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Distributed representation of perceptual categories in the auditory cortex (Kim and Bao 2008)
Accession: 113459
Examines the hypothesis that enlargement in cortical stimulus representation is a mechanism of categorical perception. Categorical perception is tested using discrimination and identification ability.
Reference: Kim H, Bao S (2008) Distributed representation of perceptual categories in the auditory cortex. J Comput Neurosci 24:277-90 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type:  Network;
Brain Region(s)/Organism:  Auditory cortex;
Cell Type(s):   
Channel(s):   
Gap Junctions:  
Receptor(s):  
Gene(s):  
Transmitter(s):  
Simulation Environment:  MATLAB;
Model Concept(s):  Perceptual Categories;
Implementer(s):  Bao, Shaowen [sbao at berkeley.edu]; Kim, Heesoo [heesoo at berkeley.edu];
Model files   Download zip file             Help downloading and running models
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modelDB
README.txt
categorization_llikhd.m
discrimination_MLE.m
discr_temp.mat
get_params.m
ident_temp.mat
likhood.m
MNRRS.m
binornd_sim.m
smth_gass_distr.m
                            
The following m files are included:

discrimination_MLE.m
    Calculates the discrimination ability of a given population of neurons.
    This program may take an hour to run
categorization_llikhd.m
    Calculates the identification ability of a given population of neurons.
    This program may take 4 minutes to run.

    Both scripts above use the following helper functions:
        MNRRS.m
            Gets the response of the population of neurons to a specific frequency.
        likhood.m
            The likelihood function (equation 2 in manuscript)
        get_params.m
            The parameters used for the simulation (population parameters and testing parameters)
            Editing this file will suffice to change population or testing parameters
        smth_gass_distr.m
            Helper function for get_params to redefine over-representation.

    categorization_llikhd also includes:
        binornd_sim.m
            The Bernoulli random process simulation (Eq 5 in paper)


In addition, the following mat files are included:

discr_temp.mat
	output expected at line 93 of discrimination_MLE
	to plot, use lines 98-103

ident_temp.mat             
	output expected at line 68 of categorization_llikhd
	to plot, use lines 73-78


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