Journal
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
Volume 2013, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2013/251905
Keywords
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Funding
- Mathematical Biosciences Institute, Ohio State University
- NSF-IIS CRCNS (Collaborative Research in Computational Neuroscience) Grant from the National Science Foundation [1307645]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1307645] Funding Source: National Science Foundation
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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.
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