Convergence regulates synchronization-dependent AP transfer in feedforward NNs (Sailamul et al 2017)

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Accession:241979
We study how synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. We implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.
Reference:
1 . Sailamul P, Jang J, Paik SB (2017) Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks. J Comput Neurosci 43:189-202 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Synapse;
Brain Region(s)/Organism:
Cell Type(s): Hodgkin-Huxley neuron;
Channel(s): I Sodium; I Potassium; I T low threshold; I Cl, leak;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Synchronization; Oscillations; Action Potentials; Activity Patterns; Information transfer; Synaptic Convergence;
Implementer(s): Sailamul, Pachaya [pachaya_sailamul at brown.edu]; Jang, Jaeson ; Paik, Se-Bum ;
Search NeuronDB for information about:  I T low threshold; I Sodium; I Potassium; I Cl, leak;
function [varargout ] = GetCellsDistInfo( locTxt , PLOT)
%Return Matrix for position of the specific neural mosaics
%   locTxt : Input text file of location of neuronal mosaic

if(nargin == 1)
    PLOT = 1;
end

NNloc= importdata([locTxt '.txt']); 
ncells = NNloc(1); NNloc = NNloc(2:end);

Cellpos = reshape(NNloc,2,ncells)';
if(PLOT)
figure; scatter(Cellpos(:,1),Cellpos(:,2),20); axis square;
xlabel('um');  ylabel('um'); title('Distribution of cell position');
end

Distmat = squareform (pdist(Cellpos));
tmpnn = sort(Distmat,2);
nnDist = tmpnn(:,2);

if(PLOT)
figure; hist(nnDist,25); 
xlabel('Nearest Neighbor Distance (um)');
ylabel('# of Cells'); 
title({'Distribution of nearest neighbor distance', ['Mean = ' num2str(mean(nnDist)) ', std = ' num2str(std(nnDist))]});
end


switch nargout
    case 1 
        varargout = {Cellpos};
    case 2
        varargout = {Cellpos, Distmat};
    case 3
        varargout = {Cellpos, Distmat, nnDist};
    otherwise 
          varargout = {Cellpos, Distmat, nnDist};
end

end


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