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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

Coding for motion in the receptive field surround in the salamander retina.A: Schematic of the experiment: the bar is randomly moved at three different average locations relative to the array. B, C, D: Prediction of the bar’s trajectory using the linear decoding (black); real trajectory (red), for the three average locations above. E: Performance of linear decoding (blue) for individual ganglion cells (dots) plotted as a function of the distance between their receptive field center coordinate and the average bar position; probability distribution of bar position (black). Blue line: average decoding performance as a function of the distance. F: Performance of linear decoding (blue) for individual ganglion cells (blue dots) plotted as a function of the normalized distance between their receptive field and the average bar position (see text). Blue line: average decoding performance as a function of the normalized distance.
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pcbi.1004304.g005: Coding for motion in the receptive field surround in the salamander retina.A: Schematic of the experiment: the bar is randomly moved at three different average locations relative to the array. B, C, D: Prediction of the bar’s trajectory using the linear decoding (black); real trajectory (red), for the three average locations above. E: Performance of linear decoding (blue) for individual ganglion cells (dots) plotted as a function of the distance between their receptive field center coordinate and the average bar position; probability distribution of bar position (black). Blue line: average decoding performance as a function of the distance. F: Performance of linear decoding (blue) for individual ganglion cells (blue dots) plotted as a function of the normalized distance between their receptive field and the average bar position (see text). Blue line: average decoding performance as a function of the normalized distance.

Mentions: From this lack of spatial structure we hypothesized that even the ganglion cells whose receptive fields were far from the bar carried information about the trajectory. To test this, we displayed the randomly moving bar stimulus in three different average locations, each separated by 430 microns and lasting 20 minutes (see Methods and Fig 5A). We then estimated the bar’s trajectory for each stimulus ensemble one-at-a-time. The trajectory could be decoded for the three locations at nearly the same level of high performance (Fig 5B, 5C, 5D).


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)

Coding for motion in the receptive field surround in the salamander retina.A: Schematic of the experiment: the bar is randomly moved at three different average locations relative to the array. B, C, D: Prediction of the bar’s trajectory using the linear decoding (black); real trajectory (red), for the three average locations above. E: Performance of linear decoding (blue) for individual ganglion cells (dots) plotted as a function of the distance between their receptive field center coordinate and the average bar position; probability distribution of bar position (black). Blue line: average decoding performance as a function of the distance. F: Performance of linear decoding (blue) for individual ganglion cells (blue dots) plotted as a function of the normalized distance between their receptive field and the average bar position (see text). Blue line: average decoding performance as a function of the normalized distance.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4489022&req=5

pcbi.1004304.g005: Coding for motion in the receptive field surround in the salamander retina.A: Schematic of the experiment: the bar is randomly moved at three different average locations relative to the array. B, C, D: Prediction of the bar’s trajectory using the linear decoding (black); real trajectory (red), for the three average locations above. E: Performance of linear decoding (blue) for individual ganglion cells (dots) plotted as a function of the distance between their receptive field center coordinate and the average bar position; probability distribution of bar position (black). Blue line: average decoding performance as a function of the distance. F: Performance of linear decoding (blue) for individual ganglion cells (blue dots) plotted as a function of the normalized distance between their receptive field and the average bar position (see text). Blue line: average decoding performance as a function of the normalized distance.
Mentions: From this lack of spatial structure we hypothesized that even the ganglion cells whose receptive fields were far from the bar carried information about the trajectory. To test this, we displayed the randomly moving bar stimulus in three different average locations, each separated by 430 microns and lasting 20 minutes (see Methods and Fig 5A). We then estimated the bar’s trajectory for each stimulus ensemble one-at-a-time. The trajectory could be decoded for the three locations at nearly the same level of high performance (Fig 5B, 5C, 5D).

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