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Biologically plausible neuromorphic computing systems are attracting considerable attention due to their low latency, massively parallel information processing abilities, and their high energy efficiency. To achieve these features neuromorphic silicon neuron circuits need to be integrated with plastic synapse circuits capable of on-line learning and storage of synaptic weights. Within this context, memristive devices play a key role thanks to their non-volatility, scalability, and compatibility with the complementary metal–oxide–semiconductor fabrication process. However, neuro-memristive systems are still facing difficult challenges for implementing efficient learning protocols. Here, we propose and demonstrate in hardware a spike-driven threshold-based learning rule which goes beyond conventional spike-timing dependent plasticity mechanisms, by also taking into account the neuron membrane potential and its firing …
IOP Publishing
Publication date: 
30 Jul 2018

Erika Covi, Richard George, Jacopo Frascaroli, Stefano Brivio, C Mayr, Hesham Mostafa, Giacomo Indiveri, Sabina Spiga

Biblio References: 
Volume: 51 Issue: 34 Pages: 344003
Journal of Physics D: Applied Physics