Unsupervised Detection of Cell-Assembly Sequences by Similarity-Based Clustering.

ABSTRACT: Neurons which fire in a fixed temporal pattern (i.e., "cell assemblies") are hypothesized to be a fundamental unit of neural information processing. Several methods are available for the detection of cell assemblies without a time structure. However, the systematic detection of cell assemblies with time structure has been challenging, especially in large datasets, due to the lack of efficient methods for handling the time structure. Here, we show a method to detect a variety of cell-assembly activity patterns, recurring in noisy neural population activities at multiple timescales. The key innovation is the use of a computer science method to comparing strings ("edit similarity"), to group spikes into assemblies. We validated the method using artificial data and experimental data, which wer

SUBMITTER: Watanabe K 

PROVIDER: S-EPMC6554434 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature