Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and l...

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Veröffentlicht in:	Frontiers in neuroscience Jg. 11; S. 324
Hauptverfasser:	Neftci, Emre O., Augustine, Charles, Paul, Somnath, Detorakis, Georgios
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Research Foundation 21.06.2017 Frontiers Media S.A
Schlagworte:	Artificial intelligence Back propagation backpropagation algorithm Brain research Computers Deep learning Distance learning embedded cognition Feedback feedback alignment Firing pattern International conferences Learning algorithms Machine learning Memory Neural networks Neurons Neuroscience Neurosciences Signal processing spiking neural networks stochastic processes Synaptic plasticity Synaptic strength United States > US spiking neural networks feedback alignment backpropagation algorithm stochastic processes embedded cognition
ISSN:	1662-453X, 1662-4548, 1662-453X
Online-Zugang:	Volltext
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