ArticleNature communications2021
Collective and synchronous dynamics of photonic spiking neurons.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 51 citations in OpenAlex.
- Time-synthetic optical neural networks with stable programmable gain.Nature communications · 2026Article
- Control of Oscillator Networks with Mean-Field Measurement: A Hybrid Open/Closed-Loop Approach.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2026Article
- Photonic multiplexing techniques for neuromorphic computing.Nanophotonics (Berlin, Germany) · 2023Review
- Multidimensional hyperspin machine.Nature communications · 2022Article
- Transient Response and Firing Behaviors of Memristive Neuron Circuit.Frontiers in neuroscience · 2022Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nonlinear dynamics of spiking neural networks have recently attracted much interest as an approach to understand possible information processing in the brain and apply it to artificial intelligence. Since information can be processed by collective spiking dynamics of neurons, the fine control of spiking dynamics is desirable for neuromorphic devices. Here we show that photonic spiking neurons implemented with paired nonlinear optical oscillators can be controlled to generate two modes of bio-realistic spiking dynamics by changing optical-pump amplitude. When the photonic neurons are coupled in a network, the interaction between them induces an effective change in the pump amplitude depending on the order parameter that characterizes synchronization. The experimental results show that the effective change causes spontaneous modification of the spiking modes and firing rates of clustered neurons, and such collective dynamics can be utilized to realize efficient heuristics for solving NP-hard combinatorial optimization problems.
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