Storing serial order in intrinsic excitability: a working memory model (Conde-Sousa & Aguiar 2013)

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" … Here we present a model for working memory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. We show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity. … The presented model exhibits properties which are in close agreement with experimental results in working memory. ... "
1 . Conde-Sousa E, Aguiar P (2013) A working memory model for serial order that stores information in the intrinsic excitability properties of neurons. J Comput Neurosci 35:187-99 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Simulation Environment: NEURON;
Model Concept(s): Working memory;
Generate a random permutation vector

vdest = randperm( r, N )
vdest is a Vector() class obj
r is a Random() class obj used to generate a random stream of values

N = 10
objref r, vdest
r = new Random()
r.uniform(0.0, 1.0)
vdest = randperm( r, N )
vdest.printf //if you want to visualize the result

Could be easily implemented using the modern version of the Fisher–Yates shuffle but since hoc has a vector sort function (hopefully computationally efficient), this saves my time...

Paulo de Castro Aguiar, 2012

obfunc randperm() {localobj dummyvec
		dummyvec = new Vector($2)
		return dummyvec.sortindex

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