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Fisher and Shannon information in finite neural populations (Yarrow et al. 2012)
 
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Model Information
Model File
Citations
Accession:
142990
Here we model populations of rate-coding neurons with bell-shaped tuning curves and multiplicative Gaussian noise. This Matlab code supports the calculation of information theoretic (mutual information, stimulus-specific information, stimulus-specific surprise) and Fisher-based measures (Fisher information, I_Fisher, SSI_Fisher) in these population models. The information theoretic measures are computed by Monte Carlo integration, which allows computationally-intensive decompositions of the mutual information to be computed for relatively large populations (hundreds of neurons).
Reference:
1 .
Yarrow S, Challis E, Seriès P (2012) Fisher and Shannon information in finite neural populations.
Neural Comput
24
:1740-80
[
PubMed
]
Model Information
(Click on a link to find other models with that property)
Model Type:
Connectionist Network;
Brain Region(s)/Organism:
Unknown;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment:
MATLAB;
Model Concept(s):
Rate-coding model neurons;
Implementer(s):
Yarrow, Stuart [s.yarrow at ed.ac.uk];
/
Yarrow2012
popcode
bootstrapErrorProp.m
cellsxfun.cpp
CircGaussNeurons.m
CosNeurons.m
degToRad.m
GaussNeurons.m
Neurons.m
OnlineStats.m
poisspdfln.m
radToDeg.m
StimulusEnsemble.m
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