ArticleFrontiers in neuroscience2021
Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- An integrated coupled oscillator network to solve optimization problems.Communications engineering · 2024Article
- Design of oscillatory neural networks by machine learning.Frontiers in neuroscience · 2024Article
- Computing with oscillators from theoretical underpinnings to applications and demonstrators.Npj unconventional computing · 2024Review
- The Coupled Reactance-Less Memristor Based Relaxation Oscillators for Binary Oscillator Networks.Micromachines · 2023Article
- Oscillatory neural network learning for pattern recognition: an on-chip learning perspective and implementation.Frontiers in neuroscience · 2023Article
- Learning algorithms for oscillatory neural networks as associative memory for pattern recognition.Frontiers in neuroscience · 2023Article
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Authors and funding
2 authors.
Funding
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Abstract
Oscillatory Neural Network (ONN) is an emerging neuromorphic architecture with oscillators representing neurons and information encoded in oscillator's phase relations. In an ONN, oscillators are coupled with electrical elements to define the network's weights and achieve massive parallel computation. As the weights preserve the network functionality, mapping weights to coupling elements plays a crucial role in ONN performance. In this work, we investigate relaxation oscillators based on VO
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