Fine Spatial Information Represented in a Population of Retinal Ganglion Cells

Soo, Frederick S.; Schwartz, Greg; Sadeghi, Kolia; Berry, Michael

doi:10.1523/jneurosci.5129-10.2011

Cited by 23 publications

(29 citation statements)

References 40 publications

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“…At this point, the information carried by multiple retinal ganglion cells is, on average, redundant, so that the information per spike declines as we examine the responses of larger groups of neurons. Although the details of the experiments are different, the observation of coding redundancy at t lat is consistent with many previous results (9)(10)(11)(12)(13)(14)(15)(16)(17). However, the information that neural responses carry about position extends far into the past, t′ t − t lat , and more importantly…”

Section: Coding For the Position Of A Single Visual Objectsupporting

confidence: 87%

Predictive information in a sensory population

Palmer

Marre

Berry

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

221

276

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Guiding behavior requires the brain to make predictions about the future values of sensory inputs. Here, we show that efficient predictive computation starts at the earliest stages of the visual system. We compute how much information groups of retinal ganglion cells carry about the future state of their visual inputs and show that nearly every cell in the retina participates in a group of cells for which this predictive information is close to the physical limit set by the statistical structure of the inputs themselves. Groups of cells in the retina carry information about the future state of their own activity, and we show that this information can be compressed further and encoded by downstream predictor neurons that exhibit feature selectivity that would support predictive computations. Efficient representation of predictive information is a candidate principle that can be applied at each stage of neural computation.neural coding | retina | information theory A lmost all neural computations involve making predictions. Whether we are trying to catch prey, avoid predators, or simply move through a complex environment, the data we collect through our senses can guide our actions only to the extent that these data provide information about the future state of the world. Although it is natural to focus on the prediction of rewards (1), prediction is a much broader problem, ranging from the extrapolation of the trajectories of moving objects to the learning of abstract rules that describe the unfolding pattern of events around us (2-4). An essential aspect of the problem in all these forms is that not all features of the past carry predictive power. Because there are costs associated with representing and transmitting information, it is natural to suggest that sensory systems have optimized coding strategies to keep only a limited number of bits of information about the past, ensuring that these bits are maximally informative about the future. This principle can be applied at successive stages of signal processing, as the brain attempts to predict future patterns of neural activity. We explore these ideas in the context of the vertebrate retina, provide evidence for nearoptimal coding, and find that this performance cannot be explained by classical models of ganglion cell firing. Coding for the Position of a Single Visual ObjectThe structure of the prediction problem depends on the structure of the world around us. In a world of completely random stimuli, for example, prediction is impossible. Consider a simple visual world such that, in the small patch of space represented by the neurons from which we record, there is just one object (a dark horizontal bar against a light background) moving along a trajectory x t . We want to construct trajectories that are predictable, but not completely; the moving object has some inertia, so that the velocities υ t are correlated across time, but is also "kicked" by unseen random forces. A mathematically tractable example (Eqs. 4 and 5 in Materials and Methods) is shown in Fig...

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Section: Coding For the Position Of A Single Visual Objectsupporting

confidence: 87%

Predictive information in a sensory population

Palmer

Marre

Berry

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

221

276

View full text Add to dashboard Cite

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“…More recently, electrical stimulation of individual cones in primate retina was shown to drive significant synaptic currents in parasol cells (Ala-Laurila et al, 2011). The present findings are also consistent with currents evoked in salamander RGCs by stimuli illuminating 1–2 cones (Soo et al, 2011), although again the number of cones feeding the RGCs was not reported.…”

Section: Discussionsupporting

confidence: 90%

Retinal Representation of the Elementary Visual Signal

Field

Greschner

et al. 2014

Neuron

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Summary The propagation of visual signals from individual cone photoreceptors through parallel neural circuits was examined in the primate retina. Targeted stimulation of individual cones was combined with simultaneous recording from multiple retinal ganglion cells of identified types. The visual signal initiated by an individual cone produced strong responses with different kinetics in three of the four numerically dominant ganglion cell types. The magnitude and kinetics of light responses in each ganglion cell varied nonlinearly with stimulus strength, but in a manner that was independent of the cone of origin after accounting for the overall input strength of each cone. Based on this property of independence, the receptive field profile of an individual ganglion cell could be well estimated from responses to stimulation of each cone individually. Together these findings provide a quantitative account of how elementary visual inputs form the ganglion cell receptive field.

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“…RGC receptive fields exhibit large-scale structure that deviates from either circular or elliptical Gaussian profiles 12 . Heterogeneities on a finer scale have been measured as “hot-spots” within the receptive field that are unique for each RGC 13–16 . Brown et al 15 measured fine-scale heterogeneity in the receptive fields of rabbit RGCs, but did not find a clear correspondence between the receptive field hot-spots and the dendritic patterns of individual RGCs.…”

Section: Introductionmentioning

confidence: 99%

The spatial structure of a nonlinear receptive field

et al. 2012

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Understanding a sensory system implies the ability to predict responses to a variety of inputs from a common model. In the retina, this includes predicting how the integration of signals across visual space shapes the outputs of retinal ganglion cells. Existing models of this process generalize poorly to predict responses to novel stimuli. This failure arises in part from properties of the ganglion cell response that are not well captured by standard receptive field mapping techniques: nonlinear spatial integration and fine-scale heterogeneities in spatial sampling. Here, we characterize a ganglion cell’s spatial receptive field using a mechanistic model based on measurements of the physiological properties and connectivity of only the primary excitatory circuitry of the retina. The resulting simplified circuit model successfully predicts ganglion cell responses to a variety of spatial patterns and thus provides a direct correspondence between circuit connectivity and retinal output.

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Fine Spatial Information Represented in a Population of Retinal Ganglion Cells

Cited by 23 publications

References 40 publications

Predictive information in a sensory population

Predictive information in a sensory population

Retinal Representation of the Elementary Visual Signal

The spatial structure of a nonlinear receptive field

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