Synaptic Plasticity & STDP Lab
A small spiking neuron network where every synapse learns by spike-timing-dependent plasticity (STDP): fire neurons in patterns and watch connection weights strengthen or weaken in real time against a live Δw-vs-Δt curve.
This simulator visualizes Hebbian learning at the level of individual synapses. Eight spiking neurons are wired into a small directed network; every connection between them carries a weight, drawn as the thickness and color of its line. Fire the network with a stimulation pattern — or manually test one synapse at a precise spike-timing offset — and watch each synapse's weight update live according to the real spike-timing-dependent plasticity (STDP) rule: potentiation when the presynaptic neuron fires just before the postsynaptic one, depression when the order reverses, both decaying exponentially with the size of the timing gap. A live Δw-vs-Δt plot overlays the theoretical STDP curve with the actual updates the network just made, and a weight-vs-time strip chart tracks how a chosen synapse evolves.
Model Hebbian learning in a ring of 8 spiking neurons where synaptic weights update via STDP. Potentiate or depress synapses by firing neurons at specific intervals, and observe live updates on theoretical and actual STDP curves.
2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install