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

(Spaghetti) Implementation of Hodgkin-Huxley Spiking Neuron Model

Implementation of Neuron-model: Integrate-and-fire, Hodgkin–Huxley, Izhikevich, FitzHugh-Nagumo, Poisson Spike

Code for the paper "Stochastic analysis of the electromagnetic induction effect on a neuron's action potential dynamics"

This repository contains all material related to the course Computational Neuroscience (BT6270) in the Fall 2020 semester.

Modelling Hodgkin-Huxley neural response with dynamic input

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.

Model 3 HH neurons connected in different motifs and different axonal delays. Compute synchronization between spikes and information flow between them.

Physiological Models

KU ELEC 436 - Bioelectronics

Simulation of a Mathematical Model of Homeostatic Regulation of Sleep-Wake Cycles by Hypocretin/Orexin (Postnova et al., 2009)

NEUROFIT is a program that fits Hodgkin-Huxley models to voltage-clamp data.

Homeworks of Neuroscience of Learning, Memory and Cognition, taught by Dr. Hamid Karbalai Aghajan.

an implementation of Hodgkin-Huxley model using python package numpy and brian2

Hodgkin-Huxley, Logistic Map, Game of Life, Brian's Brain, Neuronal Cellular Automata, Morris-Lecar, Wilson-Cowan, EEG, fMRI

Large-scale thalamocortical network model for simulating physiological and paroxysmal brain rhythms: version 1