Learning in Neural Networks
Petrut Bogdan, Garibaldi Pineda García, Michael J. Hopkins, Edward George Jones, James C. Knight, Perrett Adam · now publishers, Inc. eBooks · 2020
This chapter is concerned with the motivation, design and implementation behind mimicking biological learning rules with a focus on, you guessed it, SpiNNaker. It starts by presenting Spike-timing-dependent plasticity (STDP) operating in an unsupervised fashion based on relative spike times of the pre- and post-synaptic neurons or based on the sub-threshold membrane potential. This is followed by a model of STDP modulated by the presence of an additional signal and operating on eligibility traces. Longer-term mechanisms in the form of structural plasticity, involving the rewiring of connections between the neurons, and (very long-term) neuroevolution close out the chapter.