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# V1Novelty | |
## A spiking neural network model to investigate cortical novelty responses | |
This project is concerned with the underlying mechanisms that give rise to novelty responses in the primary visual cortex as reported in Homann et al., BioRxiv 2017. | |
We set up a spiking neural network model of the mouse primary visual cortex (80% excitatory, 20% inhibitory) using various plasticity mechanisms. | |
Some of the simulation code follows code provided by Litwin-Kumar and Doiron [1]. | |
The code is written in Julia (https://julialang.org/, https://github.com/JuliaLang/julia). | |
Required packages: | |
1. PyPlot | |
2. HDF5 | |
3. Distributions | |
4. Dates | |
5. LinearAlgebra | |
6. Random | |
Packages can be installed via `using Pkg` `Pkg.add("PackageName")`. | |
The project is work in progress and will be updated continuously. | |
Project strucutre | |
* `main` - contains initialisation files that start the simulation (to be run on cm) and evaluation (to be run locally - no plotting on cm possible) | |
* `simulation` - contains the main simulation and simulation helper files | |
* `evaluation` - contains the main evaluation (weight, spiketime) and evaluation helper files | |
At this point plotting is still included directly in the evaluation files. | |
The data is stored on the server using the `hdf5` file format. | |
To avoid many small files on the server the analysis files are stored locally. | |
[1] A. Litwin-Kumar & B. Doiron. Formation and maintenance of neuronal assemblies through synaptic plasticity. Nature Communications (2014). | |
Copyright notice: | |
http://lk.zuckermaninstitute.columbia.edu/ | |
litwin-kumar_doiron_formation_2014 | |
Copyright (C) 2014 Ashok Litwin-Kumar | |