Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)

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Accession:230138
We simulate a LIF neuron equipped with STDP. A pattern repeats in its inputs. The LIF progressively becomes selective to the repeating pattern, in an optimal manner.
Reference:
1 . Masquelier T (2017) STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons Neuroscience, in press, accepted manuscript
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Coincidence Detection; STDP; Unsupervised Learning; Hebbian plasticity; Long-term Synaptic Plasticity; Pattern Recognition; Spatio-temporal Activity Patterns;
Implementer(s): Masquelier, Tim [timothee.masquelier at alum.mit.edu];
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STDP
data
src
README.txt
                            
This code was used in: Masquelier T (2017) STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons. Neuroscience.
https://doi.org/10.1016/j.neuroscience.2017.06.032
Jan 2017
timothee.masquelier@cnrs.fr

The code is in STDP/src
The main script in STDP/src/main.m
It has a long header with some info.

plots.m can be launched afterwards to analyze the results.

perf.m computes some performance indicators
mean_perf.m averages batch results (after running batch.py)

batch.py is a Python script that launches multiple threads of main.m with different random seeds (see its header for more information)

All the numerical parameters are in STDP/src/param.m

The data is read/written in STDP/data/


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