code demos for primitives of spiking neural networks
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Updated
May 3, 2021 - Jupyter Notebook
code demos for primitives of spiking neural networks
Repo of the bachelor thesis 'Dynamic memory traces for sequence learning in spiking networks'
A few Colab Notebooks that can be used to get started with pyNN and test virtual sPyNNaker without many issues.
Cerebellum learning to perform the VOR.
Accelerated SNN simulator for neuromorphic computing and research in Python
Network model of Rosenbaum et al. (2017) reimplemented in Brian 2. Developed as a course project during OCNC2017.
A neuroscientific sequence learning model on spiking neural networks with winner-take-all circuits and lateral inhibition. Written using the NEST neural simulator and custom neuron/synapse models.
Simulation of a real time spiking neural network
Runs networkx graphs representing spiking neural networks of LIF-neurons on lava-nc or networkx.
This repository contains all codes necessary to reproduce figures and results reported in Stein, Barbosa et al. (Nature Communications, 2020) from the raw data acquired in human behavioral experiments (data included in the repository), and from the relevant model simulations.
Bio-inspired spiking-neural-network framework on an autonomous robot car.
A simple experiment to compare Artificial and Spiking Neural Networks in Sequential and Few-Shot Learning.
Demo: Spiking Neural Network (SNN) using Generalised Linear Model (GLM)
Self-Control Reservoir Network is a neural network concept that inspires from the human brain and from Reservoir Computing, designed for a better and smarter computing network
Spiking Neural Network implementation in pure C++ with minimal dependencies
A demo for robotic control loop with Intel's Loihi neuromorphic chip used in our papers
Showcases of Spiking Neural Network, which have Synaptic Plasticity
Python and ROS implementation of an SNN on Intel's Loihi neuromorphic processor mimicking the oculomotor system controlling a biomimetic robotic head
Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks
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