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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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Statistics of orientation discrimination using single cell spike latencies and firing rates.(A)–(B) Pc (probability of correct discrimination) using first spike latency vs. Pc using the spike count of the entire response. Each point corresponds to a single cell. The identity line is shown for comparison (solid black line). (A) Comparison of performance at a fine resolution discrimination task, Δθ = 22.5°. (B) Comparison of performance at a coarse resolution discrimination task, Δθ = 90°. (C) Proportion of cells above a given performance level. The dashed curves correspond to a 22.5° discrimination task and the solid curves to a 90° discrimination task. Different curves correspond to first spike latency (red), second spike latency (green), third spike latency (blue) and firing rate from the entire response (black). (D) Comparison of latency and rate performance at a given decision time. The abscissa is the difference between Pc using the n'th spike latency and Pc of the conventional rate code readout, where the rate is estimated from the spike count in the time window from stimulus onset to the mean decision time using the n'th spike latency. These differences correspond to the vertical distances between the circles and the solid black curve in the right panels of Figure 5. The curves show the proportion of cells above a given difference. The color code is the same as in (C). The data for all panels are from the tuned cells (B>15 ms) in datasets 1, 2, 4 and 5 in Table 1 (244 cells).
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pcbi-1002536-g006: Statistics of orientation discrimination using single cell spike latencies and firing rates.(A)–(B) Pc (probability of correct discrimination) using first spike latency vs. Pc using the spike count of the entire response. Each point corresponds to a single cell. The identity line is shown for comparison (solid black line). (A) Comparison of performance at a fine resolution discrimination task, Δθ = 22.5°. (B) Comparison of performance at a coarse resolution discrimination task, Δθ = 90°. (C) Proportion of cells above a given performance level. The dashed curves correspond to a 22.5° discrimination task and the solid curves to a 90° discrimination task. Different curves correspond to first spike latency (red), second spike latency (green), third spike latency (blue) and firing rate from the entire response (black). (D) Comparison of latency and rate performance at a given decision time. The abscissa is the difference between Pc using the n'th spike latency and Pc of the conventional rate code readout, where the rate is estimated from the spike count in the time window from stimulus onset to the mean decision time using the n'th spike latency. These differences correspond to the vertical distances between the circles and the solid black curve in the right panels of Figure 5. The curves show the proportion of cells above a given difference. The color code is the same as in (C). The data for all panels are from the tuned cells (B>15 ms) in datasets 1, 2, 4 and 5 in Table 1 (244 cells).

Mentions: Figures 5A, C and E show the neurometric curves of 3 single cells. The red, green and blue curves correspond to the n-tWTA readout for n = 1, 2, and 3, respectively. For comparison, we show the neurometric curve of the conventional rate code readout in black (the firing rate was estimated from the total number of spikes fired by the cell during the entire response). Typically, as n increases, the performance improves and approaches that of the rate code. Figures 6A and B compare the accuracy of the first spike latency code, in terms of probability of correct discrimination, and the rate code, for a relatively fine discrimination task (Figure 6A; 22.5 deg) and for a coarse one (Figure 6B; 90 deg). Latency and rate code accuracy are correlated and, for the coarse discrimination task, the latency code performance is often comparable to that of the rate code. The cumulative distributions of the accuracy of the different codes in these two tasks are shown in Figure 6C.


Fast coding of orientation in primary visual cortex.

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

Statistics of orientation discrimination using single cell spike latencies and firing rates.(A)–(B) Pc (probability of correct discrimination) using first spike latency vs. Pc using the spike count of the entire response. Each point corresponds to a single cell. The identity line is shown for comparison (solid black line). (A) Comparison of performance at a fine resolution discrimination task, Δθ = 22.5°. (B) Comparison of performance at a coarse resolution discrimination task, Δθ = 90°. (C) Proportion of cells above a given performance level. The dashed curves correspond to a 22.5° discrimination task and the solid curves to a 90° discrimination task. Different curves correspond to first spike latency (red), second spike latency (green), third spike latency (blue) and firing rate from the entire response (black). (D) Comparison of latency and rate performance at a given decision time. The abscissa is the difference between Pc using the n'th spike latency and Pc of the conventional rate code readout, where the rate is estimated from the spike count in the time window from stimulus onset to the mean decision time using the n'th spike latency. These differences correspond to the vertical distances between the circles and the solid black curve in the right panels of Figure 5. The curves show the proportion of cells above a given difference. The color code is the same as in (C). The data for all panels are from the tuned cells (B>15 ms) in datasets 1, 2, 4 and 5 in Table 1 (244 cells).
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pcbi-1002536-g006: Statistics of orientation discrimination using single cell spike latencies and firing rates.(A)–(B) Pc (probability of correct discrimination) using first spike latency vs. Pc using the spike count of the entire response. Each point corresponds to a single cell. The identity line is shown for comparison (solid black line). (A) Comparison of performance at a fine resolution discrimination task, Δθ = 22.5°. (B) Comparison of performance at a coarse resolution discrimination task, Δθ = 90°. (C) Proportion of cells above a given performance level. The dashed curves correspond to a 22.5° discrimination task and the solid curves to a 90° discrimination task. Different curves correspond to first spike latency (red), second spike latency (green), third spike latency (blue) and firing rate from the entire response (black). (D) Comparison of latency and rate performance at a given decision time. The abscissa is the difference between Pc using the n'th spike latency and Pc of the conventional rate code readout, where the rate is estimated from the spike count in the time window from stimulus onset to the mean decision time using the n'th spike latency. These differences correspond to the vertical distances between the circles and the solid black curve in the right panels of Figure 5. The curves show the proportion of cells above a given difference. The color code is the same as in (C). The data for all panels are from the tuned cells (B>15 ms) in datasets 1, 2, 4 and 5 in Table 1 (244 cells).
Mentions: Figures 5A, C and E show the neurometric curves of 3 single cells. The red, green and blue curves correspond to the n-tWTA readout for n = 1, 2, and 3, respectively. For comparison, we show the neurometric curve of the conventional rate code readout in black (the firing rate was estimated from the total number of spikes fired by the cell during the entire response). Typically, as n increases, the performance improves and approaches that of the rate code. Figures 6A and B compare the accuracy of the first spike latency code, in terms of probability of correct discrimination, and the rate code, for a relatively fine discrimination task (Figure 6A; 22.5 deg) and for a coarse one (Figure 6B; 90 deg). Latency and rate code accuracy are correlated and, for the coarse discrimination task, the latency code performance is often comparable to that of the rate code. The cumulative distributions of the accuracy of the different codes in these two tasks are shown in Figure 6C.

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