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Dual roles for spike signaling in cortical neural populations.

Ballard DH, Jehee JF - Front Comput Neurosci (2011)

Bottom Line: Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times.This model exhibits standard features characteristic of Poisson models such as orientation tuning and exponential interval histograms.In addition, it makes testable predictions that follow from the γ latency coding.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Texas at Austin Austin, TX, USA.

ABSTRACT
A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning and exponential interval histograms. In addition, it makes testable predictions that follow from the γ latency coding.

No MeSH data available.


Related in: MedlinePlus

γ latency code. The action potential of a neuron encodes an analog value in terms of a latency(black curve) that is scaled with respect to the phase of a particular γ frequency (gray curve). The model assumes that several different subnetworks can be simultaneously active and that they are each distinguished by using a frequency in the γ range.
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Figure 2: γ latency code. The action potential of a neuron encodes an analog value in terms of a latency(black curve) that is scaled with respect to the phase of a particular γ frequency (gray curve). The model assumes that several different subnetworks can be simultaneously active and that they are each distinguished by using a frequency in the γ range.

Mentions: A general way that all cells can signal coefficient information is via γ latencies, as shown in Figure 2. Each spike communicates numerical information by using relative timing (Kirchner and Thorpe, 2006; Gollisch and Meister, 2008) where in a wave of spikes the earlier spikes represent higher values. This strategy can be used in general circuitry, including feedback circuitry, if such waves are referenced to the γ oscillatory signal (Fries et al., 2007). Spikes coincident with zero phase in the γ signal can signal large numbers and spikes lagging by a few milliseconds can signal small numbers. The particular formula we use to relate the projection r to latency l is given by


Dual roles for spike signaling in cortical neural populations.

Ballard DH, Jehee JF - Front Comput Neurosci (2011)

γ latency code. The action potential of a neuron encodes an analog value in terms of a latency(black curve) that is scaled with respect to the phase of a particular γ frequency (gray curve). The model assumes that several different subnetworks can be simultaneously active and that they are each distinguished by using a frequency in the γ range.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3108387&req=5

Figure 2: γ latency code. The action potential of a neuron encodes an analog value in terms of a latency(black curve) that is scaled with respect to the phase of a particular γ frequency (gray curve). The model assumes that several different subnetworks can be simultaneously active and that they are each distinguished by using a frequency in the γ range.
Mentions: A general way that all cells can signal coefficient information is via γ latencies, as shown in Figure 2. Each spike communicates numerical information by using relative timing (Kirchner and Thorpe, 2006; Gollisch and Meister, 2008) where in a wave of spikes the earlier spikes represent higher values. This strategy can be used in general circuitry, including feedback circuitry, if such waves are referenced to the γ oscillatory signal (Fries et al., 2007). Spikes coincident with zero phase in the γ signal can signal large numbers and spikes lagging by a few milliseconds can signal small numbers. The particular formula we use to relate the projection r to latency l is given by

Bottom Line: Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times.This model exhibits standard features characteristic of Poisson models such as orientation tuning and exponential interval histograms.In addition, it makes testable predictions that follow from the γ latency coding.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Texas at Austin Austin, TX, USA.

ABSTRACT
A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning and exponential interval histograms. In addition, it makes testable predictions that follow from the γ latency coding.

No MeSH data available.


Related in: MedlinePlus