stdp (spike-timing-dependent plasticity)

**STDP** (Spike-Timing-Dependent Plasticity) is a **biologically plausible unsupervised learning rule for SNNs** — adjusting synaptic weights based on the relative timing of pre-synaptic and post-synaptic spikes. **What Is STDP?** - **The Rule**: "Neurons that fire together, wire together" (Hebb). - If input spike (Pre) comes *before* output spike (Post) -> **Strengthen** weight (LTP). "I caused you to fire." - If input spike (Pre) comes *after* output spike (Post) -> **Weaken** weight (LTD). "I was late/irrelevant." - **Causality**: STDP inherently captures causal relationships. **Why It Matters** - **Unsupervised**: Allows networks to learn features from data streams locally without global error backpropagation. - **Hardware Friendly**: Extremely easy to implement on local neuromorphic circuits (memristors). - **Adaptation**: Enables continuous online learning and adaptation to drifting signals. **STDP** is **the mechanism of memory** — the fundamental synaptic algorithm that allows biological brains to wire themselves based on experience.