Computational Classification Approach to Profile Neuron Subtypes from Brain Activity Mapping Data.

ABSTRACT: The analysis of cell type-specific activity patterns during behaviors is important for better understanding of how neural circuits generate cognition, but has not been well explored from in vivo neurophysiological datasets. Here, we describe a computational approach to uncover distinct cell subpopulations from in vivo neural spike datasets. This method, termed "inter-spike-interval classification-analysis" (ISICA), is comprised of four major steps: spike pattern feature-extraction, pre-clustering analysis, clustering classification, and unbiased classification-dimensionality selection. By using two key features of spike dynamic - namely, gamma distribution shape factors and a coefficient of variation of inter-spike interval - we show that this ISICA method provides invariant classification

SUBMITTER: Li M 

PROVIDER: S-EPMC4515637 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature