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Memristors for Neuromorphic Circuits and Artificial Intelligence Applications
| Memristors for Neuromorphic Circuits and Artificial Intelligence Applications |
| Autore | Suñé Jordi |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (244 p.) |
| Soggetto non controllato |
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 |
| ISBN | 3-03928-577-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404090703321 |
Suñé Jordi
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Spiking Neural Network Learning, Benchmarking, Programming and Executing
| Spiking Neural Network Learning, Benchmarking, Programming and Executing |
| Autore | Li Guoqi |
| Pubbl/distr/stampa | Frontiers Media SA, 2020 |
| Descrizione fisica | 1 electronic resource (234 p.) |
| Soggetto topico |
Science: general issues
Neurosciences |
| Soggetto non controllato |
deep spiking neural networks
SNN learning algorithms programming framework SNN benchmarks neuromorphics |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557631803321 |
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Li Guoqi
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| Frontiers Media SA, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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