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 role in the processing of visual stimuli. In this talk, I will present a model-based approach to understanding the neural code in populations of spiking neurons from primate retina. A multivariate, generalized linear point-process model captures both the stimulus-dependence and the spatio-temporal correlation structure of responses from a complete population of retinal ganglion cells, and can be used to assess the importance of correlations for 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.
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