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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

Responses of 256 model neurons when re-coding a single 10 × 10 image patch repeatedly for 200 γ cycles. (Top) Invariant encoding. The reconstruction of the input patch for cycles 20, 40, 60, 80, 120, 140, 160, 180, and 200. At each update cycle, the reconstruction of the image input patch changes slightly but is for the most part invariant. (Center) Individual spikes communicate numerical values by virtue of their latency relationship to a γ phase. Owing to the probabilistic nature of cell selection, at each cycle, different neurons are selected and send a spike. The latency of each spike is denoted by a color that encodes its scalar coefficient. The highest values are light yellow to white and lowest values are dark red. These values are translated into milliseconds using the latency formula in Eq. 2. (Right) Basis functions for two coding cycles Cycles 60 (1200 ms) and 120 (2400 ms) are representative examples showing that the sets of 12 neurons sending spikes vary from update to update.
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Figure 4: Responses of 256 model neurons when re-coding a single 10 × 10 image patch repeatedly for 200 γ cycles. (Top) Invariant encoding. The reconstruction of the input patch for cycles 20, 40, 60, 80, 120, 140, 160, 180, and 200. At each update cycle, the reconstruction of the image input patch changes slightly but is for the most part invariant. (Center) Individual spikes communicate numerical values by virtue of their latency relationship to a γ phase. Owing to the probabilistic nature of cell selection, at each cycle, different neurons are selected and send a spike. The latency of each spike is denoted by a color that encodes its scalar coefficient. The highest values are light yellow to white and lowest values are dark red. These values are translated into milliseconds using the latency formula in Eq. 2. (Right) Basis functions for two coding cycles Cycles 60 (1200 ms) and 120 (2400 ms) are representative examples showing that the sets of 12 neurons sending spikes vary from update to update.

Mentions: The Gabor image test shows that a probabilistic selection method can produce orientation tuning but does not show off two crucial features of the coding method, namely (1) the codings of an image patch vary from γ cycle to γ cycle and (2) they use latency coding to send a coefficient. These two features are made explicit in Figure 4. In this larger 10 × 10 simulation the same image patch is fit repeatedly with 12 basis functions for 200 times. The simulation essentially assumes that there is no underlying time constant linking one fitting iteration to the next, i.e., the circuit is assumed to be memoryless. However, by adapting the method of (Druckmann and Chklovskii, 2010), memory could be added.


Dual roles for spike signaling in cortical neural populations.

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

Responses of 256 model neurons when re-coding a single 10 × 10 image patch repeatedly for 200 γ cycles. (Top) Invariant encoding. The reconstruction of the input patch for cycles 20, 40, 60, 80, 120, 140, 160, 180, and 200. At each update cycle, the reconstruction of the image input patch changes slightly but is for the most part invariant. (Center) Individual spikes communicate numerical values by virtue of their latency relationship to a γ phase. Owing to the probabilistic nature of cell selection, at each cycle, different neurons are selected and send a spike. The latency of each spike is denoted by a color that encodes its scalar coefficient. The highest values are light yellow to white and lowest values are dark red. These values are translated into milliseconds using the latency formula in Eq. 2. (Right) Basis functions for two coding cycles Cycles 60 (1200 ms) and 120 (2400 ms) are representative examples showing that the sets of 12 neurons sending spikes vary from update to update.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Figure 4: Responses of 256 model neurons when re-coding a single 10 × 10 image patch repeatedly for 200 γ cycles. (Top) Invariant encoding. The reconstruction of the input patch for cycles 20, 40, 60, 80, 120, 140, 160, 180, and 200. At each update cycle, the reconstruction of the image input patch changes slightly but is for the most part invariant. (Center) Individual spikes communicate numerical values by virtue of their latency relationship to a γ phase. Owing to the probabilistic nature of cell selection, at each cycle, different neurons are selected and send a spike. The latency of each spike is denoted by a color that encodes its scalar coefficient. The highest values are light yellow to white and lowest values are dark red. These values are translated into milliseconds using the latency formula in Eq. 2. (Right) Basis functions for two coding cycles Cycles 60 (1200 ms) and 120 (2400 ms) are representative examples showing that the sets of 12 neurons sending spikes vary from update to update.
Mentions: The Gabor image test shows that a probabilistic selection method can produce orientation tuning but does not show off two crucial features of the coding method, namely (1) the codings of an image patch vary from γ cycle to γ cycle and (2) they use latency coding to send a coefficient. These two features are made explicit in Figure 4. In this larger 10 × 10 simulation the same image patch is fit repeatedly with 12 basis functions for 200 times. The simulation essentially assumes that there is no underlying time constant linking one fitting iteration to the next, i.e., the circuit is assumed to be memoryless. However, by adapting the method of (Druckmann and Chklovskii, 2010), memory could be added.

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