Experimental group training timeline b) Control group training timeline During their time in the exposure setup, the stimuli were displayed continuously, with M0 microcontrollers monitored remotely using ThingSpeak. During the exposure phase, cage conditions were checked twice daily, and no hardware issues or abnormal behavior were observed. All images were displayed as scheduled. However, no video data were collected for this cohort due to the later implementation of the camera system. 59 The training phase began on June 25, 2024 and continued until September 5, 2024 with mice being trained five to six times per week, completing 20 trials per session. The pre-training phase, which included habituation and incorrect choice punishment (using airplane versus blank stimulus), was successfully completed in 12 days. After pre-training, the mice were trained on a complex pairwise discrimination task for six weeks, directly progressing to the novel pairwise discrimination (flower versus honeycomb) stage while skipping the simple pairwise discrimination phase (airplane versus spider). Despite 30 days of training at this stage, the mice did not reach the learning criterion of 70% correct responses over two consecutive days. Several factors likely contributed to the failure to reach criterion: ● Inconsistent Beam Break Implementation: Beam break sensors were introduced at the reward port for the first time. Because they were externally mounted, their alignment required manual adjustments through trial and error. This led to variability in how reward collection was detected across sessions. ● Limited Trial Randomization: Stimuli were randomized by training stage but not across individual sessions. As a result, mice experienced repeated trial sequences, which may have promoted habitual responses. Correction trials were introduced mid-training, and different limits (15 or 20 maximum) were tested, though by that point, some mice had developed strong side preferences. ● Ghost Touches and Memory leaks: Sporadic ghost touches persisted during training, though they occurred less frequently than in the previous cohort. These included both false positives and false negatives, occasionally resulting in incorrect rewards or punishments. While code modifications improved performance, the system still required periodic manual resets of the ESP32-E and connected M0 boards to maintain stability. 60 ● ITI and Punishment Protocols: The ITI was initially set to 20 seconds, which led to reduced engagement and signs of restlessness. It was later decreased to 15, then 10 seconds. Additionally, correction trials were not included in early training sessions, which may have contributed to the development of side biases. While the mice did not reach the learning criterion, this experiment provided insights that guided refinements in subsequent iterations. The exposure apparatus functioned as expected, confirming its feasibility for future studies. However, the beam break positioning required further calibration to allow for accurate trial initiation and response detection. The importance of daily trial randomization became apparent, as non-randomized sequences contributed to habitual responses. The presence of ghost touches underscored the need to address memory management issues in the ESP32-E. These findings informed adjustments to stimulus presentation, task complexity, and system stability in future cohorts. 3.4 Cohort 4: Testing Version 0.2 A new cohort of five Ascl-Cre Hm4Di mice (four males, one female; three months old) was tested under a refined protocol for pairwise visual discrimination and the improved chamber. Mice were trained six days per week, with 20 trials per session, and a learning criterion of 70% correct over two consecutive days (excluding correction trials) (Fig. 33). Two males were excluded in the novel discrimination task due to consistent poor performance. Pre-training Performance: Flower vs. Black Discrimination All mice successfully completed the pretraining stages—Habituation 1, Habituation 2, Initial Touch, Mandatory Image Touch, Must Initiate, and Incorrect Choice Punishment (flower vs. black screen)—within 15 days. Simple Pairwise Discrimination: Flower vs. Spider Discrimination 61 The cohort then advanced to a simple discrimination task using flower vs. spider images. Over the first 10 days, no correction trials were used, leading several mice to develop strong side biases (Fig. 35). Correction trials were introduced after Day 10 to counteract this effect, and continued for another 14 days. Despite this adjustment, no mice reached the 70% criterion; one male consistently performed below chance and was excluded from further testing (Fig. 34) Retest on Known Stimulus: Flower vs Black Discrimination To confirm that the mice could recognize the flower stimulus the remaining four mice were re-evaluated using the precious training stage , the incorrect choice punishment task (flower vs. black screen). Three of the four reached >70% accuracy, confirming they could discriminate against the flower from a black background. The failure in the previous task thus appeared linked to the similarity between the flower and spider stimuli. New Stimulus Set: Circle vs. Stripes Discrimination We replaced the flower vs. spider stimuli with a new image set: circle vs. stripes. Over 19 days, two males and the single female reached passing criterion. One male again failed to do so and was excluded. Final Stimulus Set: Circle vs. Cross Discrimination The three remaining mice were tested on a third stimulus pair—circle vs. cross—for 12 days. Only one male reached the criterion. Training was then concluded to allow experimenters a break. 62 Figure 33. Cohort 4 training timeline. The number of days spent on each training stage until December 29 is demonstrated. 63 Figure 34. Daily mean percent correct across cohort 4 mice. Error bars represent standard deviation. Individual dots show performance per mouse per day. Correction trials were excluded from the calculations. A dashed line at 70% represents the criterion to advance to the next training stage. M1 and M2 both were excluded in the Novel Discrimination task. M3, M4 and F1 successfully completed the Novel Discrimination task. However, only M4 was able to complete Novel Discrimination 2 by December 29 before the study concluded. Side Bias Analysis To assess spatial response patterns, a side bias index was computed for each session, defined as the percentage of right-side touches minus 50%. Positive values indicate right-side bias; negative values indicate left-side bias. Values were plotted over training days, with shaded regions indicating different training stages (Fig. 35). Correction trials were excluded from both visualization and statistical analysis. One-sample t-tests were used to assess whether side biases deviated significantly from zero. To control for false positives across multiple comparisons, each p-value was compared against a Benjamini–Hochberg–adjusted threshold based on its rank; any p-value below this threshold was 64 considered statistically significant. Two of the five mice exhibited significant side biases during the Simple Discrimination stage without correction trials. Bias levels were reduced in the subsequent stage when correction trials were implemented. Full statistical details are provided in Appendix A. Right vs. left touch percentages across training days are shown in Appendix B. Figure 35. Side bias over training sessions for each mouse in cohort 4. Positive values indicate a right-side bias; negative values indicate a left-side bias. The horizontal dashed line denotes no side bias. Shaded regions correspond to distinct training stages. Correction trials were excluded from all calculations. 65 3.5 Cohort 5: Testing Version 1.0 This experiment aimed to validate the finalized NC4Touch system (Version 1.0) using a full behavioral training pipeline in rats. The cohort included two male and one female Long-Evans rats, aged two months at the onset of training. Animals were trained five days per week, with 30 trials per session. Pre-training and Pairwise Visual Discrimination Performance All rats successfully completed all pre-training stages and advanced through the simple and novel pairwise discrimination tasks. A full training timeline is shown in Figure 36. Figure 36. Cohort 5 training timeline. The number of days spent on each training stage is demonstrated. All rats successfully completed the Novel Discrimination Stage. 66 Correction Trial Trends Figure 37 shows the proportion of correction trials across training days and stages. Correction trial rates typically spiked at the beginning of each new stage and gradually declined over time, indicating improved performance accuracy and learning progression. Figure 37. Cohort 5 learning curves. Daily percent‐correct performance is shown for each rat (M1, M2, and F1) across successive training stages. The black line in each panel indicates the proportion of correction trials on that day, and the dashed horizontal line marks the 77 percent accuracy criterion used to advance to the next stage. Side Bias Analysis To examine spatial response preferences, a side bias index was calculated for each session, defined as the percentage of right-side touches minus 50%. Positive values indicate a right-side bias; negative values indicate a left-side bias. A value of zero indicates no side preference. Side bias trajectories were plotted across training days for each rat, with shaded regions indicating the different training stages (Figure 38). One-sample t-tests were used to assess whether side biases deviated significantly from zero. To control for false positives across multiple comparisons, each 67 p-value was compared against a Benjamini–Hochberg–adjusted threshold based on its rank; any p-value below this threshold was considered statistically significant. ● M1 exhibited a significant left-side bias only during the novel discrimination stage. ● No significant side biases were observed for M2 and F1. Detailed statistical results are provided in Appendix D. Right vs. left touch percentages across training days are shown in Appendix C. Figure 38. Side bias over training sessions for each animal in cohort 5. Positive values indicate a right-side bias; negative values indicate a left-side bias. The horizontal dashed line denotes no side bias. Shaded regions correspond to distinct training stages. Correction trials were excluded from all calculations. 68 3.6 Cohort 6: Testing Version 1.0 To validate the final version of NC4Touch for mice, three male and three female mice (four months old at the start of the experiment) are currently being trained on a pairwise visual discrimination task, five to six days a week. As shown in Figure 39, the training timeline indicates steady progression through the pre-training stages. Two female mice (F2 and F3) were excluded due to a lack of engagement, failing to complete 30 trials within 60 minutes on three consecutive days. Another female mouse (F2) was excluded after failing to perform above chance in the Incorrect Choice Punishment stage after 17 days of training. At the time of writing, all remaining animals are in the Novel Discrimination stage of training. While full behavioral results are not yet available, these preliminary findings demonstrate that mice can progress through the NC4Touch training pipeline, supporting its usability for mouse models. Figure 39. Cohort 6 training timeline. F3 and F2 excluded due to failure to complete 30 trials in 60 minutes in 3 consecutive days. F1 excluded due to consistent poor performance in incorrect choice punishment over 17 days. 69 Correction Trial Trends Figure 40 shows the proportion of correction trials across training days and stages for the three male mice. Similar to the previous cohort, the correction trial rates spiked at the beginning of each new stage and gradually declined over time, indicating improved performance accuracy and learning progression. Figure 40. Cohort 6 learning curves. Daily percent‐correct performance is shown for each mouse (M1, M2, and M3) across successive training stages. The black line in each panel indicates the proportion of correction trials on that day, and the dashed horizontal line marks the 77 percent accuracy criterion used to advance to the next stage. Side Bias Analysis The side bias index was calculated for each session as the percentage of right-side touches minus 50%. Correction trials were excluded from all visualizations and statistical analyses. One-sample t-tests were used to assess whether side biases deviated significantly from zero. To control for false positives across multiple comparisons, each p-value was compared against a 70 Benjamini–Hochberg–adjusted threshold based on its rank; any p-value below this threshold was considered statistically significant. None of the mice showed any significant side bias during Punish Incorrect Choice, Simple Discrimination and Novel Discrimination training stages. Detailed statistical results are provided in Appendix E. Figure 41. Side bias over training sessions for each animal in cohort 6. Positive values indicate a right-side bias; negative values indicate a left-side bias. The horizontal dashed line denotes no side bias. Shaded regions correspond to distinct training stages. Correction trials were excluded from all calculations. 71 4. Discussion We aimed to develop, refine, and validate a low-cost, open-source touchscreen-based cognitive testing system for rodents, alongside a modular exposure paradigm for passive visual exposure. Through five experiments involving both mice and rats, we tested multiple versions of the hardware and software, explored different training protocols, and identified technical and behavioral factors influencing learning performance. These results confirm that the final NC4Touch system can reliably support touchscreen-based behavioral training in both rats and mice. In the initial cohorts, the primary focus was debugging the system and identifying issues related to hardware functionality and software stability. Cohort 1 provided essential feedback on hardware testing and software debugging. The findings from these early experiments were instrumental in refining task presentation and response detection. For example, in Cohort 2, issues such as side biases, incomplete stimulus rendering, and ghost touches were identified as obstacles to training. These issues were resolved through iterative changes to stimulus presentation, ITI settings, and hardware stability. In Cohorts 3 and 4, further refinements were made to both hardware and training protocols. For instance, the introduction of beam-break sensors allowed for more accurate trial initiation, though challenges with their placement initially led to inconsistencies in trial detection. The decision to introduce correction trials in Cohort 4 improved performance by mitigating the development of side biases. This highlighted the importance of balancing task difficulty, stimulus salience, and animal behavior to optimize learning outcomes. 72 A major technical challenge identified during these cohorts was the occurrence of ghost touches, which were traced back to memory leak issues in the ESP32-E microcontroller. Although code modifications reduced the frequency of ghost touches, sporadic issues persisted, indicating that further refinement was needed in memory management. These insights were crucial in improving the system's overall reliability, with the frequency of ghost touches decreasing significantly in later experiments (Cohorts 5–6). The final validation of NC4Touch in Long-Evans rats (Cohort 5) demonstrated that the system could support full behavioral training pipelines, including pre-training, discrimination tasks, and error correction. Both male rats successfully completed the simple and novel pairwise discrimination tasks, and the female rat was in the process of completing the novel discrimination stage at the time of writing. Correction trial rates showed a consistent decrease over time, reflecting improved performance accuracy. However, spatial side biases were observed in some rats, with significant left-side bias in M1 during the novel discrimination stage. These biases were mitigated as training progressed, particularly with the use of correction trials. Preliminary testing of NC4Touch with mice (Cohort 6) showed that the system was also effective in supporting visual discrimination tasks in this species. Although full data are not yet available, the mice progressed through pre-training stages and continued to the Novel Discrimination stage. This provides early evidence that NC4Touch can be adapted for both rats and mice, with potential applications across various rodent species. While NC4Touch has demonstrated strong potential for rodent cognitive testing, several limitations remain. In earlier cohorts using prototype versions of the system with ESP32-E 73 microcontrollers, ghost touches were primarily attributed to memory leak issues—specifically, the failure of IR sensor data to clear properly after each trial. These leaks resulted in increasing system instability over time and were partially resolved through firmware modifications and periodic resets of the ESP32-E and M0 boards. In later cohorts using the final NC4Touch version (v1.0), ghost touches were still observed, though they originated from different causes. Specifically, intermittent ghost touches occurred after the chamber had been running for extended periods. Debugging revealed that the front acrylic panel—onto which the capacitive screens were mounted—sometimes shifted toward the touchscreens and triggered unintended responses. As a temporary solution, the software was modified to reduce the active touch area, preventing inputs from being registered at the edges if the panel shifted inward. In the long term, this issue will be addressed by slightly enlarging the acrylic cutouts to reduce interference with the screen housing. Additionally, electrical noise was found to contribute to false touch signals. This was mitigated by grounding the touchscreen floor, which substantially reduced ghost touches and improved system stability. To expand the capabilities of NC4Touch, we are actively developing a multi-chamber configuration in which several behavioral chambers can be run in parallel under the control of a single master Raspberry Pi. This configuration includes a centralized GUI for managing experiments across chambers and is expected to improve scalability and streamline experiment management for high-throughput studies. We also aim to integrate DeepLabCut, an open-source software for markerless tracking of animal movement, into the NC4Touch system. This addition will enable automated analysis of motor patterns, approach strategies, and other behavioral 74 metrics that go beyond touchscreen responses alone, providing a more comprehensive understanding of cognitive and behavioral performance. Another key direction is to expand the range of validated cognitive tasks. Currently, visual pairwise discrimination is the only paradigm tested in the final NC4Touch version. Future adaptations may include reversal learning, delayed match-to-sample, and object-location tasks—similar to those in the Bussey-Saksida system. Incorporating these paradigms would broaden NC4Touch's utility for studying various aspects of learning, and memory in rodents. As part of long-term system refinement, we also plan to design sound-attenuating enclosures to reduce external noise and enhance environmental control. 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Mouse Stage tStat df meanBias pValue AdjustedThreshold (BH) BiasDirection F1 Incorrect Choice Punishment 0.955434 8 3.961988 0.367333847 0.001852 RIGHT F1 Simple Discrimination (W/o CT) -3.74073 8 -22.037 0.00569937368742396* 0.003704 LEFT F1 Simple Discrimination (W CT) -0.23327 13 -1.54762 0.819181221 0.005556 LEFT F1 Novel Discrimination -5.37449 18 -25 0.0000415812489068544* 0.007407 LEFT F1 Novel Discrimination 2 -2.95134 11 -14.5833 0.013177782 0.009259 LEFT M1 Incorrect Choice Punishment -2.99696 8 -10.3509 0.017150935 0.011111 LEFT M1 Simple Discrimination (W/o CT) -2.635 7 -17.0018 0.033667364 0.012963 LEFT M1 Simple Discrimination (W CT) -2.39322 12 -14.7154 0.033935596 0.014815 LEFT M1 Novel Discrimination 0.627737 18 2.368421 0.538059161 0.018519 RIGHT M2 Incorrect Choice Punishment 2.369104 8 6.234568 0.045309728 0.02037 RIGHT 82 M2 Simple Discrimination (W/o CT) 1.290065 8 7.962963 0.233066826 0.022222 RIGHT M2 Simple Discrimination (W CT) -1.56551 13 -7.22789 0.1414717 0.024074 LEFT M2 Novel Discrimination 3.534977 9 23 0.00636404878902611* 0.027778 RIGHT M3 Incorrect Choice Punishment 0.70531 8 3.888889 0.500634553 0.02963 RIGHT M3 Simple Discrimination (W/o CT) 3.028913 8 18.51852 0.016336415* 0.031481 RIGHT M3 Simple Discrimination (W CT) 0.530543 13 3.214286 0.60468084 0.033333 RIGHT M3 Novel Discrimination -3.74892 18 -14.6053 0.00146948763273194* 0.037037 LEFT M3 Novel Discrimination 2 -0.08065 11 -0.41667 0.937166042 0.038889 LEFT M4 Incorrect Choice Punishment 0.491678 8 2.690058 0.636152572 0.040741 RIGHT M4 Simple Discrimination (W/o CT) 8.404008 7 37.70833 0.0000664601428101878* 0.042593 RIGHT M4 Simple Discrimination (W CT) -0.36981 13 -1.85074 0.717481102 0.044444 LEFT M4 Novel Discrimination -0.43845 18 -1.39098 0.666274504 0.048148 LEFT M4 Novel Discrimination 2 4.622774 11 13.125 0.000737032259619951* 0.05 RIGHT 83 Figure A. Summary of one-sample t-tests for cohort 4 side bias during each training stage per mouse with Benjamini–Hochberg correction for multiple comparisons. This table reports the results of one-sample t-tests evaluating whether mice showed a significant side bias—defined as deviation from 50% right-side touches—during each training stage. Positive t-values reflect a right-side bias; negative t-values indicate a left-side bias. The AdjustedThreshold (BH) column reflects the BH-corrected p-value threshold for significance based on the false discovery rate. Comparisons where the p-value falls below the BH-adjusted threshold are marked with an asterisk (*) and bolded. Correction trials were excluded from all analyses. A result is considered significant when the raw p-value is less than or equal to the BH-adjusted threshold. 84 B. Figure B. Percentage of left and right screen touches across training days for each mouse. Line plots show the proportion of left and right touches made by each mouse across sessions, with different shaded regions indicating training stages. Each point represents a single training session. Correction trials were excluded from the data. 85 C. Figure C. Percentage of left and right screen touches across training days for each rat. Line plots show the proportion of left and right touches made by each rat across sessions, with different shaded regions indicating training stages. Each point represents a single training session. Correction trials were excluded from the data. 86 D. Rat Stage tStat df meanBias pValue AdjustedThresho-ld (BH) BiasDi-rection M1 Punish Incorrect -1 2 -4.44444 0.42265 0.022222222 LEFT M1 Simple Discrimination -2.03488 7 -6.50552 0.081325 0.016666667 LEFT M1 Novel Discrimination -4.16762 8 -21.4028 0.003132* 0.00555555555555556 LEFT M2 Punish Incorrect -0.46625 4 -3.33333 0.665294 0.044444444 LEFT M2 Simple Discrimination -0.73465 4 -1.63197 0.503297 0.033333333 LEFT M2 Novel Discrimination 0.772672 9 4.362101 0.459526 0.027777778 RIGHT F1 Punish Incorrect -0.56986 7 -5 0.586587 0.038888889 LEFT F1 Simple Discrimination 2.788663 11 7.792844 0.017629 0.011111111 RIGHT F1 Novel Discrimination -0.11914 10 -0.27173 0.907522 0.05 LEFT Figure D. Summary of one-sample t-tests for cohort 5 side bias during each training stage per rat, with Benjamini–Hochberg correction for multiple comparisons. This table reports the results of one-sample t-tests evaluating whether mice showed a significant side bias—defined as deviation from 50% right-side touches—during each training stage. Positive t-values reflect a right-side bias; negative t-values indicate a left-side bias. The AdjustedThreshold (BH) column reflects the BH-corrected p-value threshold for significance based on the false discovery rate. Comparisons where the p-value falls below the BH-adjusted threshold are marked with an asterisk (*) and bolded. Correction trials were excluded from all analyses. A result is considered significant when the raw p-value is less than or equal to the BH-adjusted threshold. 87 E. Mo-use Stage tStat df meanBias pValue AdjustedThreshold (BH) BiasDirection M1 Complex Discrimination 1.619698 4 11.46361 0.180611 0.011111 RIGHT M1 Punish Incorrect -1.05796 10 -5.15152 0.314959 0.022222 LEFT M1 Simple Discrimination -0.04495 2 -0.13592 0.96823 0.05 LEFT M2 Complex Discrimination 0.936549 4 7.406298 0.402013 0.027778 RIGHT M2 Punish Incorrect -1.21578 9 -7.66667 0.254997 0.016667 LEFT M2 Simple Discrimination 0.894737 3 4.166667 0.436856 0.033333 RIGHT M3 Complex Discrimination 0.231897 4 1.153822 0.827998 0.044444 RIGHT M3 Punish Incorrect 0.351391 9 2 0.733381 0.038889 RIGHT M3 Simple Discrimination -1.88715 3 -4.46899 0.155592 0.005556 LEFT Figure E. Summary of one-sample t-tests for side bias during each training stage per mouse, with Benjamini–Hochberg correction for multiple comparisons. This table reports the results of one-sample t-tests evaluating whether mice showed a significant side bias—defined as deviation from 50% right-side touches—during each training stage. Positive t-values reflect a right-side bias; negative t-values indicate a left-side bias. The AdjustedThreshold (BH) column reflects the BH-corrected p-value threshold for significance based on the false discovery rate. Comparisons where the raw p-value falls below the BH-adjusted threshold are marked with an asterisk (*). Correction trials were excluded from all analyses. A result is considered significant when the raw p-value is less than or equal to the BH-adjusted threshold. 88
Thesis/Dissertation
eng
Biomedical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Madhav, Manu
Snyder, Jason
NC4Touch : an open-source platform for rodent behavioural training
Text