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Fast coding of orientation in primary visual cortex.

Shriki O, Kohn A, Shamir M - PLoS Comput. Biol. (2012)

Bottom Line: To extract this information, stimulus onset must be estimated accurately.We show that the responses of cells with weak tuning of spike latency can provide a reliable onset detector.Our results provide a novel mechanism for extracting information from neuronal populations over the very brief time scales in which behavioral judgments must sometimes be made.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology and Neurobiology, Ben-Gurion University of the Negev, Be'er-Sheva, Israel. shrikio@mail.nih.gov

ABSTRACT
Understanding how populations of neurons encode sensory information is a major goal of systems neuroscience. Attempts to answer this question have focused on responses measured over several hundred milliseconds, a duration much longer than that frequently used by animals to make decisions about the environment. How reliably sensory information is encoded on briefer time scales, and how best to extract this information, is unknown. Although it has been proposed that neuronal response latency provides a major cue for fast decisions in the visual system, this hypothesis has not been tested systematically and in a quantitative manner. Here we use a simple 'race to threshold' readout mechanism to quantify the information content of spike time latency of primary visual (V1) cortical cells to stimulus orientation. We find that many V1 cells show pronounced tuning of their spike latency to stimulus orientation and that almost as much information can be extracted from spike latencies as from firing rates measured over much longer durations. To extract this information, stimulus onset must be estimated accurately. We show that the responses of cells with weak tuning of spike latency can provide a reliable onset detector. We find that spike latency information can be pooled from a large neuronal population, provided that the decision threshold is scaled linearly with the population size, yielding a processing time of the order of a few tens of milliseconds. Our results provide a novel mechanism for extracting information from neuronal populations over the very brief time scales in which behavioral judgments must sometimes be made.

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Orientation discrimination using the n-tWTA readout in populations of neurons.(A–C) Probability of correct discrimination (Pc) as a function of population size (N) for two populations that differ in preferred orientation by 45° (A), 67.5° (B) and 80° (C). Different curves correspond to different values of n (see legend). (D–F) Probability of correct discrimination using the optimal value of n for each N (for the above pairs of populations). The inset shows the optimal n for each N. (G–I) Mean decision times relative to the onset signal for the neurometric curves in the top panels. (Decision times larger than 200 ms are not shown. Error bars represent ± standard error of the mean). The black circles mark the decision times when n = N; i.e., when the number of spikes used for the decision is equal to the group size. Note that the data for the left two columns are from dataset 5 in Table 1 whereas the data for the right column are from dataset 3. These datasets had different levels of spontaneous and evoked firing, which are responsible for the differences in the optimal n and in the decision times.
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pcbi-1002536-g007: Orientation discrimination using the n-tWTA readout in populations of neurons.(A–C) Probability of correct discrimination (Pc) as a function of population size (N) for two populations that differ in preferred orientation by 45° (A), 67.5° (B) and 80° (C). Different curves correspond to different values of n (see legend). (D–F) Probability of correct discrimination using the optimal value of n for each N (for the above pairs of populations). The inset shows the optimal n for each N. (G–I) Mean decision times relative to the onset signal for the neurometric curves in the top panels. (Decision times larger than 200 ms are not shown. Error bars represent ± standard error of the mean). The black circles mark the decision times when n = N; i.e., when the number of spikes used for the decision is equal to the group size. Note that the data for the left two columns are from dataset 5 in Table 1 whereas the data for the right column are from dataset 3. These datasets had different levels of spontaneous and evoked firing, which are responsible for the differences in the optimal n and in the decision times.

Mentions: Figures 7A, B and C show the n-tWTA probability of correct discrimination for three representative pairs of columns as a function of the number of cells in each column, N. The pairs of columns differ in terms of the difference between the preferred orientations, . The blue curve depicts the performance of the naïve tWTA (n = 1) readout. Initially, for small N, tWTA performance increases with N. However, beyond a critical size of , tWTA performance saturates. Theory has shown that two factors may limit tWTA performance. The first is correlations in the first spike latencies of different cells and the second is the spontaneous firing of the cells [34]. We find that although first spike latency is correlated (Figure S3), its effect on tWTA accuracy is negligible (Figure S4; Text S1). The dominant factor that limits accumulation of information from large populations is the spontaneous firing. Clearly, adding more cells also results in adding more spontaneous spikes which interfere with informative spikes (see [34] for a detailed analysis). This effect can be reduced by increasing the decision threshold criterion; i.e., by increasing n.


Fast coding of orientation in primary visual cortex.

Shriki O, Kohn A, Shamir M - PLoS Comput. Biol. (2012)

Orientation discrimination using the n-tWTA readout in populations of neurons.(A–C) Probability of correct discrimination (Pc) as a function of population size (N) for two populations that differ in preferred orientation by 45° (A), 67.5° (B) and 80° (C). Different curves correspond to different values of n (see legend). (D–F) Probability of correct discrimination using the optimal value of n for each N (for the above pairs of populations). The inset shows the optimal n for each N. (G–I) Mean decision times relative to the onset signal for the neurometric curves in the top panels. (Decision times larger than 200 ms are not shown. Error bars represent ± standard error of the mean). The black circles mark the decision times when n = N; i.e., when the number of spikes used for the decision is equal to the group size. Note that the data for the left two columns are from dataset 5 in Table 1 whereas the data for the right column are from dataset 3. These datasets had different levels of spontaneous and evoked firing, which are responsible for the differences in the optimal n and in the decision times.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3375217&req=5

pcbi-1002536-g007: Orientation discrimination using the n-tWTA readout in populations of neurons.(A–C) Probability of correct discrimination (Pc) as a function of population size (N) for two populations that differ in preferred orientation by 45° (A), 67.5° (B) and 80° (C). Different curves correspond to different values of n (see legend). (D–F) Probability of correct discrimination using the optimal value of n for each N (for the above pairs of populations). The inset shows the optimal n for each N. (G–I) Mean decision times relative to the onset signal for the neurometric curves in the top panels. (Decision times larger than 200 ms are not shown. Error bars represent ± standard error of the mean). The black circles mark the decision times when n = N; i.e., when the number of spikes used for the decision is equal to the group size. Note that the data for the left two columns are from dataset 5 in Table 1 whereas the data for the right column are from dataset 3. These datasets had different levels of spontaneous and evoked firing, which are responsible for the differences in the optimal n and in the decision times.
Mentions: Figures 7A, B and C show the n-tWTA probability of correct discrimination for three representative pairs of columns as a function of the number of cells in each column, N. The pairs of columns differ in terms of the difference between the preferred orientations, . The blue curve depicts the performance of the naïve tWTA (n = 1) readout. Initially, for small N, tWTA performance increases with N. However, beyond a critical size of , tWTA performance saturates. Theory has shown that two factors may limit tWTA performance. The first is correlations in the first spike latencies of different cells and the second is the spontaneous firing of the cells [34]. We find that although first spike latency is correlated (Figure S3), its effect on tWTA accuracy is negligible (Figure S4; Text S1). The dominant factor that limits accumulation of information from large populations is the spontaneous firing. Clearly, adding more cells also results in adding more spontaneous spikes which interfere with informative spikes (see [34] for a detailed analysis). This effect can be reduced by increasing the decision threshold criterion; i.e., by increasing n.

Bottom Line: To extract this information, stimulus onset must be estimated accurately.We show that the responses of cells with weak tuning of spike latency can provide a reliable onset detector.Our results provide a novel mechanism for extracting information from neuronal populations over the very brief time scales in which behavioral judgments must sometimes be made.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology and Neurobiology, Ben-Gurion University of the Negev, Be'er-Sheva, Israel. shrikio@mail.nih.gov

ABSTRACT
Understanding how populations of neurons encode sensory information is a major goal of systems neuroscience. Attempts to answer this question have focused on responses measured over several hundred milliseconds, a duration much longer than that frequently used by animals to make decisions about the environment. How reliably sensory information is encoded on briefer time scales, and how best to extract this information, is unknown. Although it has been proposed that neuronal response latency provides a major cue for fast decisions in the visual system, this hypothesis has not been tested systematically and in a quantitative manner. Here we use a simple 'race to threshold' readout mechanism to quantify the information content of spike time latency of primary visual (V1) cortical cells to stimulus orientation. We find that many V1 cells show pronounced tuning of their spike latency to stimulus orientation and that almost as much information can be extracted from spike latencies as from firing rates measured over much longer durations. To extract this information, stimulus onset must be estimated accurately. We show that the responses of cells with weak tuning of spike latency can provide a reliable onset detector. We find that spike latency information can be pooled from a large neuronal population, provided that the decision threshold is scaled linearly with the population size, yielding a processing time of the order of a few tens of milliseconds. Our results provide a novel mechanism for extracting information from neuronal populations over the very brief time scales in which behavioral judgments must sometimes be made.

Show MeSH
Related in: MedlinePlus