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Assessing the effects of statistical dependencies on neural population coding in the visual pathway

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If you have a question about this talk, please contact Prof Máté Lengyel .

How does the joint spiking activity of a neural population encode a visual scene? Although the response properties of individual neurons in the visual pathway have been well-studied, much less is known about the dependencies between neurons, and their significance for the processing of visual stimuli. In this talk, I will present a model-based approach to understanding the neural code in a population of spiking neurons recorded in primate retina. A multivariate point-process model (formulated as a generalized linear model, or GLM ) captures both the stimulus-dependence and spatio-temporal correlation structure of responses from a complete population of retinal ganglion cells. In addition to a description of the encoding process, the model provides a tool for assessing the importance of dependencies to visual processing, via Bayesian decoding of population spike responses. I will discuss the implications of this framework for studying the role of correlated activity in the encoding and decoding of sensory signals.

This talk is part of the Computational Neuroscience series.

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