An Overview of Bayesian Methods for Neural Spike Train Analysis. (17th November 2013)

• Record Type: Journal Article
• Authors: Chen, Zhe
• Other Names: Wu Wei Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2013(2013)
• Publication Date: 2013-11-17
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2013/251905
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.