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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 online resource (244 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
AI
artificial intelligence artificial neural network artificial synapse autocovariance boost-factor adjustment character recognition circuit design compact model cortical neurons crossbar array deep learning networks defect-tolerant spatial pooling electronic synapses emulator Flash memories graphene oxide hardware-based deep learning ICs hierarchical temporal memory laser memristive devices memristor memristor crossbar memristor-CMOS hybrid circuit memristors multiscale modeling neocortex neural network hardware neural networks neuromorphic neuromorphic computing neuromorphic engineering neuromorphic hardware neuromorphic systems neuromorphics optimization OxRAM pattern recognition pavlov reinforcement learning resistive switching RRAM self-organization maps sensory and hippocampal responses simulation spike-timing-dependent plasticity spiking neural network spiking neural networks STDP strongly correlated oxides synapse synaptic device synaptic plasticity synaptic weight temporal pooling time series modeling transistor-like devices variability vertical RRAM wire resistance |
| 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 online resource (234 p.) |
| Soggetto topico |
Neurosciences
Science: general issues |
| Soggetto non controllato |
deep spiking neural networks
neuromorphics programming framework SNN benchmarks SNN learning algorithms |
| 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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