Spike Correlations in a Songbird Agree with a Simple Markov Population Model
Spike Correlations in a Songbird Agree with a Simple Markov Population Model
复制标题
鸣禽的尖峰相关性与简单的马尔可夫种群模型一致
DOI:
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发表时间:
2007
期刊:
影响因子:
--
通讯作者:
Richard Hans Robert Hahnloser
中科院分区:
文献类型:
--
作者:
Andrea P. Weber;Richard Hans Robert Hahnloser
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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