Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires...

Descripción completa

Detalles Bibliográficos
Otros Autores:	Suñé, Jordi (auth)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	MDPI - Multidisciplinary Digital Publishing Institute 2020
Materias:	graphene oxide artificial neural network simulation neural networks STDP neuromorphics spiking neural network artificial intelligence hierarchical temporal memory synaptic weight optimization transistor-like devices multiscale modeling memristor crossbar spike-timing-dependent plasticity memristor-CMOS hybrid circuit pavlov wire resistance AI neocortex synapse character recognition resistive switching electronic synapses defect-tolerant spatial pooling emulator compact model deep learning networks artificial synapse circuit design memristors neuromorphic engineering memristive devices OxRAM neural network hardware sensory and hippocampal responses neuromorphic hardware boost-factor adjustment RRAM variability Flash memories neuromorphic reinforcement learning laser memristor hardware-based deep learning ICs temporal pooling self-organization maps crossbar array pattern recognition strongly correlated oxides vertical RRAM autocovariance neuromorphic computing synaptic device cortical neurons time series modeling spiking neural networks neuromorphic systems synaptic plasticity
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009431799706719