🧠 Synaptic Plasticity 2D (STDP)

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🧠 Synaptic Plasticity 2D — STDP Learning Rule

About this simulation

A plain Canvas2D port of the 3D scene: two leaky integrate-and-fire (LIF) neurons connected by one adjustable synapse. The strength of that connection updates in real time by spike-timing-dependent plasticity (STDP), the leading model of how single synapses learn.

Key equations

dw = +A+ · exp(−dt/tau+) if dt > 0 (potentiation)
dw = −A− · exp(+dt/tau−) if dt < 0 (depression)
w ← clip(w + dw, 0, 1)   with   dt = t_post − t_pre

Controls

• Pre→Post timing dt — the millisecond gap between the two spikes (negative = post fires first).
• A+ / A− — maximum step size for potentiation and depression.
• tau+ — how quickly the effect decays as the spikes get further apart (tau− is locked to tau+ in this 2D build for a simpler control set — the underlying math is identical).
• Spike pair rate — how often a pair fires automatically.
• Inject pair — fire one pair now; Pause/Play and Reset control the loop and weight; tap/click the canvas to inject too.