Abstract
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.
Original language | English |
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Title of host publication | SpiNNaker |
Subtitle of host publication | a spiking neural network architecture |
Editors | Steve Furber, Petruț Antoniu Bogdan |
Place of Publication | Boston-Delft |
Publisher | Now Publishers Inc |
Chapter | 7 |
Pages | 209-265 |
Number of pages | 57 |
ISBN (Electronic) | 9781680836530 |
ISBN (Print) | 9781680835960 |
DOIs | |
Publication status | Published - 31 Mar 2020 |