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Large scale neuronal recording

University of British Columbia

Deciphering the neural basis of brain function will require a significant departure from the reductionistic status quo that has dominated the neurosciences over the past 50 years. In the domain of neurophysiology, this means supplanting single unit recording with electrodes capable of monitoring the activity of hundreds, and ultimately thousands, of neurons simultaneously. Conducting experiments on one or a few neurons at a time and then making elaborate conclusions or models based on these piecewise experiments is not sufficient. Therefore, the objectives of my dissertation were to develop a variety of multisite silicon-based electrode arrays, or polytrodes, and establish a set of analytical tools to realise their unique recording capabilities. Chapter 1 describes the design and testing of high density,' 54-site polytrodes, and their use in multiunit studies of cat visual cortex. These polytrodes were able to monitor the activity of more than 100 well-isolated neurons spanning an entire cortical column, a milestone for future experimental studies of cortical circuits. I also describe a continuous data acquisition system designed to cope with the high bandwidth of polytrodes, and techniques for precise electrode positioning. The benign nature of polytrodes was evident both histologically and in prolonged experiments where it was possible to maintain stable recordings from the same neuronal ensemble, even when the polytrode was repeatedly moved. Polytrodes present significant challenges for conventional spike detection and sorting methods that must be solved before physiological studies are possible. In chapter 2, ideal bandlimited interpolation with sample-and-hold delay correction is shown to accurately reconstruct spike shapes and facilitate spike detection and sorting by reducing waveform variability. Optimal methods of spike detection and sorting were explored in chapter 3 using real and simulated data. A new sorting algorithm that combines unsupervised template generation with multisite template matching was accurate for signal to noise ratios as low as one, and resilient to partial spike overlap. Unlike most existing sorting algorithms, this one is suitable for large contiguous electrode arrays, and is computationally feasible for extended recordings comprising millions of spikes. Extracellular electrodes do not usually provide accurate information about recorded neuron location, nor any indication of cell type. Chapter 4 describes an algorithm that capitalises on the fixed, closely-spaced site geometry of polytrodes to localise neurons in 3D cortical space. The algorithm was based on a mixed monopole-dipole field model of extracellular spike potentials and was able to generalise to arbitrary neuron orientation, tissue anisotropies, and cell morphology. Estimated neuron locations emerged as non-overlapping spherical clusters within 150um of the polytrode. Cluster locations moved concordantly with polytrode movements, making the algorithm a useful method for spike sorting unperturbed by electrode drift. Field potential spreads were consistent with the spike shapes and firing patterns of pyramidal cells and interneurons. These results suggest it is eminently possible to identify both the cortical location and type of neurons recorded extracellularly with high density polytrodes. Chapter 5, the concluding chapter, considers a number of outstanding questions in visual neurophysiology that polytrodes are ideally suited to explore - questions such as the organisation of micro-scale cortical maps, specific cell types responsible for intracortical mechanisms of receptive field tuning, and the identification of precise temporal codes across neural populations. Utilising the parallel recording capabilities of polytrodes, it was possible to characterise the response properties of a large number of neurons to a wide range of visual stimuli, instead of just a few neurons to a narrow selection of stimuli. Since the data were derived from the same neural population, the hope is that a fuller, more unified understanding will be gained of primary visual cortex function, beyond that possible by combining findings from independent experiments.

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2009-12-23

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http://hdl.handle.net/2429/17166

Neuroscience

University of British Columbia

UBCV

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