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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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Neuronal architectures for implementing the tWTA readout for a two-alternative task.The figure describes in a qualitative manner neuronal architectures that can implement the tWTA readout in the context of a two-alternative task. The inputs from population A and population B represent the two alternatives. Both architectures rely on reciprocal inhibition for implementing a ‘race to threshold’ competition but they differ in the implementation of the gating mechanism (see Discussion for details). (A) Implementation of the gating mechanism using NMDA synapses. (B) Implementation of the gating mechanism using disinhibition.
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pcbi-1002536-g010: Neuronal architectures for implementing the tWTA readout for a two-alternative task.The figure describes in a qualitative manner neuronal architectures that can implement the tWTA readout in the context of a two-alternative task. The inputs from population A and population B represent the two alternatives. Both architectures rely on reciprocal inhibition for implementing a ‘race to threshold’ competition but they differ in the implementation of the gating mechanism (see Discussion for details). (A) Implementation of the gating mechanism using NMDA synapses. (B) Implementation of the gating mechanism using disinhibition.

Mentions: As noted above, the implementation of the n-tWTA readout requires an integration process and a threshold decision mechanism. In this sense, n-tWTA competition is very similar to the ‘race to threshold’ mechanism suggested by Mazurek et al [40], in which the decision in a two alternative task is determined by integrating ‘evidence’ (spikes) for the two competing alternatives to reach a decision threshold (n spikes). The decision mechanism involves a winner-take-all type competition, which is an algorithm that others have also used to decode neural response [41]–[43]. Winner-take-all competition can be implemented using reciprocal inhibition between the integrators that represent the different alternatives [44], [45] (Figure 10). Each inhibitory neuron accumulates evidence for the corresponding alternative and fires when it crosses a threshold. Higher threshold values reflect a stricter decision criterion and correspond to higher values of n in the n-tWTA readout. The integration time constant of the neurons should be on the order of the relevant time scale for decisions (∼10–30 ms).


Fast coding of orientation in primary visual cortex.

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

Neuronal architectures for implementing the tWTA readout for a two-alternative task.The figure describes in a qualitative manner neuronal architectures that can implement the tWTA readout in the context of a two-alternative task. The inputs from population A and population B represent the two alternatives. Both architectures rely on reciprocal inhibition for implementing a ‘race to threshold’ competition but they differ in the implementation of the gating mechanism (see Discussion for details). (A) Implementation of the gating mechanism using NMDA synapses. (B) Implementation of the gating mechanism using disinhibition.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3375217&req=5

pcbi-1002536-g010: Neuronal architectures for implementing the tWTA readout for a two-alternative task.The figure describes in a qualitative manner neuronal architectures that can implement the tWTA readout in the context of a two-alternative task. The inputs from population A and population B represent the two alternatives. Both architectures rely on reciprocal inhibition for implementing a ‘race to threshold’ competition but they differ in the implementation of the gating mechanism (see Discussion for details). (A) Implementation of the gating mechanism using NMDA synapses. (B) Implementation of the gating mechanism using disinhibition.
Mentions: As noted above, the implementation of the n-tWTA readout requires an integration process and a threshold decision mechanism. In this sense, n-tWTA competition is very similar to the ‘race to threshold’ mechanism suggested by Mazurek et al [40], in which the decision in a two alternative task is determined by integrating ‘evidence’ (spikes) for the two competing alternatives to reach a decision threshold (n spikes). The decision mechanism involves a winner-take-all type competition, which is an algorithm that others have also used to decode neural response [41]–[43]. Winner-take-all competition can be implemented using reciprocal inhibition between the integrators that represent the different alternatives [44], [45] (Figure 10). Each inhibitory neuron accumulates evidence for the corresponding alternative and fires when it crosses a threshold. Higher threshold values reflect a stricter decision criterion and correspond to higher values of n in the n-tWTA readout. The integration time constant of the neurons should be on the order of the relevant time scale for decisions (∼10–30 ms).

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