A NN with synaptic depression for testing the effects of connectivity on dynamics (Jacob et al 2019)

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Here we used a 10,000 neuron model. The neurons are a mixture of excitatory and inhibitory integrate-and-fire neurons connected with synapses that exhibit synaptic depression. Three different connectivity paradigms were tested to look for spontaneous transition between interictal spiking and seizure: uniform, small-world network, and scale-free. All three model types are included here.
1 . Jacob T, Lillis KP, Wang Z, Swiercz W, Rahmati N, Staley KJ (2019) A Proposed Mechanism for Spontaneous Transitions between Interictal and Ictal Activity. J Neurosci 39:557-575 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism: Mouse; Hippocampus;
Cell Type(s): Abstract integrate-and-fire neuron;
Channel(s): I_K,Na;
Gap Junctions:
Receptor(s): GabaA; Glutamate;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: C or C++ program;
Model Concept(s): Connectivity matrix; Epilepsy;
Implementer(s): Jacob, Theju ;
Search NeuronDB for information about:  GabaA; Glutamate; I_K,Na; Gaba; Glutamate;
The subfolder names uniform, smallworld, and scalefree refer to the network connectivity for the simulations contained in those folders.The code in the uniform folder has additional comments on the code, when compared to the other two. Each folder has their own READMEs as well. 

The name of the executable is macgregor. In each of the folders, you will also find the executables, scripts, and outputs for independent simulations with different values for the parameter of interest (neuron neighborhood for uniform, percentage of rewired connections in small world, and slope of line from scale free definitions for the scale free configuration).

Each of the folders contain: 1) Makefile 2) parameters.txt (used by the program to read in the various parameters) 3) main.cpp 4) neuron.cpp and neuron.h containing all of the code related to individual neurons 5) layer.cpp and layer.h containing all of the code related to the network layer. OpenMP is used to parallelize the for loops.

To create the executable, run the Makefile. To run the executable, do: ./macgregor < parameters.txt. The program writes out various parameters and outputs as can be seen in the code. The code for visualizing and analyzing the program output can be found in Matlab and Python folders of the root directory.

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