Differentiable neuron simulations with biophysical detail on CPU, GPU, or TPU.
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Jul 30, 2025 - Python
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Differentiable neuron simulations with biophysical detail on CPU, GPU, or TPU.
Implementation of a Spiking Neural Network in Tensorflow.
Code for the assignments for the Computational Neuroscience Course BT6270 in the Fall 2018 semester
Neural simulations using Brian2 Python Package
A Hodgkin-Huxley model visualization for a neural tree
Implementation of Hodgkin-Huxley Spiking Neuron Model
Implementation of Neuron-model: Integrate-and-fire, Hodgkin–Huxley, Izhikevich, FitzHugh-Nagumo, Poisson Spike
This repository contains all material related to the course Computational Neuroscience (BT6270) in the Fall 2020 semester.
Investigates the mechanisms underlying epileptic seizures
Modelling Hodgkin-Huxley neural response with dynamic input
Model of Motor Network during Parkinson's Disease and Deep Brain Stimulation. The model simulates LFP, EMG and associated force signals from the motor system. A multivariable adaptive control strategy is implemented in the model to control two biomarkers of Parkinsonian tremor and motor impairment symptoms.
Code for the paper "Stochastic analysis of the electromagnetic induction effect on a neuron's action potential dynamics"
Model 3 HH neurons connected in different motifs and different axonal delays. Compute synchronization between spikes and information flow between them.
Physiological Models
an implementation of Hodgkin-Huxley model using python package numpy and brian2
Hodgkin and Huxley neuron model using Simulink and MATLAB. The Hodgkin and Huxley model is a mathematical representation of the electrical activity in a neuron, capturing the dynamics of ion channels and membrane potential.
Python scripts supporting a tutorial on the Hodgkin-Huxley model.
Large-scale thalamocortical network model for simulating physiological and paroxysmal brain rhythms: version 1
KU ELEC 436 - Bioelectronics
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