<em>Estimating nonstationary input signals from a single neuronal spike train</em>,

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タイトル別名
  • Estimating nonstationary input signals from a single neuronal spike train
公開日
2012-11
資源種別
journal article
権利情報
  • ©2012 American Physical Society
DOI
  • 10.1103/physreve.86.051903
公開者
American Physical Society (APS)

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説明

Neurons temporally integrate input signals, translating them into timed output spikes. Because neurons nonperiodically emit spikes, examining spike timing can reveal information about input signals, which are determined by activities in the populations of excitatory and inhibitory presynaptic neurons. Although a number of mathematical methods have been developed to estimate such input parameters as the mean and fluctuation of the input current, these techniques are based on the unrealistic assumption that presynaptic activity is constant over time. Here, we propose tracking temporal variations in input parameters with a two-step analysis method. First, nonstationary firing characteristics comprising the firing rate and non-Poisson irregularity are estimated from a spike train using a computationally feasible state-space algorithm. Then, information about the firing characteristics is converted into likely input parameters over time using a transformation formula, which was constructed by inverting the neuronal forward transformation of the input current to output spikes. By analyzing spike trains recorded in vivo, we found that neuronal input parameters are similar in the primary visual cortex V1 and middle temporal area, whereas parameters in the lateral geniculate nucleus of the thalamus were markedly different.

収録刊行物

  • Physical Review E

    Physical Review E 86 (5), 051903-, 2012-11

    American Physical Society (APS)

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