Convolutional spiking neural network implementing STDP

Convolutional spiking neural network implementing voltage-dependent synaptic plasticity and single-spike integrate-and-fire neurons

Neuron++ is a library which wraps NEURON (http://www.neuron.yale.edu) with easy to use Python objects.

This repository contains my current doctoral research at the University of Manchester

Showcases of Spiking Neural Network, which have Synaptic Plasticity

Simulation code for Limbacher, T. and Legenstein, R. (2020). Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring

Companion code to Confavreux*, Ramesh*, Goncalves, Macke* & Vogels*, Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference, NeurIPS 2023

Model of the Thalamo-Cortical Microcircuit - Please, refer to the new repository https://github.com/celinesoeiro/TCM-model

Python code accompanying the paper https://doi.org/10.1101/460568

Simulate multiple independent leaky integrate-and-fire neurons. Incorporates synaptic plasticity model. Useful for comparing effect of independence vs causal synaptic coupling on synaptic plasticity.

Inhibitory neuron plasticity as a mechanism for ocular dominance plasticity (Bono & Clopath 2019)

Parallel-fibre to Purkinje cell plasticity simulator. Multiple optimisation functions incorporated to fit model to experimental data.

Cerebellar long-term depression (LTD) (Antunes and De Schutter 2012)

Vestibulo-Ocular Reflex model in Matlab (Clopath at al. 2014)

Numerical integration of mean-field equations for large-scale leaky integrate-and-fire neuronal network simulations incorporating synaptic plasticity via Graupner Brunel model. Includes support for a memory-induction stim-pop.

Factors contribution to GDP-induced [Cl-]i transients (Lombardi et al 2019)

Homeostatic synaptic plasticity (Rabinowitch and Segev 2006a,b)