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Featured ArticleArticles, Behavioral/Systems/Cognitive

The Structure of Multi-Neuron Firing Patterns in Primate Retina

Jonathon Shlens, Greg D. Field, Jeffrey L. Gauthier, Matthew I. Grivich, Dumitru Petrusca, Alexander Sher, Alan M. Litke and E. J. Chichilnisky
Journal of Neuroscience 9 August 2006, 26 (32) 8254-8266; https://doi.org/10.1523/JNEUROSCI.1282-06.2006
Jonathon Shlens
1Department of Systems Neurobiology, The Salk Institute, La Jolla, California 92037, and 2Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
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Greg D. Field
1Department of Systems Neurobiology, The Salk Institute, La Jolla, California 92037, and 2Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
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Jeffrey L. Gauthier
1Department of Systems Neurobiology, The Salk Institute, La Jolla, California 92037, and 2Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
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Matthew I. Grivich
1Department of Systems Neurobiology, The Salk Institute, La Jolla, California 92037, and 2Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
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Dumitru Petrusca
1Department of Systems Neurobiology, The Salk Institute, La Jolla, California 92037, and 2Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
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Alexander Sher
1Department of Systems Neurobiology, The Salk Institute, La Jolla, California 92037, and 2Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
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Alan M. Litke
1Department of Systems Neurobiology, The Salk Institute, La Jolla, California 92037, and 2Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
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E. J. Chichilnisky
1Department of Systems Neurobiology, The Salk Institute, La Jolla, California 92037, and 2Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California 95064
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  • Figure 1.
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    Figure 1.

    Patterns of connectivity. A, A small random sample of the possible input patterns to a collection of n = 8 cells. From top to bottom, Seven different hypothetical input patterns are shown, each terminating on a different collection of target cells (circles). In general, the total number of distinct patterns is on the order of 2n. B, All distinct pairwise patterns, superimposed. Each color shows, superimposed, all pairwise patterns across cells separated by a particular distance. An example of a single pairwise pattern from A is indicated with an arrow. In general, the number of distinct pairwise patterns is on the order of n2. In this simplified diagram, the 28 patterns shown exclude wraparound and symmetric patterns. C, All distinct adjacent patterns, superimposed. Each color shows, superimposed, all adjacent patterns consisting of a particular number of cells. An example of one adjacent pattern from A is indicated with an arrow. Again, 28 distinct patterns are shown. In general, the number of distinct patterns is on the order of n2. D, All possible patterns that are both pairwise and adjacent, superimposed. Seven patterns are shown, and a single example is indicated with an arrow. In general, the number of possible patterns is on the order of n.

  • Figure 2.
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    Figure 2.

    Pairwise synchrony. A, Receptive fields of 118 ON and 175 OFF parasol RGCs simultaneously recorded in one retina. Ellipses represent 1 SD of the Gaussian fit to the spatial profile of the receptive field. Rectangles represent outline of electrode array. Scale bar, 200 μm. B, Sample cross-correlation functions for pairs of ON and OFF parasol cells shown shaded in black in A. The firing rate of one cell is shown as a function of time relative to the time of a spike in the second cell. The synchrony index for these pairs is S = 1.57 and 0.59, respectively. C, Synchrony index S as a function of distance between receptive fields of ON and OFF parasol cells. For comparison, the black bar near the origin represents the modal separation between cells in the mosaic (see Materials and Methods).

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    Figure 3.

    Triplet synchrony. A, Cross-correlation functions for each pair from a group of three adjacent ON parasol cells (labeled A, B, C in Fig. 2A). Each cell pair exhibits strong synchrony (S = 1.90, 2.05, and 1.64 from left to right). B, Three-dimensional cross-correlation for triplet synchrony (left) and maximum entropy pairwise prediction (right). Note that maximum entropy predictions computed for different time offsets are not statistically independent.

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    Figure 4.

    Pattern index for multi-neuron firing. A, Pattern index Q (Eq. 6) for firing pattern (1, 1, 1) in groups of three cells as a function of the geometric mean of the distances between each pair in the group. Black symbols, Pattern index using statistical independence as the null model. Red symbols, Pattern index using pairwise constrained maximum entropy as the null model. B, Pattern index for all eight firing patterns in groups of three cells. Symbol colors as in A. C, Pattern index for groups of six cells. Red and black symbols as in A. Blue symbols, Pattern index obtained using the pairwise–adjacent maximum entropy model. For the latter, analysis was restricted to local groups of cells conforming to selection criteria described in Materials and Methods and containing at least one non-adjacent cell pair.

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    Figure 5.

    Sample firing pattern distributions. A–C show firing pattern distributions for three ON parasol cells. Each distribution is shown as a histogram, with the firing pattern indicated by binary digits on the abscissa representing a spike or no spike in each cell, and the probability indicated on the ordinate (note logarithmic scale). Firing pattern distributions were obtained from raw data (A), the pairwise model (B), and the statistically independent model (C) (the latter obtained by multiplying the marginal probability distributions for each cell; see Eq. 5). The likelihood of the data obtained from the pairwise model was 0.99944, from the independent model was 0.94959, and from the empirical model was 0.99955. Distributions in B and C were fitted to interleaved recordings from the same cells, distinct from the data in A (data not shown). D–F, Same as A–C but for five ON parasol cells. The likelihood of the data obtained from the pairwise model was 0.99728, from the independent model was 0.92107, and from the empirical model was 0.99836.

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    Figure 6.

    Likelihood test of pairwise and adjacent models under constant, spatially uniform illumination. A, Likelihood of observed data under an assumption of statistical independence as a function of likelihood obtained from an empirical model based on a repeated measurement, for groups of three, four, five, six, and seven cells. Diagonal gray line near top indicates equality (note different scales on abscissa and ordinate); large departures from this line indicate substantial failures of statistical independence. B, Likelihood of observed data in the pairwise maximum entropy model as a function of empirical likelihood. Symbol colors and equality line same as in A. C, Likelihood of observed data in the pairwise–adjacent maximum entropy model as a function of empirical likelihood. Symbol colors and equality line same as in A. All likelihood analysis was restricted to local groups of cells conforming to selection criteria described in Materials and Methods. For C, analysis was further restricted to groups that included at least one nonadjacent cell pair.

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    Figure 7.

    Likelihood test of pairwise and adjacent models with a fine-grained random visual stimulus. Panels and symbols as in Figure 6.

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    Figure 8.

    Predicted and observed pairwise synchrony index. Left panels show the synchrony index (Eq. 4) in pairs of parasol cells as a function of distance between their receptive fields (see Fig. 2). Black points indicate the observed synchrony index, and red points indicate the predictions obtained from the maximum entropy pairwise–adjacent model fitted to groups of n = 7 cells. The black bar near the origin represents the modal separation between cells in the mosaic. Right panels show the comparison between data and model predictions for each cell pair tested. In all panels, large open symbols represent cell pairs that are adjacent in the mosaic; small symbols represent cell pairs that are not.

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    Figure 9.

    Sensitivity of maximum entropy analysis. Each panel shows the sensitivity of the maximum entropy analysis procedure for detecting a hypothetical non-pairwise, non-adjacent common input. The hypothetical input occurs at a rate r and causes a spike in each of n = 7 RGCs with a probability p. Results are shown for common input over a range of values of r and p added to simulations obtained with either the pairwise (A) or pairwise–adjacent (B) model fitted to data. In each case, the abscissa indicates the average evoked rate in the simulated RGCs, which is determined by r and p. The ordinate indicates the fraction of the departures from statistical independence accounted for by the pairwise or pairwise–adjacent model. Each gray trace shows the results obtained from a single group of ON parasol cells; red traces indicate the average across all 20 groups. In each panel, the rate of common input r required to reproduce the average value observed in the original data was converted to the equivalent evoked rate and is indicated by a dashed line.

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    Figure 10.

    Sensitivity of maximum entropy analysis for detecting non-adjacent interactions. Each panel shows the fraction of departures from independence accounted for by the pairwise analysis restricted to a random subset of cell pairs, equal in number to the number of adjacent cell pairs, as a function of the fraction accounted for by the original pairwise–adjacent analysis. Each point represents the results for a single groups of n = 7 ON or OFF parasol cells. Dashed lines indicate equality.

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    Table 1.

    Accuracy of pairwise and pairwise–adjacent models

    ConditionCell typeΔtPairwisePairwise–adjacentEmpirical
    Constant illuminationON parasol10 ms98.6 ± 0.5%98.3 ± 1.0%99.8 ± 0.1%
    5 ms97.8 ± 0.5%97.5 ± 1.0%99.8 ± 0.1%
    20 ms99.2 ± 0.4%98.9 ± 0.9%99.7 ± 0.1%
    OFF parasol10 ms98.9 ± 0.3%98.2 ± 1.3%99.4 ± 0.4%
    5 ms98.5 ± 0.4%97.4 ± 1.5%99.0 ± 0.5%
    20 ms99.2 ± 0.3%98.6 ± 1.3%99.4 ± 0.3%
    White-noise stimulusON parasol10 ms98.6 ± 0.4%98.4 ± 0.9%99.6 ± 0.2%
    5 ms97.8 ± 0.5%97.5 ± 1.0%99.6 ± 0.2%
    20 ms99.1 ± 0.3%98.9 ± 0.9%99.5 ± 0.2%
    OFF parasol10 ms98.6 ± 0.5%98.3 ± 0.9%98.8 ± 0.7%
    5 ms98.2 ± 0.8%98.0 ± 1.3%98.4 ± 1.0%
    20 ms98.6 ± 0.7%98.3 ± 1.0%98.6 ± 0.9%
    • Each numerical entry indicates the percentage of departures from statistical independence captured by a specific model, for a given stimulus condition, cell type tested, and time bin size (Δ t). Results cited in text were obtained with Δ t = 10 ms. The range indicates the mean ± 1 SD across several hundred cell groups tested, each consisting of three to seven cells (in the case of the pairwise model) or four to seven cells (in the case of the pairwise–adjacent model). For each model, the quantities shown are as follows: pairwise model, (Dind − Dpair)/Dind; pairwise–adjacent model, (Dind − Dadj)/Dind; empirical model, (Dind − Demp)/Dind.

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The Journal of Neuroscience: 26 (32)
Journal of Neuroscience
Vol. 26, Issue 32
9 Aug 2006
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The Structure of Multi-Neuron Firing Patterns in Primate Retina
Jonathon Shlens, Greg D. Field, Jeffrey L. Gauthier, Matthew I. Grivich, Dumitru Petrusca, Alexander Sher, Alan M. Litke, E. J. Chichilnisky
Journal of Neuroscience 9 August 2006, 26 (32) 8254-8266; DOI: 10.1523/JNEUROSCI.1282-06.2006

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The Structure of Multi-Neuron Firing Patterns in Primate Retina
Jonathon Shlens, Greg D. Field, Jeffrey L. Gauthier, Matthew I. Grivich, Dumitru Petrusca, Alexander Sher, Alan M. Litke, E. J. Chichilnisky
Journal of Neuroscience 9 August 2006, 26 (32) 8254-8266; DOI: 10.1523/JNEUROSCI.1282-06.2006
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Keywords

  • vision
  • information theory
  • correlated variability
  • neural coding
  • synchrony
  • retinal ganglion cell

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