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Bayesian BrainProbabilistic Approaches to Neural Coding$
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Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P.N. Rao

Print publication date: 2006

Print ISBN-13: 9780262042383

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262042383.001.0001

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Likelihood-Based Approaches to Modeling the Neural Code

Likelihood-Based Approaches to Modeling the Neural Code

Chapter:
(p.53) 3 Likelihood-Based Approaches to Modeling the Neural Code
Source:
Bayesian Brain
Author(s):

Jonathan Pillow

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262042383.003.0003

This chapter discusses likelihood-based approaches to building mathematical models of the neural code. It introduces probabilistic neural models such as the linear-non-linear-Poisson (LNP) model (models of neural response), the generalized linear model (GLM), and the generalized integrate-and-fire (GIF) model. The chapter also examines the methods of evaluating the validity of probabilistic models, which includes cross-validation, time rescaling, and model-based decoding.

Keywords:   likelihood-based approaches, neural code, Poisson model, LNP, generalized linear model, GLM, integrate and fire, GIF, cross-validation, time rescaling

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