A computational neurocience project. The aim of the project was to formulate a model accounting with the spatial distribution of neurons via propagation delays. First, aspiking neural network is built, then STDP synaptic plasticity rule is implemented and then modified. Then a study of how the spatial distribution and the external stimuli affects final state of the network after the learning process is done. Finally, multiple optimization techniques are tackled such as the JIT python implementation, parallelization, sampling and some mathematical tricks in order to reduce the computational cost of the simulations.
Unfortunately, this project is protected by a confidentiality agreement due to the future publication of itself. Only some generic details will be shown.