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

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Accession:147461
" … 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. ... "
Reference:
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]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Working memory;
Implementer(s): 
To run the simulation start the file init.hoc

This will simulate the network model and reproduce the results
associated with Fig. 6 in:

Conde-Sousa E and Aguiar P, "A working memory model for serial order
that stores information in the intrinsic excitability properties of
neurons", JComputNeuroscience 2013

This is a network model with several neurons: the simulation therefore
takes a while(*)

A raster plot is automatically generated in NEURON after the
simulation. Alternativelly you can run the MATLAB script plotspikes.m
(or plotspikes_for_all_neurons.m) after the NEURON simulation is
completed to generate the raster in MATLAB.

In the raster plot:

- neurons with id from 0 to N_PRINCIPAL_NEURONS-1 are inhibitory
  interneurons
- neurons with id from N_PRINCIPAL_NEURONS to 2*N_PRINCIPAL_NEURONS-1
  are principal neurons
- all other are gate interneurons


(*)A smaller version of the network can be explored (lines 42-50 in
WMSeqLearn.hoc):

N_PRINCIPAL_NEURONS = 20                
GATES_SAMPLING_FRACTION = 1        
CONN_RATE = 0                                        
N_PATTERNS = 5                  
PATTERN_SIZE = 3