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

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Accession:123986
"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."
Reference:
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):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Pattern Recognition; Oscillations; Spatio-temporal Activity Patterns; Olfaction;
Implementer(s):
%BioSystems 89(2007) 244-256
%G.Horcholle-Bossavit et al.
%Fig.5

global  vper vosc vdisv 
global  Kex  Kr 

Litpar('par22h20_10_11')
Litsim('sim22h20_10_11') 

Nsim=4000;      %number of simulations 
nKr=40;         %number of Kr values 
nKex=100 ;      %number of Kex values 

vper=vper(1:Nsim);
vdisv = vdisv (1:Nsim);
Kex=Kex(1:Nsim);
Kr=Kr(1:Nsim);

matper=reshape(vper,nKr,nKex);
matdisv=reshape(vdisv,nKr,nKex);
matkex=reshape(Kex,nKr,nKex);
matkr=reshape(Kr,nKr,nKex);

subplot(2,2,1)
contourf(matkex,matkr,matdisv)
set(gcf,'Color',[1 1 1])
set(gca,'Xlim',[1 30])
colorbar('vert')
subplot(2,2,3)
contourf(matkex,matkr,matper)
set(gcf,'Color',[1 1 1])
set(gca,'Xlim',[1 30])
colorbar('vert')

Litpar('par17h16_14_11')
Litsim('sim17h16_14_11') 


Nsim=4000;
nKr=40;
nKex=100 ;

vosc=vosc(1:Nsim);
vdisv = vdisv (1:Nsim);
Kex=Kex(1:Nsim);
Kr=Kr(1:Nsim);

matosc=reshape(vosc,nKr,nKex);
matdisv=reshape(vdisv,nKr,nKex);
matkex=reshape(Kex,nKr,nKex);
matkr=reshape(Kr,nKr,nKex);

subplot(2,2,2)
contourf(matkex,matkr,matdisv)
set(gcf,'Color',[1 1 1])
set(gca,'Xlim',[1 30],'CLim',[0.0065 0.7])
colorbar('vert')
subplot(2,2,4)
contourf(matkex,matkr,matosc)
set(gcf,'Color',[1 1 1])
set(gca,'Xlim',[1 30])
colorbar('vert')



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