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High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.

Marre O, Botella-Soler V, Simmons KD, Mora T, Tkačik G, Berry MJ - PLoS Comput. Biol. (2015)

Bottom Line: 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.The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar's position.As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biology and Neuroscience Institute, Princeton University, Princeton, United States of America; Institut de la Vision, INSERM UMRS 968, UPMC UM 80, CNRS UMR 7210, Paris, France.

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.

No MeSH data available.


Related in: MedlinePlus

Redundancy of the retinal code.A, B: Information rate obtained when decoding with different subsets of ganglion cells (dots) plotted against the number of cells (A) or the sum of the individual information rate of each cell of the subset (B); color scale indicates the number of cells in a subset. C, D: Plots analogous to panels A, B but for the guinea pig retina. E, F: Information rate for the best (blue) and worst (red) subsets of salamander ganglion cells (see text) plotted against the number of cells (E) or the sum of the individual information rate of each cell of the subset (F).
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pcbi.1004304.g007: Redundancy of the retinal code.A, B: Information rate obtained when decoding with different subsets of ganglion cells (dots) plotted against the number of cells (A) or the sum of the individual information rate of each cell of the subset (B); color scale indicates the number of cells in a subset. C, D: Plots analogous to panels A, B but for the guinea pig retina. E, F: Information rate for the best (blue) and worst (red) subsets of salamander ganglion cells (see text) plotted against the number of cells (E) or the sum of the individual information rate of each cell of the subset (F).

Mentions: Information about the moving bar’s trajectory encoded by subsets of ganglion cells increased as more cells were added (Fig 7A). But for the same number of cells, the performance varied substantially depending on the particular cells chosen in the subset. A natural hypothesis for this variability is that some cells carry more information about the stimulus than others. To explore this relationship, we compared the “total” information encoded by a group of ganglion cells (y-axis in Fig 7A and 7B) versus the sum of the individual informations encoded by each cell in the group (x-axis in Fig 7B). Plotted in these units, there was much less variability in the information encoded by different groups of ganglion cells. The same result was obtained in the guinea pig retina (Fig 7C, 7D).


High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.

Marre O, Botella-Soler V, Simmons KD, Mora T, Tkačik G, Berry MJ - PLoS Comput. Biol. (2015)

Redundancy of the retinal code.A, B: Information rate obtained when decoding with different subsets of ganglion cells (dots) plotted against the number of cells (A) or the sum of the individual information rate of each cell of the subset (B); color scale indicates the number of cells in a subset. C, D: Plots analogous to panels A, B but for the guinea pig retina. E, F: Information rate for the best (blue) and worst (red) subsets of salamander ganglion cells (see text) plotted against the number of cells (E) or the sum of the individual information rate of each cell of the subset (F).
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pcbi.1004304.g007: Redundancy of the retinal code.A, B: Information rate obtained when decoding with different subsets of ganglion cells (dots) plotted against the number of cells (A) or the sum of the individual information rate of each cell of the subset (B); color scale indicates the number of cells in a subset. C, D: Plots analogous to panels A, B but for the guinea pig retina. E, F: Information rate for the best (blue) and worst (red) subsets of salamander ganglion cells (see text) plotted against the number of cells (E) or the sum of the individual information rate of each cell of the subset (F).
Mentions: Information about the moving bar’s trajectory encoded by subsets of ganglion cells increased as more cells were added (Fig 7A). But for the same number of cells, the performance varied substantially depending on the particular cells chosen in the subset. A natural hypothesis for this variability is that some cells carry more information about the stimulus than others. To explore this relationship, we compared the “total” information encoded by a group of ganglion cells (y-axis in Fig 7A and 7B) versus the sum of the individual informations encoded by each cell in the group (x-axis in Fig 7B). Plotted in these units, there was much less variability in the information encoded by different groups of ganglion cells. The same result was obtained in the guinea pig retina (Fig 7C, 7D).

Bottom Line: 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.The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar's position.As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biology and Neuroscience Institute, Princeton University, Princeton, United States of America; Institut de la Vision, INSERM UMRS 968, UPMC UM 80, CNRS UMR 7210, Paris, France.

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.

No MeSH data available.


Related in: MedlinePlus