#TemporalCoding

Fabrizio MusacchioFabMusacchio
2026-02-17

Spike-timing-dependent (#STDP) is a core rule in that adjusts strength based on precise pre- vs. postsynaptic timing, enabling and in . In this post, I summarize its mathematical formulation, functional consequences for learning and along with a simple example:

🌍 fabriziomusacchio.com/blog/202

STDP learning window W(Δt) as a function of the relative spike timing ΔtSynaptic weight dynamics with and without spike-timing-dependent plasticity. Left: STDP-enabled network. Synaptic weights differentiate over time and converge toward a bimodal distribution. Right: Control simulation without STDP. Synaptic weights remain at their initial random values and show no dynamical reorganization. Top panels show the final synaptic weights, middle panels show the distribution of synaptic weights, and bottom panels show the time course of two example synapses.

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