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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 and Sleep-Related Firing in HVC and RA Neurons of Zebra Finches(A) During the production of a song motif (sound spectrogram on top), RA-projecting HVC neurons (HVCRA neurons) produce at most one stereotyped spike burst (red rasters). HVC interneurons (HVCI neurons) produce dense and less-stereotyped spike patterns (green rasters). A more elaborate version of this figure was originally published in [1].(B) Sleep-related firing in HVCRA and RA neurons. (i) Top: spike-raster plot of a simultaneously recorded HVCRA–RA pair during sleep. RA spikes (black rasters) have been time aligned to HVCRA bursts (red rasters). (i) Bottom: CSP function of the same neuron pair. Also known as the cross-intensity function, the CSP function is an estimate of the conditional RA spiking probability as a function of the time lag to HVCRA spikes (see Methods). (ii) ISI pdfs of RA neurons vary from one dataset to another. ISI pdfs have been averaged either over 29 RA neurons recorded in isolation (full line), or over 26 RA neurons recorded simultaneously with HVCRA neurons (dashed line), or over 50 RA neurons recorded simultaneously with HVCI neurons (dotted line).
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pcbi-0030249-g001: Song and Sleep-Related Firing in HVC and RA Neurons of Zebra Finches(A) During the production of a song motif (sound spectrogram on top), RA-projecting HVC neurons (HVCRA neurons) produce at most one stereotyped spike burst (red rasters). HVC interneurons (HVCI neurons) produce dense and less-stereotyped spike patterns (green rasters). A more elaborate version of this figure was originally published in [1].(B) Sleep-related firing in HVCRA and RA neurons. (i) Top: spike-raster plot of a simultaneously recorded HVCRA–RA pair during sleep. RA spikes (black rasters) have been time aligned to HVCRA bursts (red rasters). (i) Bottom: CSP function of the same neuron pair. Also known as the cross-intensity function, the CSP function is an estimate of the conditional RA spiking probability as a function of the time lag to HVCRA spikes (see Methods). (ii) ISI pdfs of RA neurons vary from one dataset to another. ISI pdfs have been averaged either over 29 RA neurons recorded in isolation (full line), or over 26 RA neurons recorded simultaneously with HVCRA neurons (dashed line), or over 50 RA neurons recorded simultaneously with HVCI neurons (dotted line).

Mentions: In the robust nucleus of the arcopallium (RA) and the high vocal center (HVC) of zebra finches, neurons exhibit precise and stereotyped high-frequency bursts during singing. The number of bursts produced per song motif varies strongly between neuron types, from about one burst in RA-projecting HVC neurons (HVCRA neurons), to about 12 bursts in RA projection neurons, and up to more than 20 bursts in HVC interneurons (HVCI neurons) (Figure 1A) [1,10,11]. In awake, non-singing birds, RA and HVC neurons do not burst and are either silent or in a mode of tonic firing [12,13]. And during sleep they display incessant switching between bursting and tonic firing modes; in RA neurons, the sleep-related burst patterns can be highly similar to song-related patterns [4], and often the patterns are time-locked to bursts in simultaneously recorded RA-projecting HVC neurons (Figure 1Bi) [12].


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

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

Song and Sleep-Related Firing in HVC and RA Neurons of Zebra Finches(A) During the production of a song motif (sound spectrogram on top), RA-projecting HVC neurons (HVCRA neurons) produce at most one stereotyped spike burst (red rasters). HVC interneurons (HVCI neurons) produce dense and less-stereotyped spike patterns (green rasters). A more elaborate version of this figure was originally published in [1].(B) Sleep-related firing in HVCRA and RA neurons. (i) Top: spike-raster plot of a simultaneously recorded HVCRA–RA pair during sleep. RA spikes (black rasters) have been time aligned to HVCRA bursts (red rasters). (i) Bottom: CSP function of the same neuron pair. Also known as the cross-intensity function, the CSP function is an estimate of the conditional RA spiking probability as a function of the time lag to HVCRA spikes (see Methods). (ii) ISI pdfs of RA neurons vary from one dataset to another. ISI pdfs have been averaged either over 29 RA neurons recorded in isolation (full line), or over 26 RA neurons recorded simultaneously with HVCRA neurons (dashed line), or over 50 RA neurons recorded simultaneously with HVCI neurons (dotted line).
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pcbi-0030249-g001: Song and Sleep-Related Firing in HVC and RA Neurons of Zebra Finches(A) During the production of a song motif (sound spectrogram on top), RA-projecting HVC neurons (HVCRA neurons) produce at most one stereotyped spike burst (red rasters). HVC interneurons (HVCI neurons) produce dense and less-stereotyped spike patterns (green rasters). A more elaborate version of this figure was originally published in [1].(B) Sleep-related firing in HVCRA and RA neurons. (i) Top: spike-raster plot of a simultaneously recorded HVCRA–RA pair during sleep. RA spikes (black rasters) have been time aligned to HVCRA bursts (red rasters). (i) Bottom: CSP function of the same neuron pair. Also known as the cross-intensity function, the CSP function is an estimate of the conditional RA spiking probability as a function of the time lag to HVCRA spikes (see Methods). (ii) ISI pdfs of RA neurons vary from one dataset to another. ISI pdfs have been averaged either over 29 RA neurons recorded in isolation (full line), or over 26 RA neurons recorded simultaneously with HVCRA neurons (dashed line), or over 50 RA neurons recorded simultaneously with HVCI neurons (dotted line).
Mentions: In the robust nucleus of the arcopallium (RA) and the high vocal center (HVC) of zebra finches, neurons exhibit precise and stereotyped high-frequency bursts during singing. The number of bursts produced per song motif varies strongly between neuron types, from about one burst in RA-projecting HVC neurons (HVCRA neurons), to about 12 bursts in RA projection neurons, and up to more than 20 bursts in HVC interneurons (HVCI neurons) (Figure 1A) [1,10,11]. In awake, non-singing birds, RA and HVC neurons do not burst and are either silent or in a mode of tonic firing [12,13]. And during sleep they display incessant switching between bursting and tonic firing modes; in RA neurons, the sleep-related burst patterns can be highly similar to song-related patterns [4], and often the patterns are time-locked to bursts in simultaneously recorded RA-projecting HVC neurons (Figure 1Bi) [12].

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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