Limits...
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

For each basis function the scaled and quantized histogram of latencies is plotted where the number of coefficients of a given latency range is represented by a gray scale with the scale calibration on the RHS. This example shows, contrary to a Poisson expectation, that the distribution is very non-uniform.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3108387&req=5

Figure 8: For each basis function the scaled and quantized histogram of latencies is plotted where the number of coefficients of a given latency range is represented by a gray scale with the scale calibration on the RHS. This example shows, contrary to a Poisson expectation, that the distribution is very non-uniform.

Mentions: Figure 8 shows an essential feature that would distinguish the model from a standard Poisson model. If the same image patch is used repeatedly, then any given neuron will have, on average, a discrete set of coefficients that it signals, owing to the discrete nature of the signaling pool of cells. Thus, if a histogram is made of these coefficients for each cell, as is shown in the figure, the entries will not be uniform, as expected if the process were Poisson, but instead will exhibit the asymmetries shown, where certain latencies are used much more frequently than others.


Dual roles for spike signaling in cortical neural populations.

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

For each basis function the scaled and quantized histogram of latencies is plotted where the number of coefficients of a given latency range is represented by a gray scale with the scale calibration on the RHS. This example shows, contrary to a Poisson expectation, that the distribution is very non-uniform.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: For each basis function the scaled and quantized histogram of latencies is plotted where the number of coefficients of a given latency range is represented by a gray scale with the scale calibration on the RHS. This example shows, contrary to a Poisson expectation, that the distribution is very non-uniform.
Mentions: Figure 8 shows an essential feature that would distinguish the model from a standard Poisson model. If the same image patch is used repeatedly, then any given neuron will have, on average, a discrete set of coefficients that it signals, owing to the discrete nature of the signaling pool of cells. Thus, if a histogram is made of these coefficients for each cell, as is shown in the figure, the entries will not be uniform, as expected if the process were Poisson, but instead will exhibit the asymmetries shown, where certain latencies are used much more frequently than others.

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