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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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Discrimination among multiple alternatives using the n-tWTA in populations of neurons.(A) The tuned neurons in one of the datasets (dataset 3 in Table 1) were divided according to their preferred orientations into M groups of equal orientation width, Δθ = 180°/M. To illustrate this division, each point on the circle represents a neuron (the angle is twice the preferred orientation). The left plot illustrates division into M = 4 groups of width Δθ = 45° and the right plot illustrates division into M = 9 groups of width Δθ = 20°. Each group is labeled by the orientation of its center. The lengths of the blue bars are proportional to the number of neurons in each group. (B) Probability of correct discrimination of the n-tWTA as a function of group width. The different curves correspond to n = 1,2,3,4,5 and 20. (C–D) Distribution of errors for group width of Δθ = 1° for n = 1 (C) and n = 2 (D).
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pcbi-1002536-g009: Discrimination among multiple alternatives using the n-tWTA in populations of neurons.(A) The tuned neurons in one of the datasets (dataset 3 in Table 1) were divided according to their preferred orientations into M groups of equal orientation width, Δθ = 180°/M. To illustrate this division, each point on the circle represents a neuron (the angle is twice the preferred orientation). The left plot illustrates division into M = 4 groups of width Δθ = 45° and the right plot illustrates division into M = 9 groups of width Δθ = 20°. Each group is labeled by the orientation of its center. The lengths of the blue bars are proportional to the number of neurons in each group. (B) Probability of correct discrimination of the n-tWTA as a function of group width. The different curves correspond to n = 1,2,3,4,5 and 20. (C–D) Distribution of errors for group width of Δθ = 1° for n = 1 (C) and n = 2 (D).

Mentions: We next studied the issue of tWTA accuracy in a multiple (M)-alternative-forced-choice task using the following setting. All the tuned neurons (B>15 ms) in each dataset were divided into M ‘columns’ according to their preferred orientation, as depicted in Figure 9A (see Materials and Methods). Note that the number of cells in different groups is not identical and that dividing them into many groups may result in some that contain no cells. The orientation label of each column was defined as the center of that column. The n-tWTA decision in a competition among M columns was defined as the orientation label of the first column to reach a threshold of n spikes. The resolution of this decision is inversely related to the number of alternatives, . Figure 9b shows the probability of correct discrimination of the n-tWTA as a function of in one of the datasets. Different curves correspond to different values of n. The dashed line represents chance value, which is inversely proportional to the number of alternatives. As the decision threshold, n, is increased, n-tWTA performance improves. This improvement is more significant for coarse discrimination tasks; i.e., for large.


Fast coding of orientation in primary visual cortex.

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

Discrimination among multiple alternatives using the n-tWTA in populations of neurons.(A) The tuned neurons in one of the datasets (dataset 3 in Table 1) were divided according to their preferred orientations into M groups of equal orientation width, Δθ = 180°/M. To illustrate this division, each point on the circle represents a neuron (the angle is twice the preferred orientation). The left plot illustrates division into M = 4 groups of width Δθ = 45° and the right plot illustrates division into M = 9 groups of width Δθ = 20°. Each group is labeled by the orientation of its center. The lengths of the blue bars are proportional to the number of neurons in each group. (B) Probability of correct discrimination of the n-tWTA as a function of group width. The different curves correspond to n = 1,2,3,4,5 and 20. (C–D) Distribution of errors for group width of Δθ = 1° for n = 1 (C) and n = 2 (D).
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getmorefigures.php?uid=PMC3375217&req=5

pcbi-1002536-g009: Discrimination among multiple alternatives using the n-tWTA in populations of neurons.(A) The tuned neurons in one of the datasets (dataset 3 in Table 1) were divided according to their preferred orientations into M groups of equal orientation width, Δθ = 180°/M. To illustrate this division, each point on the circle represents a neuron (the angle is twice the preferred orientation). The left plot illustrates division into M = 4 groups of width Δθ = 45° and the right plot illustrates division into M = 9 groups of width Δθ = 20°. Each group is labeled by the orientation of its center. The lengths of the blue bars are proportional to the number of neurons in each group. (B) Probability of correct discrimination of the n-tWTA as a function of group width. The different curves correspond to n = 1,2,3,4,5 and 20. (C–D) Distribution of errors for group width of Δθ = 1° for n = 1 (C) and n = 2 (D).
Mentions: We next studied the issue of tWTA accuracy in a multiple (M)-alternative-forced-choice task using the following setting. All the tuned neurons (B>15 ms) in each dataset were divided into M ‘columns’ according to their preferred orientation, as depicted in Figure 9A (see Materials and Methods). Note that the number of cells in different groups is not identical and that dividing them into many groups may result in some that contain no cells. The orientation label of each column was defined as the center of that column. The n-tWTA decision in a competition among M columns was defined as the orientation label of the first column to reach a threshold of n spikes. The resolution of this decision is inversely related to the number of alternatives, . Figure 9b shows the probability of correct discrimination of the n-tWTA as a function of in one of the datasets. Different curves correspond to different values of n. The dashed line represents chance value, which is inversely proportional to the number of alternatives. As the decision threshold, n, is increased, n-tWTA performance improves. This improvement is more significant for coarse discrimination tasks; i.e., for large.

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