Open-source real-time feedback system for shaping cortical and behavioral motor dynamics in mice reveal altered cortical networks and behavior with closed-loop training
department or school: Medicine, Faculty of
data provider: DSpace
degree campus: UBCV
creator: Gupta, Kumar Pankaj
issued: 2025-10-22T00:21:08Z
issued: 2025
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Understanding how the brain learns and adapts to behavioral demands requires precise experimental control and computational frameworks capable of revealing large-scale neural dynamics. This thesis presents two complementary approaches: (1) the development of a closed-loop experimental platform for real-time neurofeedback and movement feedback in mice, and (2) a biologically constrained recurrent neural network (RNN) framework to model cortical interactions during learning tasks. First, I developed an open-source system, Closed-Loop-Py (CLoPy), enabling closed-loop neurofeedback (CLNF) and closed-loop movement feedback (CLMF) with low-latency auditory feedback (~63â67 ms). In CLNF experiments, dorsal cortical activity was monitored using widefield GCaMP6s calcium imaging, allowing mice to learn to modulate specific cortical regions of interest (ROIs) to obtain rewards. Both sensory and motor cortices supported learning, with animals adapting to changing task rules over training days (Repeated Measures ANOVA p = 8.3 Ã 10â»Â¹â°). In CLMF experiments, auditory feedback was based on limb movement, and mice rapidly improved performance compared to controls (RM ANOVA p = 9.6 Ã 10â»â·). Learning was accompanied by reduced task latency and focal cortical activity, suggesting greater behavioral and neural efficiency. These results establish CLoPy as a flexible and accessible platform for studying brain-behavior coupling through real-time feedback. Building on this foundation, I developed CorNet RNN, a modular, biologically inspired computational model for exploring how cortical networks coordinate behavior. Using simulated multi-region datasets, the model accurately recovered hidden inter-regional inputs. When trained on the Allen Instituteâs Visual Behavior 2P (VB2P) calcium imaging dataset, CorNet RNN reproduced empirically observed temporal dynamics and connectivity patterns. The network employed correlation-based weight initialization reflecting biological connectivity an…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/92694
note: Open-source real-time feedback system for shaping cortical and behavioral motor dynamics in mice reveal altered cortical networks and behavior with closed-loop training by Kumar Pankaj Gupta MSc., Trinity College Dublin, 2012 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (NEUROSCIENCE) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2025 © Kumar Pankaj Gupta, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Open-source real-time feedback system for shaping cortical and behavioral motor dynamics in mice reveal altered cortical networks and behavior with closed-loop training submitted by Kumar Pankaj Gupta in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Neuroscience Examining Committee: Dr. Timothy H. Murphy, Professor, Psychiatry, UBC Supervisor Dr. Mark S. Cembrowski, Associate Professor, Cellular and Physiological Sciences, UBC Supervisory Committee Member Dr. Naznin Virji-Babul, Associate Professor, Physical Therapy, UBC University Examiner Dr. Robert Xiao, Associate Professor, Computer Science, UBC University Examiner Dr. Francesco Saverio Pavone, Professor, University of Florence External Examiner Additional Supervisory Committee Members: Dr. Lara Boyd, Professor, Physical Therapy, UBC Supervisory Committee Member Dr. Jeremy Seamans, Professor, Psychiatry, UBC Supervisory Committee Member iii Abstract Understanding how the brain learns and adapts to behavioral demands requires precise experimental control and computational frameworks capable of revealing large-scale neural dynamics. This thesis presents two complementary approaches: (1) the development of a closed-loop experimental platform for real-time neurofeedback and movement feedback in mice, and (2) a biologically constrained recurrent …
has type: Thesis/Dissertation
date issued: 2025-11
is shown at: 10.14288/1.0450515
language: eng
degree discipline: Neuroscience
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Murphy, Timothy H.
type: Text
identifier URI: http://hdl.handle.net/2429/92694