Covi et al., 2018 - Google Patents
Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neuronsCovi et al., 2018
- Document ID
- 8610712431579217435
- Author
- Covi E
- George R
- Frascaroli J
- Brivio S
- Mayr C
- Mostafa H
- Indiveri G
- Spiga S
- Publication year
- Publication venue
- Journal of Physics D: Applied Physics
External Links
Snippet
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 …
- 210000002569 neurons 0 title abstract description 87
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
- G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
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