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Spike correlations in a songbird agree with a simple markov population model.

Weber AP, Hahnloser RH - PLoS Comput. Biol. (2007)

Bottom Line: Individual spike trains are generated by associating with each of the population states a particular firing mode, such as bursting or tonic firing.Our results suggest that song- and sleep-related firing patterns are identical on short time scales and result from random sampling of a unique underlying theme.The efficiency of our population model may apply also to other neural systems in which population hypotheses can be tested on recordings from small neuron groups.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroinformatics UZH/ETH Zurich, Zurich, Switzerland.

ABSTRACT
The relationships between neural activity at the single-cell and the population levels are of central importance for understanding neural codes. In many sensory systems, collective behaviors in large cell groups can be described by pairwise spike correlations. Here, we test whether in a highly specialized premotor system of songbirds, pairwise spike correlations themselves can be seen as a simple corollary of an underlying random process. We test hypotheses on connectivity and network dynamics in the motor pathway of zebra finches using a high-level population model that is independent of detailed single-neuron properties. We assume that neural population activity evolves along a finite set of states during singing, and that during sleep population activity randomly switches back and forth between song states and a single resting state. Individual spike trains are generated by associating with each of the population states a particular firing mode, such as bursting or tonic firing. With an overall modification of one or two simple control parameters, the Markov model is able to reproduce observed firing statistics and spike correlations in different neuron types and behavioral states. Our results suggest that song- and sleep-related firing patterns are identical on short time scales and result from random sampling of a unique underlying theme. The efficiency of our population model may apply also to other neural systems in which population hypotheses can be tested on recordings from small neuron groups.

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HVC States Evolve Sequentially and Are Formed by Distinct HVCRA GroupsDistribution of CSPs in (n = 46) HVCRA–RA pairs in the interval −60 to 60 ms of HVCRA spikes (black histogram). With the exception of two peaks at CSPs zero and one (black arrows), the distribution is well-approximated by an exponential curve (purple line). Shown are the average CSP functions of 50 simulated HVCRA–RA pairs for three different model assumptions: (1) HVCRA neurons fire with probability pP = 0.8 in a single HVCRA group (red curve); (2) HVCRA neurons fire in two (randomly selected) HVCRA groups with probabilities 0.64 and 0.16 (green curve); and (3) activation of HVCRA groups is sequential in 80% of song-like transitions and in 20% it is random (blue curve). The green and blue arrows indicate inadequacies of model assumptions 2 and 3. p = 6/7, q = 39/40, LR = 12, pR = 1, DR = 240 ms, and pb = 0.
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pcbi-0030249-g006: HVC States Evolve Sequentially and Are Formed by Distinct HVCRA GroupsDistribution of CSPs in (n = 46) HVCRA–RA pairs in the interval −60 to 60 ms of HVCRA spikes (black histogram). With the exception of two peaks at CSPs zero and one (black arrows), the distribution is well-approximated by an exponential curve (purple line). Shown are the average CSP functions of 50 simulated HVCRA–RA pairs for three different model assumptions: (1) HVCRA neurons fire with probability pP = 0.8 in a single HVCRA group (red curve); (2) HVCRA neurons fire in two (randomly selected) HVCRA groups with probabilities 0.64 and 0.16 (green curve); and (3) activation of HVCRA groups is sequential in 80% of song-like transitions and in 20% it is random (blue curve). The green and blue arrows indicate inadequacies of model assumptions 2 and 3. p = 6/7, q = 39/40, LR = 12, pR = 1, DR = 240 ms, and pb = 0.

Mentions: We determined the experimental CSP distribution of all HVCRA–RA pairs in the time interval [−60, 60] ms of HVCRA spikes (Figure 6). With the exception of extreme (very small and very large) CSPs, the distribution was well-approximated by an exponential curve. The excessive occurrence of extreme CSPs did not happen by chance: the number of CSPs in the bin [0.99, 1] was significantly larger than the number of CSPs in equally sized adjacent bins (p < 0.01, binomial test). The same held true for the number of CSPs in the bin [0, 0.01], which was significantly larger than in adjacent bins. This CSP behavior illustrates that on the population level, RA activity tends to be highly locked to HVCRA bursts within at least ±60 ms.


Spike correlations in a songbird agree with a simple markov population model.

Weber AP, Hahnloser RH - PLoS Comput. Biol. (2007)

HVC States Evolve Sequentially and Are Formed by Distinct HVCRA GroupsDistribution of CSPs in (n = 46) HVCRA–RA pairs in the interval −60 to 60 ms of HVCRA spikes (black histogram). With the exception of two peaks at CSPs zero and one (black arrows), the distribution is well-approximated by an exponential curve (purple line). Shown are the average CSP functions of 50 simulated HVCRA–RA pairs for three different model assumptions: (1) HVCRA neurons fire with probability pP = 0.8 in a single HVCRA group (red curve); (2) HVCRA neurons fire in two (randomly selected) HVCRA groups with probabilities 0.64 and 0.16 (green curve); and (3) activation of HVCRA groups is sequential in 80% of song-like transitions and in 20% it is random (blue curve). The green and blue arrows indicate inadequacies of model assumptions 2 and 3. p = 6/7, q = 39/40, LR = 12, pR = 1, DR = 240 ms, and pb = 0.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2230679&req=5

pcbi-0030249-g006: HVC States Evolve Sequentially and Are Formed by Distinct HVCRA GroupsDistribution of CSPs in (n = 46) HVCRA–RA pairs in the interval −60 to 60 ms of HVCRA spikes (black histogram). With the exception of two peaks at CSPs zero and one (black arrows), the distribution is well-approximated by an exponential curve (purple line). Shown are the average CSP functions of 50 simulated HVCRA–RA pairs for three different model assumptions: (1) HVCRA neurons fire with probability pP = 0.8 in a single HVCRA group (red curve); (2) HVCRA neurons fire in two (randomly selected) HVCRA groups with probabilities 0.64 and 0.16 (green curve); and (3) activation of HVCRA groups is sequential in 80% of song-like transitions and in 20% it is random (blue curve). The green and blue arrows indicate inadequacies of model assumptions 2 and 3. p = 6/7, q = 39/40, LR = 12, pR = 1, DR = 240 ms, and pb = 0.
Mentions: We determined the experimental CSP distribution of all HVCRA–RA pairs in the time interval [−60, 60] ms of HVCRA spikes (Figure 6). With the exception of extreme (very small and very large) CSPs, the distribution was well-approximated by an exponential curve. The excessive occurrence of extreme CSPs did not happen by chance: the number of CSPs in the bin [0.99, 1] was significantly larger than the number of CSPs in equally sized adjacent bins (p < 0.01, binomial test). The same held true for the number of CSPs in the bin [0, 0.01], which was significantly larger than in adjacent bins. This CSP behavior illustrates that on the population level, RA activity tends to be highly locked to HVCRA bursts within at least ±60 ms.

Bottom Line: Individual spike trains are generated by associating with each of the population states a particular firing mode, such as bursting or tonic firing.Our results suggest that song- and sleep-related firing patterns are identical on short time scales and result from random sampling of a unique underlying theme.The efficiency of our population model may apply also to other neural systems in which population hypotheses can be tested on recordings from small neuron groups.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroinformatics UZH/ETH Zurich, Zurich, Switzerland.

ABSTRACT
The relationships between neural activity at the single-cell and the population levels are of central importance for understanding neural codes. In many sensory systems, collective behaviors in large cell groups can be described by pairwise spike correlations. Here, we test whether in a highly specialized premotor system of songbirds, pairwise spike correlations themselves can be seen as a simple corollary of an underlying random process. We test hypotheses on connectivity and network dynamics in the motor pathway of zebra finches using a high-level population model that is independent of detailed single-neuron properties. We assume that neural population activity evolves along a finite set of states during singing, and that during sleep population activity randomly switches back and forth between song states and a single resting state. Individual spike trains are generated by associating with each of the population states a particular firing mode, such as bursting or tonic firing. With an overall modification of one or two simple control parameters, the Markov model is able to reproduce observed firing statistics and spike correlations in different neuron types and behavioral states. Our results suggest that song- and sleep-related firing patterns are identical on short time scales and result from random sampling of a unique underlying theme. The efficiency of our population model may apply also to other neural systems in which population hypotheses can be tested on recordings from small neuron groups.

Show MeSH