Neural Comput - Hidden Markov models for the stimulus-response relationships of multistate neural systems.

Tópicos

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Resumo

Given recent experimental results suggesting that neural circuits may evolve through multiple firing states, we develop a framework for estimating state-dependent neural response properties from spike train data. We modify the traditional hidden Markov model (HMM) framework to incorporate stimulus-driven, non-Poisson point-process observations. For maximal flexibility, we allow external, time-varying stimuli and the neurons' own spike histories to drive both the spiking behavior in each state and the transitioning behavior between states. We employ an appropriately modified expectation-maximization algorithm to estimate the model parameters. The expectation step is solved by the standard forward-backward algorithm for HMMs. The maximization step reduces to a set of separable concave optimization problems if the model is restricted slightly. We first test our algorithm on simulated data and are able to fully recover the parameters used to generate the data and accurately recapitulate the sequence of hidden states. We then apply our algorithm to a recently published data set in which the observed neuronal ensembles displayed multistate behavior and show that inclusion of spike history information significantly improves the fit of the model. Additionally, we show that a simple reformulation of the state space of the underlying Markov chain allows us to implement a hybrid half-multistate, half-histogram model that may be more appropriate for capturing the complexity of certain data sets than either a simple HMM or a simple peristimulus time histogram model alone.

Resumo Limpo

given recent experiment result suggest neural circuit may evolv multipl fire state develop framework estim statedepend neural respons properti spike train data modifi tradit hidden markov model hmm framework incorpor stimulusdriven nonpoisson pointprocess observ maxim flexibl allow extern timevari stimuli neuron spike histori drive spike behavior state transit behavior state employ appropri modifi expectationmaxim algorithm estim model paramet expect step solv standard forwardbackward algorithm hmms maxim step reduc set separ concav optim problem model restrict slight first test algorithm simul data abl fulli recov paramet use generat data accur recapitul sequenc hidden state appli algorithm recent publish data set observ neuron ensembl display multist behavior show inclus spike histori inform signific improv fit model addit show simpl reformul state space under markov chain allow us implement hybrid halfmultist halfhistogram model may appropri captur complex certain data set either simpl hmm simpl peristimulus time histogram model alon

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