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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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Burst Epochs and Pairwise Correlations(A) Instantaneous firing rates of a recorded HVCI neuron (top), a simulated HVCI neuron without burst epochs (middle), and a simulated HVCI neuron with burst epochs (bottom). Burst epochs are indicated by arrows.(B) A sample raster plot of a simultaneously recorded HVCRA–HVCI pair (top) and a comparable plot from model simulations (bottom). The inclusion of burst epochs gives rise to rows with very sparse HVCI bursting (top arrow) and rows with dense HVCI bursting (bottom arrow), as is seen in real data.(C–F) Average CSP functions in different neuron types. The functions are plotted in reference to a spike in the first pair, i.e., with respect to RA spikes in (D) and with respect to HVCRA spikes in (E).(C) RA–RA neuron pairs (from n = 29 recorded pairs). p = 6/7, and q = 39/40.(D) RA–HVCI pairs (n = 50 pairs). The arrow indicates an asymmetry that is reproduced by the model. p = 9/11, and q = 49/50.(E) HVCRA–HVCI (n = 26). p = 7/8, and q = 59/60.(F) HVCI–HVCI pairs (n = 19). HVCI neurons randomly link to 56 of the 100 HVCRA groups. p = 7/8, and q = 32/33.In (C–F) LI = 50, pI = 0.63, DR = 240 ms, pR = 0.92, and LR = 13.
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pcbi-0030249-g005: Burst Epochs and Pairwise Correlations(A) Instantaneous firing rates of a recorded HVCI neuron (top), a simulated HVCI neuron without burst epochs (middle), and a simulated HVCI neuron with burst epochs (bottom). Burst epochs are indicated by arrows.(B) A sample raster plot of a simultaneously recorded HVCRA–HVCI pair (top) and a comparable plot from model simulations (bottom). The inclusion of burst epochs gives rise to rows with very sparse HVCI bursting (top arrow) and rows with dense HVCI bursting (bottom arrow), as is seen in real data.(C–F) Average CSP functions in different neuron types. The functions are plotted in reference to a spike in the first pair, i.e., with respect to RA spikes in (D) and with respect to HVCRA spikes in (E).(C) RA–RA neuron pairs (from n = 29 recorded pairs). p = 6/7, and q = 39/40.(D) RA–HVCI pairs (n = 50 pairs). The arrow indicates an asymmetry that is reproduced by the model. p = 9/11, and q = 49/50.(E) HVCRA–HVCI (n = 26). p = 7/8, and q = 59/60.(F) HVCI–HVCI pairs (n = 19). HVCI neurons randomly link to 56 of the 100 HVCRA groups. p = 7/8, and q = 32/33.In (C–F) LI = 50, pI = 0.63, DR = 240 ms, pR = 0.92, and LR = 13.

Mentions: RA and HVCI neurons frequently display 1–2 s epochs of increased burst density during sleep ([12]; Figure 5A, top). From a recent experimental study, we know that these burst epochs are shaped by input from the thalamic nucleus uveaformis (Uva): decreased tonic firing in HVC-projecting Uva neurons leads to increased bursting in HVC and RA neurons, whereas increased tonic firing in HVC-projecting Uva neurons suppresses HVC and RA burst rates (unpublished data). Here, we modeled this Uva-mediated control of burst epochs by random fluctuations of the parameter p (we transiently set p = 1 to model a burst epoch; see Methods) (Figure 5A, middle and bottom). By modifying p rather than any other parameter, we satisfied the experimental finding that burst shapes (burst-related ISI distributions) are unchanged during burst epochs. By virtue of burst epochs, raster plots of simulated HVCRA–HVCI pairs were very realistic and displayed characteristic horizontal bands of long, uninterrupted bursting, coexisting with brief bands of very few bursts (Figure 5B). Without fluctuations in p, HVCI burst patterns would mostly be either narrow or wide, but not both.


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

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

Burst Epochs and Pairwise Correlations(A) Instantaneous firing rates of a recorded HVCI neuron (top), a simulated HVCI neuron without burst epochs (middle), and a simulated HVCI neuron with burst epochs (bottom). Burst epochs are indicated by arrows.(B) A sample raster plot of a simultaneously recorded HVCRA–HVCI pair (top) and a comparable plot from model simulations (bottom). The inclusion of burst epochs gives rise to rows with very sparse HVCI bursting (top arrow) and rows with dense HVCI bursting (bottom arrow), as is seen in real data.(C–F) Average CSP functions in different neuron types. The functions are plotted in reference to a spike in the first pair, i.e., with respect to RA spikes in (D) and with respect to HVCRA spikes in (E).(C) RA–RA neuron pairs (from n = 29 recorded pairs). p = 6/7, and q = 39/40.(D) RA–HVCI pairs (n = 50 pairs). The arrow indicates an asymmetry that is reproduced by the model. p = 9/11, and q = 49/50.(E) HVCRA–HVCI (n = 26). p = 7/8, and q = 59/60.(F) HVCI–HVCI pairs (n = 19). HVCI neurons randomly link to 56 of the 100 HVCRA groups. p = 7/8, and q = 32/33.In (C–F) LI = 50, pI = 0.63, DR = 240 ms, pR = 0.92, and LR = 13.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2230679&req=5

pcbi-0030249-g005: Burst Epochs and Pairwise Correlations(A) Instantaneous firing rates of a recorded HVCI neuron (top), a simulated HVCI neuron without burst epochs (middle), and a simulated HVCI neuron with burst epochs (bottom). Burst epochs are indicated by arrows.(B) A sample raster plot of a simultaneously recorded HVCRA–HVCI pair (top) and a comparable plot from model simulations (bottom). The inclusion of burst epochs gives rise to rows with very sparse HVCI bursting (top arrow) and rows with dense HVCI bursting (bottom arrow), as is seen in real data.(C–F) Average CSP functions in different neuron types. The functions are plotted in reference to a spike in the first pair, i.e., with respect to RA spikes in (D) and with respect to HVCRA spikes in (E).(C) RA–RA neuron pairs (from n = 29 recorded pairs). p = 6/7, and q = 39/40.(D) RA–HVCI pairs (n = 50 pairs). The arrow indicates an asymmetry that is reproduced by the model. p = 9/11, and q = 49/50.(E) HVCRA–HVCI (n = 26). p = 7/8, and q = 59/60.(F) HVCI–HVCI pairs (n = 19). HVCI neurons randomly link to 56 of the 100 HVCRA groups. p = 7/8, and q = 32/33.In (C–F) LI = 50, pI = 0.63, DR = 240 ms, pR = 0.92, and LR = 13.
Mentions: RA and HVCI neurons frequently display 1–2 s epochs of increased burst density during sleep ([12]; Figure 5A, top). From a recent experimental study, we know that these burst epochs are shaped by input from the thalamic nucleus uveaformis (Uva): decreased tonic firing in HVC-projecting Uva neurons leads to increased bursting in HVC and RA neurons, whereas increased tonic firing in HVC-projecting Uva neurons suppresses HVC and RA burst rates (unpublished data). Here, we modeled this Uva-mediated control of burst epochs by random fluctuations of the parameter p (we transiently set p = 1 to model a burst epoch; see Methods) (Figure 5A, middle and bottom). By modifying p rather than any other parameter, we satisfied the experimental finding that burst shapes (burst-related ISI distributions) are unchanged during burst epochs. By virtue of burst epochs, raster plots of simulated HVCRA–HVCI pairs were very realistic and displayed characteristic horizontal bands of long, uninterrupted bursting, coexisting with brief bands of very few bursts (Figure 5B). Without fluctuations in p, HVCI burst patterns would mostly be either narrow or wide, but not both.

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