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]
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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): 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;
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%                           GenerateSaltPepper.m                          %
%                           --------------------                          %
% copyright            : (C) 2013 by Jesus Garrido                        %
% email                : jesus.garrido@unipv.it                           %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function SpikesMatrix=GenerateSaltPepper(MFInit, MFNumber, TimeInit, Duration, Frequency)
    SpikesPerNeuron = floor(Frequency*Duration);
    Times = rand(SpikesPerNeuron,MFNumber)*Duration+TimeInit;
    Neurons = repmat(MFInit:(MFInit+MFNumber-1),1,SpikesPerNeuron);
    SpikesMatrix = [transpose(Times(:)); Neurons];
    
    SpikesMatrix = transpose(SpikesMatrix);