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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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Song-Related ISI pdfs of RA and HVCI Neurons(A,B) Model-based fits of averaged ISI pdfs in RA and HVCI neurons during singing. The arrows delimit the ISI range of the burst models in Figure 2C, i.e., 6 ms and 10 ms, respectively. The RA-neuron data (A) were taken from [10], and the HVCI data (B) were provided courtesy of A. Kozhevnikov. LR = 12, and LI = 35.(C,D) Raster plots of song-related spike trains in four RA and four HVCI model neurons for two different values of link counts LR/I and burst probabilities pR/I. Spikes are represented as tick marks and drawn in alternating colors for different neurons.
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pcbi-0030249-g003: Song-Related ISI pdfs of RA and HVCI Neurons(A,B) Model-based fits of averaged ISI pdfs in RA and HVCI neurons during singing. The arrows delimit the ISI range of the burst models in Figure 2C, i.e., 6 ms and 10 ms, respectively. The RA-neuron data (A) were taken from [10], and the HVCI data (B) were provided courtesy of A. Kozhevnikov. LR = 12, and LI = 35.(C,D) Raster plots of song-related spike trains in four RA and four HVCI model neurons for two different values of link counts LR/I and burst probabilities pR/I. Spikes are represented as tick marks and drawn in alternating colors for different neurons.

Mentions: We found that song-related ISI pdfs beyond the burst scale could be well fit over the entire ISI range (up to 100 ms) by randomly linking RA neurons to LR = 12 HVCRA groups and HVCI neurons to LI = 35 groups (Figure 3A and 3B). Note that the larger the link counts LR and LI, the steeper were the corresponding exponential tails of the pdfs. However, to also account for the considerable lack of stereotypy mainly in raster plots of HVCI neurons [11], we had to trade off high link counts against reduced burst probabilities (the probability that a neuron bursts when an HVCRA group to which it is linked is activated). Note that a less than unit burst probability can be interpreted as a reduction in neural responsiveness to excitatory synaptic drive, or as increased inhibition. We obtained good results with burst probabilities in RA neurons of pR = 0.92 (LR = 13) and in HVCI neurons pI = 0.63 (LI = 50) (Figure 3C and 3D). Note that first-order statistics impose the following constraints on the average number of RA and HVCI bursts per song motif: pRLR ≅ 12 and pILI ≅ 35.


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

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

Song-Related ISI pdfs of RA and HVCI Neurons(A,B) Model-based fits of averaged ISI pdfs in RA and HVCI neurons during singing. The arrows delimit the ISI range of the burst models in Figure 2C, i.e., 6 ms and 10 ms, respectively. The RA-neuron data (A) were taken from [10], and the HVCI data (B) were provided courtesy of A. Kozhevnikov. LR = 12, and LI = 35.(C,D) Raster plots of song-related spike trains in four RA and four HVCI model neurons for two different values of link counts LR/I and burst probabilities pR/I. Spikes are represented as tick marks and drawn in alternating colors for different neurons.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2230679&req=5

pcbi-0030249-g003: Song-Related ISI pdfs of RA and HVCI Neurons(A,B) Model-based fits of averaged ISI pdfs in RA and HVCI neurons during singing. The arrows delimit the ISI range of the burst models in Figure 2C, i.e., 6 ms and 10 ms, respectively. The RA-neuron data (A) were taken from [10], and the HVCI data (B) were provided courtesy of A. Kozhevnikov. LR = 12, and LI = 35.(C,D) Raster plots of song-related spike trains in four RA and four HVCI model neurons for two different values of link counts LR/I and burst probabilities pR/I. Spikes are represented as tick marks and drawn in alternating colors for different neurons.
Mentions: We found that song-related ISI pdfs beyond the burst scale could be well fit over the entire ISI range (up to 100 ms) by randomly linking RA neurons to LR = 12 HVCRA groups and HVCI neurons to LI = 35 groups (Figure 3A and 3B). Note that the larger the link counts LR and LI, the steeper were the corresponding exponential tails of the pdfs. However, to also account for the considerable lack of stereotypy mainly in raster plots of HVCI neurons [11], we had to trade off high link counts against reduced burst probabilities (the probability that a neuron bursts when an HVCRA group to which it is linked is activated). Note that a less than unit burst probability can be interpreted as a reduction in neural responsiveness to excitatory synaptic drive, or as increased inhibition. We obtained good results with burst probabilities in RA neurons of pR = 0.92 (LR = 13) and in HVCI neurons pI = 0.63 (LI = 50) (Figure 3C and 3D). Note that first-order statistics impose the following constraints on the average number of RA and HVCI bursts per song motif: pRLR ≅ 12 and pILI ≅ 35.

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
Related in: MedlinePlus