Likelihood-Based Approaches to Modeling the Neural Code
Likelihood-Based Approaches to Modeling the Neural Code
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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