Dissecting inhibitory mechanisms and their contribution to information processing in retinal ganglion cells. - ABSTRACT A fundamental goal in neuroscience is to determine how information is coded by the spiking activity of a neural population. In the retina, parallel circuits process different features of visual input into a pattern of spatiotemporal activity across retinal ganglion cells (RGCs). Each RGC type corresponds to a distinct parallel processing channel, collectively aimed at simultaneously encoding diverse features from the entire visual field. Given the limited capacity of the optic nerve for information transmission, the RGC population has evolved to be highly efficient at this task. A diverse set of complex linear and nonlinear computations achieves this efficient coding, but the parallel retinal circuits supporting these computations are poorly understood. This project will investigate how feed-forward inhibition onto RGCs contributes to the parallel processing of visual scenes. In particular, I will determine how GABAergic inhibition shapes the feature selectively of RGCs and the signaling performed by a population of neurons. To identify the role of feed-forward GABAergic inhibition onto different RGC types, I have developed an innovative approach that combines a chemogenetic technique called DART to selectively modulate GABAA receptors on RGCs with simultaneous recording of spiking activity from hundreds of RGCs with multi-electrode arrays. In Aim 1, I will evaluate how inhibition shapes the RGC- type specific spatiotemporal linear filter, which describes how visual input is encoded linearly in space and time. In Aim 2, I will identify how feed-forward inhibition contributes to static nonlinearity, which defines the relative sensitivity of RGC responses to different visual stimuli. Lastly, in Aim 3, I will determine how GABA inhibition shapes correlated activity, a crucial aspect for population-level decoding. The expected outcome of this proposal will be identifying how synapse-level mechanisms shape the output of the retina. The project is significant because determining the inhibitory signaling pathways underlying neural computations is critical for comprehending information processing with broader implications for fields like perception, behavior, and adaptation.
