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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 tuning of spike latencies.(A) Raster plot for of a sample cell in the data (taken from dataset 1 in Table 1). For each orientation, 100 randomly chosen trials (out of 400) are shown. For clarity, only the first 120 ms after stimulus onset are shown. Stimulus duration was 400 ms. (B) Cumulative distribution functions of first, second and third spike latencies (n denotes the spike number) for the same neuron. Each row corresponds to a different stimulus orientation and the gray levels represent the probability of the spike occurring before the time indicated on the abscissa. (C) Tuning curves of first, second and third spike latencies, computed as level curves of the corresponding cumulative distributions at 0.5. Cosine fits are shown as solid lines and are also shown as dashed lines in (A). (Error bars were calculated according to the method described in Materials and Methods, but are often smaller than the marker size). (D) Rate tuning curve for the same cell over the entire stimulus duration (black circles) and a fitted von-Mises function (solid line). (Error bars were calculated using the standard error of the mean, but are smaller than the marker size).
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pcbi-1002536-g001: Orientation tuning of spike latencies.(A) Raster plot for of a sample cell in the data (taken from dataset 1 in Table 1). For each orientation, 100 randomly chosen trials (out of 400) are shown. For clarity, only the first 120 ms after stimulus onset are shown. Stimulus duration was 400 ms. (B) Cumulative distribution functions of first, second and third spike latencies (n denotes the spike number) for the same neuron. Each row corresponds to a different stimulus orientation and the gray levels represent the probability of the spike occurring before the time indicated on the abscissa. (C) Tuning curves of first, second and third spike latencies, computed as level curves of the corresponding cumulative distributions at 0.5. Cosine fits are shown as solid lines and are also shown as dashed lines in (A). (Error bars were calculated according to the method described in Materials and Methods, but are often smaller than the marker size). (D) Rate tuning curve for the same cell over the entire stimulus duration (black circles) and a fitted von-Mises function (solid line). (Error bars were calculated using the standard error of the mean, but are smaller than the marker size).

Mentions: We first investigated the tuning of first spike times to stimulus orientation. Figure 1A presents eight raster plots showing the response of the same V1 neuron to eight different orientations of the visual stimulus. Qualitatively, both response strength and response latency seem tuned to the stimulus. Measuring latency by simply calculating the mean time to the first spike is problematic because stimuli that evoke weak responses may result in no spikes on some trials. A more principled approach is to incorporate both response time and probability of firing by computing the probability density function and the corresponding cumulative distribution function of the first spike latency.


Fast coding of orientation in primary visual cortex.

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

Orientation tuning of spike latencies.(A) Raster plot for of a sample cell in the data (taken from dataset 1 in Table 1). For each orientation, 100 randomly chosen trials (out of 400) are shown. For clarity, only the first 120 ms after stimulus onset are shown. Stimulus duration was 400 ms. (B) Cumulative distribution functions of first, second and third spike latencies (n denotes the spike number) for the same neuron. Each row corresponds to a different stimulus orientation and the gray levels represent the probability of the spike occurring before the time indicated on the abscissa. (C) Tuning curves of first, second and third spike latencies, computed as level curves of the corresponding cumulative distributions at 0.5. Cosine fits are shown as solid lines and are also shown as dashed lines in (A). (Error bars were calculated according to the method described in Materials and Methods, but are often smaller than the marker size). (D) Rate tuning curve for the same cell over the entire stimulus duration (black circles) and a fitted von-Mises function (solid line). (Error bars were calculated using the standard error of the mean, but are smaller than the marker size).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3375217&req=5

pcbi-1002536-g001: Orientation tuning of spike latencies.(A) Raster plot for of a sample cell in the data (taken from dataset 1 in Table 1). For each orientation, 100 randomly chosen trials (out of 400) are shown. For clarity, only the first 120 ms after stimulus onset are shown. Stimulus duration was 400 ms. (B) Cumulative distribution functions of first, second and third spike latencies (n denotes the spike number) for the same neuron. Each row corresponds to a different stimulus orientation and the gray levels represent the probability of the spike occurring before the time indicated on the abscissa. (C) Tuning curves of first, second and third spike latencies, computed as level curves of the corresponding cumulative distributions at 0.5. Cosine fits are shown as solid lines and are also shown as dashed lines in (A). (Error bars were calculated according to the method described in Materials and Methods, but are often smaller than the marker size). (D) Rate tuning curve for the same cell over the entire stimulus duration (black circles) and a fitted von-Mises function (solid line). (Error bars were calculated using the standard error of the mean, but are smaller than the marker size).
Mentions: We first investigated the tuning of first spike times to stimulus orientation. Figure 1A presents eight raster plots showing the response of the same V1 neuron to eight different orientations of the visual stimulus. Qualitatively, both response strength and response latency seem tuned to the stimulus. Measuring latency by simply calculating the mean time to the first spike is problematic because stimuli that evoke weak responses may result in no spikes on some trials. A more principled approach is to incorporate both response time and probability of firing by computing the probability density function and the corresponding cumulative distribution function of the first spike latency.

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