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estimating short-term synaptic plasticity using spikes
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in vitro data
stp_glm
LICENSE
LIF_SFA.m
LIFoutput.m
MarkramStimuli.m
README.md
corr_fast.m
facilitation_screenshot.png
inhomoPoiss.m
markram_response.m
short_hist.m
spike_poiss2.m
stp_demo.m
stp_demo.m~

README.md

Estimating short-term synaptic plasticity from pre- and postsynaptic spiking

for convinience all codes related to estimating STP parameters are in stp_glm folder

addpath(genpath('stp_glm'))

  • data for the in vitro experiment is available in in vitro data

Demo

stp_demo.m generates the pre and postsynaptic spiking activity in a connection with TM parameters in true_params and estimates the parameters using both TM-GLM and GBLM descibed in:

Ghanbari, A. & Malyshev, A. & Volgushev, M. & Stevenson, I. (2017) Estimating short-term synaptic plasticity from pre- and postsynaptic spiking (http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005738)

here we generated pre and postsynaptic spikes from an LIF neuron - replace that with your own data

[Tpre, Tpost] = LIFoutput(T,20,50,true_params,1);

a sample of estimated parameters for a facilitating neuron with only few hundreds of spikes and T=100 sec

Marginals

References

  • Acerbi, L. & Ma, W. J. (2017) Bayesian Adaptive Direct Search (BADS) optimization algorithm for model fitting in MATLAB (https://github.com/lacerbi/bads)
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