Likelihood of a spike train

Description

This lecture covers the likelihood of a spike train in computational neuroscience, focusing on generative models and log-likelihood calculations based on observed spike trains. The instructor discusses the stochastic generation of spikes and the escape rate model, emphasizing the calculation of the likelihood that an experimental spike train could have been produced by a given model.

In MOOCs (2)

Instructor

Official source

https://mediaspace.epfl.ch/media/0_tvnc6cha

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In statistics, the likelihood principle is the proposition that, given a statistical model, all the evidence in a sample relevant to model parameters is contained in the likelihood function. A likelihood function arises from a probability density function considered as a function of its distributional parameterization argument.

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Neural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among the electrical activity of the neurons in the ensemble. Based on the theory that sensory and other information is represented in the brain by networks of neurons, it is thought that neurons can encode both digital and analog information.

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Biological neuron model

Biological neuron models, also known as a spiking neuron models, are mathematical descriptions of the properties of certain cells in the nervous system that generate sharp electrical potentials across their cell membrane, roughly one millisecond in duration, called action potentials or spikes (Fig. 2). Since spikes are transmitted along the axon and synapses from the sending neuron to many other neurons, spiking neurons are considered to be a major information processing unit of the nervous system.

Neuron

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