Neuronal Spike Train Entropy Estimation by History Clustering

Abstract

Neurons send signals to each other by means of sequences of action potentials (spikes). Ignoring variations in spike amplitude and shape that are probably not meaningful to a receiving cell, the information content, or entropy of the signal depends on only the timing of action potentials, and because there is no external clock, only the interspike intervals, and not the absolute spike times, are significant. Estimating spike train entropy is a difficult task, particularly with small data sets, and many methods of entropy estimation have been proposed. Here we present two related model-based methods for estimating the entropy of neural signals and compare them to existing methods. One of the methods is fast and reasonably accurate, and it converges well with short spike time records; the other is impractically time-consuming but apparently very accurate, relying on generating artificial data that are a statistical match to the experimental data. Using the slow, accurate method to generate a best-estimate entropy value, we find that the faster estimator converges to this value more closely and with smaller data sets than many existing entropy estimators.

Stimulus relevance modulates contrast adaptation in visual cortex

“…We identified potential spikes using an action potential detector described elsewhere ( Choi et al, 2006 ). Then, we performed a principal component analysis (PCA) for each channel using the open-source cluster analysis program KlustaKwik ( http://klusta-team.github.io/klustakwik/ ) ( Watters and Reeke, 2014 ). Clusters of potential spikes were determined based on the first three components of the PCA.…”

Variability and Randomness of the Instantaneous Firing Rate

“…For example multi-modal data with well separated peaks may have higher variability than uniformly distributed data where the outcomes are the least predictable. Shannon's entropy (Shannon and Weaver, 1949 ) is widely used to measure randomness (Steuer et al, 2001 ; McDonnell et al, 2011 ; Watters and Reeke, 2014 ), however it is not suitable for continuous distributions. Few other randomness measures based on entropy have been used in neural context recently.…”

Information Thermodynamics Derives the Entropy Current of Cell Signal Transduction as a Model of a Binary Coding System

“…In recent decades, the theoretical analysis of signal transduction has been broadly applied in various research fields, in parallel with significant development of information theory [1][2][3][4][5]. Informational thermodynamics for analyzing dynamic biochemical networks and systems biology have also been developed to assess the cell response to external stimuli [1][2][3][4][5][6].…”

“…In recent decades, the theoretical analysis of signal transduction has been broadly applied in various research fields, in parallel with significant development of information theory [1][2][3][4][5]. Informational thermodynamics for analyzing dynamic biochemical networks and systems biology have also been developed to assess the cell response to external stimuli [1][2][3][4][5][6]. In addition, in in vivo analysis, a significant amount of data on signal transduction has accumulated, and the quantitative analysis of a network of signaling cascades can be performed using new technology [7][8][9][10][11][12][13][14][15][16].…”