Coding and computation by neural ensembles in the retina

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By Liam Paninski, Columbia.

The neural coding problem --- deciding which stimuli will cause a given neuron to spike, and with what probability --- is a fundamental question in systems neuroscience. We apply statistical modeling methods to analyze data recorded from a complete mosaic of macaque parasol retinal ganglion cells in a small region of visual space. We find that a surprisingly simple model with functional coupling between neurons captures both the stimulus dependence and the detailed spatiotemporal correlation structure of multi-neuronal responses; in addition, ongoing network activity in the retina accounts for a significant portion of the trial-to-trial variability in a neuron's response. We assess the significance of correlated spiking by performing optimal Bayesian decoding of the population spike responses; we find that approximately 20% more stimulus-related information is captured when correlations are taken into account. Finally, we discuss work in progress on the following questions: how much temporal precision is necessary to capture the neural code in the retina? How can we adapt our optimal decoding methods to estimate behaviorally relevant signals such as image velocity? How do we perceive stable images when the retina must contend with the constant motion due to small random eye movements?
Joint work with Y. Ahmadian, E. Lalor, J. Kulkarni, X. Pitkow, T. Toyoizumi, E.J. Chichilnisky, J. Pillow, E. Simoncelli, and J. Shlens.

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