Biological neurons don't just emit constant 0...1 float values, they communicate using time sensitive bursts of voltage known as "spike trains". Spiking Neural Networks (SNN) are a closer aproximation of natural networks than typical ML ANNs. [0] gives a quick overview.
Spike-Timing-Dependant-Plasticity is a local learning rule experimentally observed in biological neurons. It's a form of Hebbian learning, aka "Neurons that fire together wire together."
Summary from [1]. The top graph gives a clear picture of how the rule works.
> With STDP, repeated presynaptic spike arrival a few milliseconds before postsynaptic action potentials leads in many synapse types to Long-Term Potentiation (LTP) of the synapses, whereas repeated spike arrival after postsynaptic spikes leads to Long-Term Depression (LTD) of the same synapse.
Biological neurons don't just emit constant 0...1 float values, they communicate using time sensitive bursts of voltage known as "spike trains". Spiking Neural Networks (SNN) are a closer aproximation of natural networks than typical ML ANNs. [0] gives a quick overview.
Spike-Timing-Dependant-Plasticity is a local learning rule experimentally observed in biological neurons. It's a form of Hebbian learning, aka "Neurons that fire together wire together."
Summary from [1]. The top graph gives a clear picture of how the rule works.
> With STDP, repeated presynaptic spike arrival a few milliseconds before postsynaptic action potentials leads in many synapse types to Long-Term Potentiation (LTP) of the synapses, whereas repeated spike arrival after postsynaptic spikes leads to Long-Term Depression (LTD) of the same synapse.
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[0]: https://towardsdatascience.com/deep-learning-versus-biologic...
[1]: http://www.scholarpedia.org/article/Spike-timing_dependent_p...