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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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Modeling Sleep-Related Activity (p,q < 1)(A) An RA neuron producing few burst ISIs. A good fit is produced when the survival time of the ground state is long, compared to that of song states (light sleep, q much closer to 1 than p). DR = 80 ms, and VR = 0.7.(B) A different RA neuron producing many burst ISIs. A good fit was produced by a relatively long survival time of sleep states (deep sleep). DR = 120 ms, and VR = 0.67.(C,D) Spike raster plots of HVCRA and RA neurons. All HVCRA bursts (red rasters) are aligned at the center of the plots. Corresponding RA spikes (black rasters) are shown below each HVCRA burst. When p is large (strongly coherent sleep) (C), stereotyped RA bursting is observed over larger intervals than when p is small (D).
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pcbi-0030249-g004: Modeling Sleep-Related Activity (p,q < 1)(A) An RA neuron producing few burst ISIs. A good fit is produced when the survival time of the ground state is long, compared to that of song states (light sleep, q much closer to 1 than p). DR = 80 ms, and VR = 0.7.(B) A different RA neuron producing many burst ISIs. A good fit was produced by a relatively long survival time of sleep states (deep sleep). DR = 120 ms, and VR = 0.67.(C,D) Spike raster plots of HVCRA and RA neurons. All HVCRA bursts (red rasters) are aligned at the center of the plots. Corresponding RA spikes (black rasters) are shown below each HVCRA burst. When p is large (strongly coherent sleep) (C), stereotyped RA bursting is observed over larger intervals than when p is small (D).

Mentions: Sleep-related ISI pdfs of RA neurons could be well-fit given a suitable tonic-firing model and suitable persistence probabilities p and q (Figure 4A and 4B). The peak at small ISIs resulted from spikes produced in song states, and the peak at large ISIs from spikes produced in the ground state. Raster plots of simulated RA-neuron activity aligned to HVCRA bursts looked very realistic (compare Figure 4C and 4D to Figure 1Bi). Autocovariance functions of sleep-related RA spike trains could also be well-fit (see Figure S2).


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

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

Modeling Sleep-Related Activity (p,q < 1)(A) An RA neuron producing few burst ISIs. A good fit is produced when the survival time of the ground state is long, compared to that of song states (light sleep, q much closer to 1 than p). DR = 80 ms, and VR = 0.7.(B) A different RA neuron producing many burst ISIs. A good fit was produced by a relatively long survival time of sleep states (deep sleep). DR = 120 ms, and VR = 0.67.(C,D) Spike raster plots of HVCRA and RA neurons. All HVCRA bursts (red rasters) are aligned at the center of the plots. Corresponding RA spikes (black rasters) are shown below each HVCRA burst. When p is large (strongly coherent sleep) (C), stereotyped RA bursting is observed over larger intervals than when p is small (D).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2230679&req=5

pcbi-0030249-g004: Modeling Sleep-Related Activity (p,q < 1)(A) An RA neuron producing few burst ISIs. A good fit is produced when the survival time of the ground state is long, compared to that of song states (light sleep, q much closer to 1 than p). DR = 80 ms, and VR = 0.7.(B) A different RA neuron producing many burst ISIs. A good fit was produced by a relatively long survival time of sleep states (deep sleep). DR = 120 ms, and VR = 0.67.(C,D) Spike raster plots of HVCRA and RA neurons. All HVCRA bursts (red rasters) are aligned at the center of the plots. Corresponding RA spikes (black rasters) are shown below each HVCRA burst. When p is large (strongly coherent sleep) (C), stereotyped RA bursting is observed over larger intervals than when p is small (D).
Mentions: Sleep-related ISI pdfs of RA neurons could be well-fit given a suitable tonic-firing model and suitable persistence probabilities p and q (Figure 4A and 4B). The peak at small ISIs resulted from spikes produced in song states, and the peak at large ISIs from spikes produced in the ground state. Raster plots of simulated RA-neuron activity aligned to HVCRA bursts looked very realistic (compare Figure 4C and 4D to Figure 1Bi). Autocovariance functions of sleep-related RA spike trains could also be well-fit (see Figure S2).

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