Distributed synaptic plasticity and spike timing (Garrido et al. 2013)

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Accession:149913
Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular layer network. In response to mossy fiber bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time scale and allows the cerebellar granular layer to flexibly control burst transmission along the mossy fiber pathway.
Reference:
1 . Garrido JA, Ros E, D'Angelo E (2013) Spike timing regulation on the millisecond scale by distributed synaptic plasticity at the cerebellum input stage: a simulation study. Front Comput Neurosci 7:64 [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): Cerebellum interneuron granule GLU cell; Cerebellum golgi cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB; EDLUT;
Model Concept(s): Long-term Synaptic Plasticity;
Implementer(s): Garrido, Jesus A [jesus.garrido at unipv.it];
Search NeuronDB for information about:  Cerebellum interneuron granule GLU cell;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                           ShowRasterPlot.m                              %
%                           ---------------                               %
% copyright            : (C) 2013 by Jesus Garrido                        %
% email                : jesus.garrido@unipv.it                           %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function ShowRasterPlot(ResultFile, NumMFCells, NumGCCells, NumGoCells, InitInterval, FinishInterval)

%% Cells numbers
NeuronMF_MZ1 = 0;
NeuronGC_MZ1 = NeuronMF_MZ1+NumMFCells+1;
NeuronGO_MZ1 = NeuronGC_MZ1+NumGCCells+1;
NeuronSC_MZ1 = NeuronGO_MZ1+NumGoCells+1;

%% Load output file
Spikes = load(ResultFile);

% Plot MF activity
iref=find(Spikes(:,2)>=NeuronMF_MZ1);
SpikesRef = Spikes(iref,:);
iref=find(SpikesRef(:,2)<NeuronGC_MZ1);
SpikesRef = SpikesRef(iref,1:2);
figure;
plot(SpikesRef(:,1),SpikesRef(:,2),'.b');
axis([InitInterval FinishInterval NeuronMF_MZ1-1 NeuronGC_MZ1+1]);
title('Mossy Fibers');
xlabel('Time (s)');
ylabel('Cell Number');
    
% Plot GrC activity
iref=find(Spikes(:,2)>=NeuronGC_MZ1);
SpikesRef = Spikes(iref,:);
iref=find(SpikesRef(:,2)<NeuronGO_MZ1);
SpikesRef = SpikesRef(iref,1:2);
figure;
plot(SpikesRef(:,1),SpikesRef(:,2),'.r');
axis([InitInterval FinishInterval NeuronGC_MZ1-1 NeuronGO_MZ1+1]);
title('Granule Cells');
ylabel('Cell Number');
xlabel('Time (s)');

% Plot GoC activity
iref=find(Spikes(:,2)>=NeuronGO_MZ1);
SpikesRef = Spikes(iref,:);
iref=find(SpikesRef(:,2)<NeuronSC_MZ1);
SpikesRef = SpikesRef(iref,1:2);
figure;
plot(SpikesRef(:,1),SpikesRef(:,2),'.g');
axis([InitInterval FinishInterval NeuronGO_MZ1-1 NeuronSC_MZ1+1]);
title('Golgi Cells');
ylabel('Cell Number');
xlabel('Time (s)');

clear iref;
clear SpikesRef;

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