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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

Random action potential spike selection. On each γ cycle, only one of neurons with overlapping receptive fields can signal. Whether or not a neuron sends an action potential is determined probabilistically according to the relative projections of the input onto receptive fields. For the two neuron example shown, for the particular input shown in red, the odds of the leftmost neuron being chosen over the rightmost neuron is given by the ratio of r2 to r1. In the case of multiple overlapping receptive fields, the odds are distributed appropriately among them.
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Figure 1: Random action potential spike selection. On each γ cycle, only one of neurons with overlapping receptive fields can signal. Whether or not a neuron sends an action potential is determined probabilistically according to the relative projections of the input onto receptive fields. For the two neuron example shown, for the particular input shown in red, the odds of the leftmost neuron being chosen over the rightmost neuron is given by the ratio of r2 to r1. In the case of multiple overlapping receptive fields, the odds are distributed appropriately among them.

Mentions: A central constraint on action potential generation concerns neurons with overlapping receptive fields. Two receptive fields are said to overlap when the dot product of their normalized receptive fields is significant, which we take to be greater than some scaler value μ. In our simulations most neurons are nearly orthogonal, that is, the dot product is less than 0.20. Rather than selecting the most similar neuron at each instant, neurons with significantly overlapping receptive fields compete to be chosen (Jehee et al., 2006). In terms of the spike model, when two basis functions overlap significantly, they are as a consequence not orthogonal and must probabilistically compete to be the one chosen to send a spike as shown in Figure 1. The probability of being chosen is given by p = e10r/Z where r is the projection, the empirically determined scalar 10 weights the largest responders, and Z is a normalization factor. In the figure the red circles denote the two competing responses of the left and right receptive fields for a particular input. The probabilistic protocol is dictated by the expectation maximization constraint (Dempster et al., 1977) for estimating overlapping probability distributions. Its absence leads to double counting that results in erroneous receptive fields. Our claim is that the observed randomness in spike trains may be a consequence of the need to satisfy this constraint, which ensures that receptive fields are learned correctly.


Dual roles for spike signaling in cortical neural populations.

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

Random action potential spike selection. On each γ cycle, only one of neurons with overlapping receptive fields can signal. Whether or not a neuron sends an action potential is determined probabilistically according to the relative projections of the input onto receptive fields. For the two neuron example shown, for the particular input shown in red, the odds of the leftmost neuron being chosen over the rightmost neuron is given by the ratio of r2 to r1. In the case of multiple overlapping receptive fields, the odds are distributed appropriately among them.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Random action potential spike selection. On each γ cycle, only one of neurons with overlapping receptive fields can signal. Whether or not a neuron sends an action potential is determined probabilistically according to the relative projections of the input onto receptive fields. For the two neuron example shown, for the particular input shown in red, the odds of the leftmost neuron being chosen over the rightmost neuron is given by the ratio of r2 to r1. In the case of multiple overlapping receptive fields, the odds are distributed appropriately among them.
Mentions: A central constraint on action potential generation concerns neurons with overlapping receptive fields. Two receptive fields are said to overlap when the dot product of their normalized receptive fields is significant, which we take to be greater than some scaler value μ. In our simulations most neurons are nearly orthogonal, that is, the dot product is less than 0.20. Rather than selecting the most similar neuron at each instant, neurons with significantly overlapping receptive fields compete to be chosen (Jehee et al., 2006). In terms of the spike model, when two basis functions overlap significantly, they are as a consequence not orthogonal and must probabilistically compete to be the one chosen to send a spike as shown in Figure 1. The probability of being chosen is given by p = e10r/Z where r is the projection, the empirically determined scalar 10 weights the largest responders, and Z is a normalization factor. In the figure the red circles denote the two competing responses of the left and right receptive fields for a particular input. The probabilistic protocol is dictated by the expectation maximization constraint (Dempster et al., 1977) for estimating overlapping probability distributions. Its absence leads to double counting that results in erroneous receptive fields. Our claim is that the observed randomness in spike trains may be a consequence of the need to satisfy this constraint, which ensures that receptive fields are learned correctly.

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