An Overview of Bayesian Methods for Neural Spike Train Analysis
Name
CIN.2013.251905.pdf
Size
737.83 KB
Format
Adobe PDF
Checksum (MD5)
5af1ea6ff89e1b2b04cb1695e9054b24
Author(s)
Chen, Zhe
Date Issued
2013
Journal
Computational Intelligence and Neuroscience
Publisher
Hindawi Publishing Corporation
Citation
Chen, Zhe. “An Overview of Bayesian Methods for Neural Spike Train Analysis.” Computational Intelligence and Neuroscience 2013 (2013): 1–17.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Picower Institute for Learning and Memory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1155/2013/251905
