Abstract
Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar’s position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina’s population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar’s position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits.
Highlights
Our current understanding of how sensory neurons collectively encode information about the environment is limited
We recorded a large population of ganglion cells, the retinal output, in a dense patch of salamander and guinea pig retinas while displaying a bar moving in complex motion
The naive expectation was that individual ganglion cells would spike when an object was moving on their receptive field center and that the entire population’s activity would resemble a “hill” that continuously tracked the object’s location
Summary
Our current understanding of how sensory neurons collectively encode information about the environment is limited. Being able to reconstruct a dynamical stimulus from the neural activity would greatly improve our understanding of the neural code, but there have been only a few attempts in the visual system [10,11,12]. The retina is an ideal circuit in which to attempt to decode a dynamical stimulus because recordings from a large, diverse, complete population of ganglion cells—the retinal output—have recently become possible [13, 14]. Since the retina encodes all visual information available to the brain, the decoding performance of a complete retinal population can be rigorously compared to behavioral performance for an equivalent task [15, 16]
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