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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

Spike interval histograms were computed for both the pure noise bath and the noise bath with the code embedded in it under two conditions. In one random routing was used to select the coefficients, and in the other the same coefficients were re-sent at each coding cycle. Next the cumulative density functions (cdfs) of these two cases (vertical axis) were plotted against the cdf for a pure noise bath of the same number of spikes (horizontal axis). Note that if two cdfs were identical the result would be a straight line. The γ timing signal clearly shows up using the cdf for the case where the same coefficients are used at every cycle (red curve) vs. the random cdf. In contrast, the random routing cdf vs. the random cdf tends to obscure the γ signal completely (black curve).
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Figure 6: Spike interval histograms were computed for both the pure noise bath and the noise bath with the code embedded in it under two conditions. In one random routing was used to select the coefficients, and in the other the same coefficients were re-sent at each coding cycle. Next the cumulative density functions (cdfs) of these two cases (vertical axis) were plotted against the cdf for a pure noise bath of the same number of spikes (horizontal axis). Note that if two cdfs were identical the result would be a straight line. The γ timing signal clearly shows up using the cdf for the case where the same coefficients are used at every cycle (red curve) vs. the random cdf. In contrast, the random routing cdf vs. the random cdf tends to obscure the γ signal completely (black curve).

Mentions: In the first test, we constructed a more complete spike data set by embedding those spikes in a “noise bath” of background neural spiking. The idea of noise here is as a model for any other ongoing processing. For each neuron, the probability of firing at each millisecond was set to 0.008, representing approximately 10 Hz background rate, and the model's spikes were added to this bath. For comparison, we compared these spike trains to a pure noise bath with firing probability of 0.01. The different background rates were chosen so that the number of spikes in each case was approximately the same. The results are shown in Figure 6. The black curve shows the cumulative distribution of the spike train for the spikes that use random routing plotted against the cumulative distribution for white noise spikes. By way of comparison, the red curve shows the distribution of spikes in a case where random routing is not used and one set of coefficients is simply repeated, plotted against the cumulative node distribution. It is easily seen that the random routing necessitated by the learning algorithm obscures the timing signal. With the random routing, no timing perturbation is noticeable even after 40 s of simulation. In contrast, without the random routing constraint, the effect of the γ latency code clearly shows up in the plot as a step change, representing the influence of the highly detectable 50 Hz signal.


Dual roles for spike signaling in cortical neural populations.

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

Spike interval histograms were computed for both the pure noise bath and the noise bath with the code embedded in it under two conditions. In one random routing was used to select the coefficients, and in the other the same coefficients were re-sent at each coding cycle. Next the cumulative density functions (cdfs) of these two cases (vertical axis) were plotted against the cdf for a pure noise bath of the same number of spikes (horizontal axis). Note that if two cdfs were identical the result would be a straight line. The γ timing signal clearly shows up using the cdf for the case where the same coefficients are used at every cycle (red curve) vs. the random cdf. In contrast, the random routing cdf vs. the random cdf tends to obscure the γ signal completely (black curve).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Spike interval histograms were computed for both the pure noise bath and the noise bath with the code embedded in it under two conditions. In one random routing was used to select the coefficients, and in the other the same coefficients were re-sent at each coding cycle. Next the cumulative density functions (cdfs) of these two cases (vertical axis) were plotted against the cdf for a pure noise bath of the same number of spikes (horizontal axis). Note that if two cdfs were identical the result would be a straight line. The γ timing signal clearly shows up using the cdf for the case where the same coefficients are used at every cycle (red curve) vs. the random cdf. In contrast, the random routing cdf vs. the random cdf tends to obscure the γ signal completely (black curve).
Mentions: In the first test, we constructed a more complete spike data set by embedding those spikes in a “noise bath” of background neural spiking. The idea of noise here is as a model for any other ongoing processing. For each neuron, the probability of firing at each millisecond was set to 0.008, representing approximately 10 Hz background rate, and the model's spikes were added to this bath. For comparison, we compared these spike trains to a pure noise bath with firing probability of 0.01. The different background rates were chosen so that the number of spikes in each case was approximately the same. The results are shown in Figure 6. The black curve shows the cumulative distribution of the spike train for the spikes that use random routing plotted against the cumulative distribution for white noise spikes. By way of comparison, the red curve shows the distribution of spikes in a case where random routing is not used and one set of coefficients is simply repeated, plotted against the cumulative node distribution. It is easily seen that the random routing necessitated by the learning algorithm obscures the timing signal. With the random routing, no timing perturbation is noticeable even after 40 s of simulation. In contrast, without the random routing constraint, the effect of the γ latency code clearly shows up in the plot as a step change, representing the influence of the highly detectable 50 Hz signal.

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