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Oscillation and coding in a proposed NN model of insect olfaction (Horcholle-Bossavit et al. 2007)

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"For the analysis of coding mechanisms in the insect olfactory system, a fully connected network of synchronously updated McCulloch and Pitts neurons (MC-P type) was (previously) developed. ... Considering the update time as an intrinsic clock, this “Dynamic Neural Filter” (DNF), which maps regions of input space into spatio-temporal sequences of neuronal activity, is able to produce exact binary codes extracted from the synchronized activities recorded at the level of projection neurons (PN) in the locust antennal lobe (AL) in response to different odors ... We find synaptic matrices which lead to both the emergence of robust oscillations and spatio-temporal patterns, using a formal criterion, based on a Normalized Euclidian Distance (NED), in order to measure the use of the temporal dimension as a coding dimension by the DNF. Similarly to biological PN, the activity of excitatory neurons in the model can be both phase-locked to different cycles of oscillations which (is reminiscent of the) local field potential (LFP), and nevertheless exhibit dynamic behavior complex enough to be the basis of spatio-temporal codes."
1 . Horcholle-Bossavit G, Quenet B, Foucart O (2007) Oscillation and coding in a formal neural network considered as a guide for plausible simulations of the insect olfactory system. Biosystems 89:244-56 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Pattern Recognition; Oscillations; Spatio-temporal Activity Patterns; Olfaction;
% readme.txt for OlfactmodelBioSystems %%%

The archive contains matlab files that
simulate the model for Antennal lobe model that we developed and
reported in the paper:

Oscillation and coding in a formal neural network considered as a
guide for plausible simulations of the insect olfactory system.
Horcholle-Bossavit G, Quenet B, Foucart O.
Biosystems. 2007;89(1-3):244-56. 

Please cite this paper if you publish any research results obtained
with this code or any modified versions of this code.

Copy all the m-files to a single directory

The Matlab scripts

   Fig2.m   Fig3.m   Fig4.m   Fig5.m   Fig6.m   Fig7.m

can be run to generate figures like those of Horcholle-Bossavit et

Note that the stochastic models will give slightly different results
every time they are run, so the figures generated with this code will
not match the published figures exactly.

A random simulation trial can be run with Progsim.m

A single simulation with results corresponding to a specific value of
NED can be run with Simulnum.m

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