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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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RA-Intrinsic Dynamics and Inhibition(A) When RA burst sequences extend beyond HVCI sequences by a random time uniformly distributed in the interval 0–15 ms, then the left flank of the average RA–HVCI CSP function gets uncharacteristically wide (arrows).(B) Transitive suppression of tonic firing in RA neurons is explained by RA inhibition. Shown are average RA IFR curves in 1.2 s time windows in which one RA neuron does not fire a burst, and time-aligned to burst onset in a simultaneously recorded RA neuron. Conjunctively with the bursts, there is a transient reduction in firing rate of the nonbursting neuron (black curve, n = 50 RA neuron pairs). The model in which RA inhibition suppresses spontaneous firing (red curve) is able to reproduce this transient reduction, but the model in which RA neurons display a soft refractory period after bursts (blue curve) is not. p = 6/7, q = 39/40, LR = 12, pR = 1, DR = 240 ms, and pb = 0.
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pcbi-0030249-g007: RA-Intrinsic Dynamics and Inhibition(A) When RA burst sequences extend beyond HVCI sequences by a random time uniformly distributed in the interval 0–15 ms, then the left flank of the average RA–HVCI CSP function gets uncharacteristically wide (arrows).(B) Transitive suppression of tonic firing in RA neurons is explained by RA inhibition. Shown are average RA IFR curves in 1.2 s time windows in which one RA neuron does not fire a burst, and time-aligned to burst onset in a simultaneously recorded RA neuron. Conjunctively with the bursts, there is a transient reduction in firing rate of the nonbursting neuron (black curve, n = 50 RA neuron pairs). The model in which RA inhibition suppresses spontaneous firing (red curve) is able to reproduce this transient reduction, but the model in which RA neurons display a soft refractory period after bursts (blue curve) is not. p = 6/7, q = 39/40, LR = 12, pR = 1, DR = 240 ms, and pb = 0.

Mentions: In our model, RA neurons are simply driven by HVCRA bursts. To test for the possibility that RA burst sequences can be self-sustaining due to recurrent RA circuitry and in the absence of HVC drive, we performed model simulations in which after each transition into the ground state, RA burst sequences continued to propagate for a random duration uniformly distributed in the time interval 0–15 ms. By doing this, RA neurons produced less than 4% additional burst spikes compared to before. Despite this small addition of spikes, average CSP functions of RA–HVCI pairs became unrealistically heavy at negative time lags, Figure 7A. This behavior was very robust, though it obviously depended on the estimated HVCRA spike propagation time tR = 4 ms; see Methods and [24]. To assess the relevance of RA intrinsic dynamics in a manner independent of spike-propagation estimates, we removed single spikes in RA neurons (these are spikes forming ISI pairs of more than 10 ms each). Thus-formed RA–HVCI CSP functions (with single RA spikes removed) displayed a high peak that in fact could not be reproduced with any set of model parameters p and q unless RA links were correlated with HVCI links (good agreement could be achieved when RA neurons were linked to 13 among the 56 HVCRA groups to which HVCI neurons were linked). Thus, rather than finding evidence for RA intrinsic dynamics, we found the contrary evidence that in order to explain the non-lagging and strong RA–HVCI correlations, RA neurons must be preferentially linked to and driven by the same HVCRA groups as are HVCI neurons.


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

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

RA-Intrinsic Dynamics and Inhibition(A) When RA burst sequences extend beyond HVCI sequences by a random time uniformly distributed in the interval 0–15 ms, then the left flank of the average RA–HVCI CSP function gets uncharacteristically wide (arrows).(B) Transitive suppression of tonic firing in RA neurons is explained by RA inhibition. Shown are average RA IFR curves in 1.2 s time windows in which one RA neuron does not fire a burst, and time-aligned to burst onset in a simultaneously recorded RA neuron. Conjunctively with the bursts, there is a transient reduction in firing rate of the nonbursting neuron (black curve, n = 50 RA neuron pairs). The model in which RA inhibition suppresses spontaneous firing (red curve) is able to reproduce this transient reduction, but the model in which RA neurons display a soft refractory period after bursts (blue curve) is not. p = 6/7, q = 39/40, LR = 12, pR = 1, DR = 240 ms, and pb = 0.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-0030249-g007: RA-Intrinsic Dynamics and Inhibition(A) When RA burst sequences extend beyond HVCI sequences by a random time uniformly distributed in the interval 0–15 ms, then the left flank of the average RA–HVCI CSP function gets uncharacteristically wide (arrows).(B) Transitive suppression of tonic firing in RA neurons is explained by RA inhibition. Shown are average RA IFR curves in 1.2 s time windows in which one RA neuron does not fire a burst, and time-aligned to burst onset in a simultaneously recorded RA neuron. Conjunctively with the bursts, there is a transient reduction in firing rate of the nonbursting neuron (black curve, n = 50 RA neuron pairs). The model in which RA inhibition suppresses spontaneous firing (red curve) is able to reproduce this transient reduction, but the model in which RA neurons display a soft refractory period after bursts (blue curve) is not. p = 6/7, q = 39/40, LR = 12, pR = 1, DR = 240 ms, and pb = 0.
Mentions: In our model, RA neurons are simply driven by HVCRA bursts. To test for the possibility that RA burst sequences can be self-sustaining due to recurrent RA circuitry and in the absence of HVC drive, we performed model simulations in which after each transition into the ground state, RA burst sequences continued to propagate for a random duration uniformly distributed in the time interval 0–15 ms. By doing this, RA neurons produced less than 4% additional burst spikes compared to before. Despite this small addition of spikes, average CSP functions of RA–HVCI pairs became unrealistically heavy at negative time lags, Figure 7A. This behavior was very robust, though it obviously depended on the estimated HVCRA spike propagation time tR = 4 ms; see Methods and [24]. To assess the relevance of RA intrinsic dynamics in a manner independent of spike-propagation estimates, we removed single spikes in RA neurons (these are spikes forming ISI pairs of more than 10 ms each). Thus-formed RA–HVCI CSP functions (with single RA spikes removed) displayed a high peak that in fact could not be reproduced with any set of model parameters p and q unless RA links were correlated with HVCI links (good agreement could be achieved when RA neurons were linked to 13 among the 56 HVCRA groups to which HVCI neurons were linked). Thus, rather than finding evidence for RA intrinsic dynamics, we found the contrary evidence that in order to explain the non-lagging and strong RA–HVCI correlations, RA neurons must be preferentially linked to and driven by the same HVCRA groups as are HVCI neurons.

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