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

The orientation tuning of the γ latency code shows the classical bell-shape tuning. The histogram of two 8 × 8 receptive fields learned by the algorithm tested with a rotated set of Gabor patches 10° apart. Each histogram records, for 1200 presentations at each tested orientation, the number of times the neuron was randomly selected by the algorithm. By way of comparison, in a conventional model the y-axis would record the match between the input and the receptive field. The two peaks for each neuron result from the fact that the Gabor patch is self similar every 180°.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: The orientation tuning of the γ latency code shows the classical bell-shape tuning. The histogram of two 8 × 8 receptive fields learned by the algorithm tested with a rotated set of Gabor patches 10° apart. Each histogram records, for 1200 presentations at each tested orientation, the number of times the neuron was randomly selected by the algorithm. By way of comparison, in a conventional model the y-axis would record the match between the input and the receptive field. The two peaks for each neuron result from the fact that the Gabor patch is self similar every 180°.

Mentions: Thirty-six Gabor image patches were created, one for every 10° rotation. These were then presented to the network 1200 times and fit with learned basis functions each time. To emphasize the point that the randomized neural selection process models the receptive field, we chose two basis functions that overlap and measured their receptive fields using their spike counts for the different Gabor orientations. Their histogram data are indicated in Figure 3. The spike counts, which in the model reflect the number of times they were chosen probabilistically, are representative of standard oriented receptive fields measured experimentally using repeated trials to generate a post-stimulus time histogram, even though the model's process for generating spikes is very different from any standard rate-code model.


Dual roles for spike signaling in cortical neural populations.

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

The orientation tuning of the γ latency code shows the classical bell-shape tuning. The histogram of two 8 × 8 receptive fields learned by the algorithm tested with a rotated set of Gabor patches 10° apart. Each histogram records, for 1200 presentations at each tested orientation, the number of times the neuron was randomly selected by the algorithm. By way of comparison, in a conventional model the y-axis would record the match between the input and the receptive field. The two peaks for each neuron result from the fact that the Gabor patch is self similar every 180°.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: The orientation tuning of the γ latency code shows the classical bell-shape tuning. The histogram of two 8 × 8 receptive fields learned by the algorithm tested with a rotated set of Gabor patches 10° apart. Each histogram records, for 1200 presentations at each tested orientation, the number of times the neuron was randomly selected by the algorithm. By way of comparison, in a conventional model the y-axis would record the match between the input and the receptive field. The two peaks for each neuron result from the fact that the Gabor patch is self similar every 180°.
Mentions: Thirty-six Gabor image patches were created, one for every 10° rotation. These were then presented to the network 1200 times and fit with learned basis functions each time. To emphasize the point that the randomized neural selection process models the receptive field, we chose two basis functions that overlap and measured their receptive fields using their spike counts for the different Gabor orientations. Their histogram data are indicated in Figure 3. The spike counts, which in the model reflect the number of times they were chosen probabilistically, are representative of standard oriented receptive fields measured experimentally using repeated trials to generate a post-stimulus time histogram, even though the model's process for generating spikes is very different from any standard rate-code model.

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