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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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Additional examples of spike latency tuning.Each column, (A)–(C), corresponds to data from a different unit. First row: Raster plot for each of the 8 orientations. For each orientation, 100 randomly chosen trials are shown for 120 ms after stimulus onset. Second row: PSTH (Post Stimulus Time Histogram) for the same time window. Third row: Tuning curve of first spike latency. Cosine fit is shown as a solid line. Fourth row: Rate tuning curve (black circles) and a fitted von-Mises function (solid line). The cell in (A) is taken from dataset 1 in Table 1, in which stimulus duration was 400 ms and the number of trials was 400. The cells in (B) and (C) are taken from dataset 5 in Table 1, in which stimulus duration was 300 ms and the number of trials was 300. These are the same 3 cells as in Figure 5.
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pcbi-1002536-g002: Additional examples of spike latency tuning.Each column, (A)–(C), corresponds to data from a different unit. First row: Raster plot for each of the 8 orientations. For each orientation, 100 randomly chosen trials are shown for 120 ms after stimulus onset. Second row: PSTH (Post Stimulus Time Histogram) for the same time window. Third row: Tuning curve of first spike latency. Cosine fit is shown as a solid line. Fourth row: Rate tuning curve (black circles) and a fitted von-Mises function (solid line). The cell in (A) is taken from dataset 1 in Table 1, in which stimulus duration was 400 ms and the number of trials was 400. The cells in (B) and (C) are taken from dataset 5 in Table 1, in which stimulus duration was 300 ms and the number of trials was 300. These are the same 3 cells as in Figure 5.

Mentions: Figure 2 shows three additional examples of V1 responses in each column. Eight raster plots for eight orientations are depicted at the top row for each cell. The stimulus dependence of the temporal structure of neural response can be seen from the PSTHs at the second row. The latency tuning curve, in terms of 0.5 level curve of first spike time cumulative distribution, is shown on the third row, and the conventional rate tuning curve appears on the fourth row for comparison. Examining the PSTHs of each cell, one can see that response strength has a considerable contribution to first spike latency, in our definition. For example, in cell B it is mainly the firing rate that is tuned to stimulus orientation. Nevertheless, due to the high firing rate near the preferred orientation, the first spike times tend to be shorter near that orientation. It is also evident that the temporal structure of the response is tuned to the stimulus as well. The modulation of the entire temporal structure (and not a simple temporal shift) limits the ability to extract the ‘pure’ latency tuning. However, as mentioned above, it is the distribution of the nth spike time that governs the tWTA readout accuracy; hence, the definition of spike latency used here.


Fast coding of orientation in primary visual cortex.

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

Additional examples of spike latency tuning.Each column, (A)–(C), corresponds to data from a different unit. First row: Raster plot for each of the 8 orientations. For each orientation, 100 randomly chosen trials are shown for 120 ms after stimulus onset. Second row: PSTH (Post Stimulus Time Histogram) for the same time window. Third row: Tuning curve of first spike latency. Cosine fit is shown as a solid line. Fourth row: Rate tuning curve (black circles) and a fitted von-Mises function (solid line). The cell in (A) is taken from dataset 1 in Table 1, in which stimulus duration was 400 ms and the number of trials was 400. The cells in (B) and (C) are taken from dataset 5 in Table 1, in which stimulus duration was 300 ms and the number of trials was 300. These are the same 3 cells as in Figure 5.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3375217&req=5

pcbi-1002536-g002: Additional examples of spike latency tuning.Each column, (A)–(C), corresponds to data from a different unit. First row: Raster plot for each of the 8 orientations. For each orientation, 100 randomly chosen trials are shown for 120 ms after stimulus onset. Second row: PSTH (Post Stimulus Time Histogram) for the same time window. Third row: Tuning curve of first spike latency. Cosine fit is shown as a solid line. Fourth row: Rate tuning curve (black circles) and a fitted von-Mises function (solid line). The cell in (A) is taken from dataset 1 in Table 1, in which stimulus duration was 400 ms and the number of trials was 400. The cells in (B) and (C) are taken from dataset 5 in Table 1, in which stimulus duration was 300 ms and the number of trials was 300. These are the same 3 cells as in Figure 5.
Mentions: Figure 2 shows three additional examples of V1 responses in each column. Eight raster plots for eight orientations are depicted at the top row for each cell. The stimulus dependence of the temporal structure of neural response can be seen from the PSTHs at the second row. The latency tuning curve, in terms of 0.5 level curve of first spike time cumulative distribution, is shown on the third row, and the conventional rate tuning curve appears on the fourth row for comparison. Examining the PSTHs of each cell, one can see that response strength has a considerable contribution to first spike latency, in our definition. For example, in cell B it is mainly the firing rate that is tuned to stimulus orientation. Nevertheless, due to the high firing rate near the preferred orientation, the first spike times tend to be shorter near that orientation. It is also evident that the temporal structure of the response is tuned to the stimulus as well. The modulation of the entire temporal structure (and not a simple temporal shift) limits the ability to extract the ‘pure’ latency tuning. However, as mentioned above, it is the distribution of the nth spike time that governs the tWTA readout accuracy; hence, the definition of spike latency used here.

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