Pinned Repositories
2015_spiking_population_response
Calculate and plot response of a population of spiking neurons to a constant and a sinusoid riding on top via simulation (brian2) and theory (integral and differential approaches); uses my neurtheor module.
archibrain
Synthesize bio-plausible neural networks for cognitive tasks, mimicking brain architecture
BeliefStateRL
FOLLOW
Feedback-based Online Local Learning Of Weights (FOLLOW)
from-papers
Notes / derivations / models from papers that I have read since April 2014.
nengo_ocl
OpenCL-based simulator for Nengo neural models
neurtheor
NeuroTheory: A Python module to compute baseline and linear response of populations of spiking neurons in 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 LIF model.
olfactory-bulb
A detailed network model of part of the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra and Upinder S. Bhalla (Gilra A, Bhalla US (2015) Bulbar Microcircuit Model Predicts Connectivity and Roles of Interneurons in Odor Coding. PLoS ONE 10(5): e0098045.) The cell morphologies and network connections are in NeuroML v1.8.
PFC_MD_weights_stability
tutorials
Tutorials that I've given as a tutor at computational neuroscience summer schools
adityagilra's Repositories
adityagilra/archibrain
Synthesize bio-plausible neural networks for cognitive tasks, mimicking brain architecture
adityagilra/FOLLOW
Feedback-based Online Local Learning Of Weights (FOLLOW)
adityagilra/olfactory-bulb
A detailed network model of part of the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra and Upinder S. Bhalla (Gilra A, Bhalla US (2015) Bulbar Microcircuit Model Predicts Connectivity and Roles of Interneurons in Odor Coding. PLoS ONE 10(5): e0098045.) The cell morphologies and network connections are in NeuroML v1.8.
adityagilra/PFC_MD_weights_stability
adityagilra/tutorials
Tutorials that I've given as a tutor at computational neuroscience summer schools
adityagilra/2015_spiking_population_response
Calculate and plot response of a population of spiking neurons to a constant and a sinusoid riding on top via simulation (brian2) and theory (integral and differential approaches); uses my neurtheor module.
adityagilra/neurtheor
NeuroTheory: A Python module to compute baseline and linear response of populations of spiking neurons in 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 LIF model.
adityagilra/BeliefStateRL
adityagilra/from-papers
Notes / derivations / models from papers that I have read since April 2014.
adityagilra/nengo_ocl
OpenCL-based simulator for Nengo neural models
adityagilra/rateSimulator
Simple python simulator for 'rate neurons' with continuous-valued, rectified output, connected by static/plastic synapses.
adityagilra/simple-A2C
A simple A2C made from scratch in PyTorch. Accompanying comic at https://hackernoon.com/intuitive-rl-intro-to-advantage-actor-critic-a2c-4ff545978752
adityagilra/TreeHMM-local
Provided is the code and accompanying documentation for inferring the parameters (via the Baum-Welch algorithm; see Prentice et al., 2016) of the Tree hidden Markov model (HMM) for neural population spike train data. This version can be used locally directly in Matlab, via mex files, or in Python via `make` using the Makelfile..
adityagilra/UnsupervisedLearningNeuralData
Finding or learning latent modes / clusters in neural spiking data
adityagilra/BiologicallyPlausibleLearningRNN
adityagilra/DDPG-PyTorch
Deep Deterministic Policy Gradient implemented in PyTorch for DeepMind Control Suite
adityagilra/FOLLOWControl
Motor control using inverse model learned via FOLLOW
adityagilra/gym
A toolkit for developing and comparing reinforcement learning algorithms.
adityagilra/Isca
Idealized GCM from the University of Exeter
adityagilra/meld
MELD: Meta-Reinforcement Learning from Images via Latent State Models https://arxiv.org/abs/2010.13957
adityagilra/nengo
A Python library for creating and simulating large-scale brain models
adityagilra/nengo_mpi
MPI backend for the nengo neural simulator.
adityagilra/PyNN
A Python package for simulator-independent specification of neuronal network models.
adityagilra/RCESN_spatio_temporal
Spatio-temporal forecasting of Lorenz96 with RC-ESN, RNN-LSTM and ANN
adityagilra/spinningup
An educational resource to help anyone learn deep reinforcement learning.
adityagilra/studywolf_control
A repository for control benchmarking code
adityagilra/VIWTA-SNN
Implementation in C++ (mexed for local callable use in Matlab, now in Python too) of the variational inference winner-take-all neural circuit model for real-time unsupervised clustering.