Name
Abstract | ||
|---|---|---|
A memristor-based architecture for neuromorphic computing is proposed. With memristors mimicking key characteristics of synapses and neurons, such nanoscale neural networks exhibit learning and memory effects with high integration density and scalability. Simulations demonstrate important features including adjustable spike generation, spiketiming and spike-rate dependent plasticity. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISCAS.2015.7168842 | 2015 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) |
Keywords | Field | DocType |
memristor, neuron, synapse, STDP, neuromorphic | Memristor circuits,Nanoelectronics,Synapse,Memristor,Computer science,Neuromorphic engineering,Electronic engineering,Artificial neural network,Neuron,Scalability | Conference |
ISSN | Citations | PageRank |
0271-4302 | 2 | 0.35 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Le Zheng | 1 | 41 | 7.48 |
| Sang-Ho Shin | 2 | 420 | 41.46 |
| Sung-Mo Steve Kang | 3 | 1198 | 213.14 |