Code for general-purpose neural simulation
I use a lot of open-source simulators / code by others: especially Nengo and Brian 2.0. I mostly use the python-numpy-scipy-matplotlib stack. Earlier, I've used MOOSE and NEURON for biological neural network simulations, and Keras and TensorFlow for ML dabbling.
I have also written some open-source code for neural simulation as below:
Rate-based neural network simulator with plastic synapses:
Integral and differential approaches to baseline and linear responses of populations of homogeneous neurons.
A neural theory (neurtheor) module at https://github.com/adityagilra/neurtheor: A collection of classes to compute baseline and linear responses of populations of homogeneous spiking neurons, using the integral or differential framework. See Gerstner et al book [ http://neuronaldynamics.epfl.ch/ ] for the spike response model of neurons and related integral approach. See Richardson 2007 [ http://link.aps.org/doi/10.1103/PhysRevE.76.021919 ] for the differential approach on the leaky integrate and fire neuron model.
https://github.com/adityagilra/2015_spiking_population_response: (uses my neurtheor module - above) Calculate and plot response of a population of spiking neurons (spike response model) to a constant and a sinusoid riding on top. Population responses calculated via 3 different methods are compared: 1) via simulation (brian2); 2) via linear response theory using the integral approach (do_integral_approach=True) explained in Gerstner et al book [ http://neuronaldynamics.epfl.ch/ ]; and 3) for a LIF neuron, you can also calculate the population response using linear response theory using the differential approach (do_differential_approach=True) from Richardson 2007 [ http://link.aps.org/doi/10.1103/PhysRevE.76.021919 ].
See also the tutorials that I developed for various international summer schools.
Code related to my publications is linked on the publications page.
View all my public code on github.