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原文連結
論文資訊
- 類型：已發表論文
- 日期：2010
摘要
Neurons perform computations, and convey the results of those computations through the 統計 structure of their output spike trains. Here we present a practical method, grounded in the 資訊-theoretic analysis of prediction, for inferring a minimal representation of that structure and for characterizing its complexity. Starting from spike trains, our approach finds their causal state models (CSMs), the minimal hidden 馬可夫 models or 隨機 automata capable of generating 統計ly identical time series. We then use these CSMs to objectively quantify both the generalizable structure and the idiosyncratic randomness of the spike train. Specifically, we show that the expected algorithmic 資訊 content ( the 資訊 needed to describe the spike train exactly) can be split into three parts describing ( 1) the time-invar
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