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| Main Authors: | , , , , , , , , , |
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| Formato: | Preprint |
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2025
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| Acceso en liña: | https://arxiv.org/abs/2510.14515 |
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| _version_ | 1866911214454439936 |
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| author | Adair, Andrew Owen-Newns, Dafydd Donati, Giovanni Robertson, Joshua Figueiredo, José Wasige, Eduard Al-Taai, Qusay Romeira, Bruno Hejda, Matěj Hurtado, Antonio |
| author_facet | Adair, Andrew Owen-Newns, Dafydd Donati, Giovanni Robertson, Joshua Figueiredo, José Wasige, Eduard Al-Taai, Qusay Romeira, Bruno Hejda, Matěj Hurtado, Antonio |
| contents | Neuromorphic computing seeks to replicate the spiking dynamics of biological neurons for brain-inspired computation. While electronic implementations of artificial spiking neurons have dominated to date, photonic approaches are attracting increasing research interest as they promise ultrafast, energy-efficient operation with low-crosstalk and high bandwidth. Nevertheless, existing photonic neurons largely mimic integrate-and-fire models, but neuroscience shows that neurons also encode information through richer mechanisms, such as the frequency and temporal patterns of spikes. Here, we present a photonic-electronic resonate-and-fire (R-and-F) spiking neuron that responds to the temporal structure of high-speed optical inputs. This is based on a light-sensitive resonant tunnelling diode that produces excitable spikes in response to nanosecond, low-power (100 microwatt) optical signals at infrared telecom wavelengths. We experimentally demonstrate control of R-and-F dynamics through inter-pulse timing of the optical stimuli and applied bias voltage, achieving bandpass filtering of both analogue and digital inputs. The R-and-F neuron also supports optical fan-in via wavelength-division multiplexed inputs from four vertical-cavity surface-emitting lasers (VCSELs). This electronic-photonic neuron exhibits key functionalities - including spike-frequency filtering, temporal pattern recognition, and digital-to-spiking conversion - critical for neuromorphic optical processing. Our approach establishes a pathway toward low-power, high-speed temporal information processing for light-enabled neuromorphic computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14515 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Resonate-and-Fire Photonic-Electronic Spiking Neurons for Fast and Efficient Light-Enabled Neuromorphic Processing Systems Adair, Andrew Owen-Newns, Dafydd Donati, Giovanni Robertson, Joshua Figueiredo, José Wasige, Eduard Al-Taai, Qusay Romeira, Bruno Hejda, Matěj Hurtado, Antonio Optics Applied Physics Computational Physics Neuromorphic computing seeks to replicate the spiking dynamics of biological neurons for brain-inspired computation. While electronic implementations of artificial spiking neurons have dominated to date, photonic approaches are attracting increasing research interest as they promise ultrafast, energy-efficient operation with low-crosstalk and high bandwidth. Nevertheless, existing photonic neurons largely mimic integrate-and-fire models, but neuroscience shows that neurons also encode information through richer mechanisms, such as the frequency and temporal patterns of spikes. Here, we present a photonic-electronic resonate-and-fire (R-and-F) spiking neuron that responds to the temporal structure of high-speed optical inputs. This is based on a light-sensitive resonant tunnelling diode that produces excitable spikes in response to nanosecond, low-power (100 microwatt) optical signals at infrared telecom wavelengths. We experimentally demonstrate control of R-and-F dynamics through inter-pulse timing of the optical stimuli and applied bias voltage, achieving bandpass filtering of both analogue and digital inputs. The R-and-F neuron also supports optical fan-in via wavelength-division multiplexed inputs from four vertical-cavity surface-emitting lasers (VCSELs). This electronic-photonic neuron exhibits key functionalities - including spike-frequency filtering, temporal pattern recognition, and digital-to-spiking conversion - critical for neuromorphic optical processing. Our approach establishes a pathway toward low-power, high-speed temporal information processing for light-enabled neuromorphic computing. |
| title | Resonate-and-Fire Photonic-Electronic Spiking Neurons for Fast and Efficient Light-Enabled Neuromorphic Processing Systems |
| topic | Optics Applied Physics Computational Physics |
| url | https://arxiv.org/abs/2510.14515 |