A biological process that adjusts the synaptic connection strength between neurons based on the exact timing of their firing. Foundational learning rule for how the brain refines Neural Circuits, encoding memory, and processing input sensory information.
Mechanism
- Spike timing-dependent plasticity and memory paper
- Operates on millisecond timescale, determining if connection gets stronger or weaker
- Long Term Potentiation - if presynaptic neuron fires just before postsynaptic neuron, synapse is strenghthened
should read https://artemkirsanov.substack.com/p/every-spike-rewires-the-rule
In Deep Learning ( 09-12-2026 )
This principle is also applied in the field of deep learning as a biologically plausible learning algorithm, specifically used to train Spiking Neural Networks by adjusting synaptic weights based on the exact relative timing of pre- and post-synaptic spikes… basically same thing as above
In this case we have:
- causal firing ( LTP ); same as above, if presyn neuron fires just before post syn neuron, synapse strenghtened
- anti causal firing ( Long Term Depression ); if a Presynaptic Neurons fires just after a Postsynaptic Neurons, the connection strength decreases
- locality; updates depend only on local spike timing rather than a global error signal, ideal for even driven systems like that of Spiking Neural Networks and Neuromorphic Computing in general.