MicrocircuitDB: Hierarchical Gaussian Filter (HGF) model of conditioned hallucinations task (Powers et al 2017)

Hierarchical Gaussian Filter (HGF) model of conditioned hallucinations task (Powers et al 2017)

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Accession:229278

This is an instantiation of the Hierarchical Gaussian Filter (HGF) model for use with the Conditioned Hallucinations Task.
Reference:
1 . Powers AR, Mathys C, Corlett PR (2017) Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors. Science 357:596-600 [PubMed]

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Model Information (Click on a link to find other models with that property)

Model Type:
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment:	MATLAB;
Model Concept(s):	Hallucinations;
Implementer(s):	Powers, Al [albert.powers at yale.edu]; Mathys, Chris H ;

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hgfToolBox_condhalluc1.4

example_binary_input.txt

example_categorical_input.mat

example_usdchf.txt

tapas_autocorr.m

tapas_bayes_optimal.m

tapas_bayes_optimal_binary.m

tapas_bayes_optimal_binary_config.m

tapas_bayes_optimal_binary_transp.m

tapas_bayes_optimal_categorical.m

tapas_bayes_optimal_categorical_config.m

tapas_bayes_optimal_categorical_transp.m

tapas_bayes_optimal_config.m

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tapas_bayes_optimal_whatworld.m

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tapas_bayes_optimal_whichworld.m

tapas_bayes_optimal_whichworld_config.m

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tapas_bayesian_parameter_average.m

tapas_beta_obs.m

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tapas_boltzmann.m

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tapas_condhalluc_obs.m

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tapas_hgf_ar1.m

tapas_hgf_ar1_binary.m

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tapas_hgf_ar1_mab.m

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