Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype

Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since brain-inspired algorithms often make extensive use...

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Bibliographic Details
Published in:	IEEE transactions on biomedical circuits and systems Vol. 13; no. 3; pp. 579 - 591
Main Authors:	Yan, Yexin, Kappel, David, Neumarker, Felix, Partzsch, Johannes, Vogginger, Bernhard, Hoppner, Sebastian, Furber, Steve, Maass, Wolfgang, Legenstein, Robert, Mayr, Christian
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.06.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accelerators Algorithms Bayesian reinforcement learning Biomedical materials Brain Brain - physiology Brain modeling Clocks Computational modeling Computer programs Computer simulation Dynamic random access memory exponential function accelerator Exponential functions Generators Hardware Humans Learning Machine Learning Mathematical models Models, Neurological Nervous system Neural Networks, Computer neuromorphic computing Plastic properties Plasticity Prototypes Random access memory random number generator Random numbers Reinforcement Silicon substrates Software SpiNNaker chip Static random access memory structural plasticity Synapses - physiology Synaptic plasticity synaptic sampling Task complexity
ISSN:	1932-4545, 1940-9990, 1940-9990
Online Access:	Get full text
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