Memristor-based neural networks: Synaptic versus neuronal stochasticity
In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors. The ionic process involved in the underlying switch...
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Veröffentlicht in: | AIP advances 2016-11, Vol.6 (11), p.111304-111304-7 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or
neuronal components. The hardware emulation of these stochastic
neural networks
are currently being extensively studied using resistive memories or memristors. The ionic
process involved in the underlying switching behavior of the memristive elements is
considered as the main source of stochasticity of its operation. Building on its inherent
variability, the memristor is incorporated into abstract models of stochastic
neurons and
synapses. Two
approaches of stochastic
neural networks
are investigated. Aside from the size and area perspective, the impact on the system
performance, in terms of accuracy, recognition rates, and learning, among these two
approaches and where the memristor would fall into place are the main comparison points to
be considered. |
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ISSN: | 2158-3226 2158-3226 |
DOI: | 10.1063/1.4967352 |