# STDP Synapse

Interactive Spike-Timing-Dependent Plasticity (STDP) visualization — explore how precise spike timing shapes synaptic weights, the neural basis of learning and memory

> Canonical page: https://elysiatools.com/en/visualizations/stdp-synapse

- **Category:** Biology

## Overview

Interactive Spike-Timing-Dependent Plasticity (STDP) visualization based on Bi & Poo (1998). Explore how precise spike timing differences (Δt = t_post − t_pre) dynamically adjust synaptic weights through LTP (Long-Term Potentiation, Δt > 0) and LTD (Long-Term Depression, Δt < 0). The STDP learning window follows exponential decay: ΔW = A+·exp(−Δt/τ+) for potentiation and ΔW = −A−·exp(Δt/τ−) for depression. Three visualization panels: (1) Main STDP learning window curve with interactive Δt marker, LTP/LTD regions color-coded (red/blue), current ΔW annotation, and zero-crossing reference; (2) Synaptic weight evolution over training iterations showing convergence to w_min or w_max; (3) Weight heatmap for 8 synapses with different Δt values (−40 to +40 ms) evolving simultaneously. Adjustable parameters: spike timing Δt (−50 to +50 ms), learning rate A+, time constants τ+ and τ− (5–60 ms), LTD/LTP asymmetry ratio, and spike protocol (pair/triplet/burst with 5 spikes). Four presets: Bi & Poo 1998 (classic symmetric window), Asymmetric (A−/A+ = 1.5, net depressive), Anti-Hebbian (inverted window), Fast Window (τ = 8 ms). Real-time statistics: current ΔW, synaptic weight, pair count, plasticity type. Educational content covers STDP discovery, exponential learning window mathematics, causal interpretation of Hebbian learning, and applications in hippocampal memory, neuromorphic computing (Intel Loihi), and spiking neural networks. Multi-language support (zh, en, es, fr, de, ru, pt).

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