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<p>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&amp;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 (&lt;100μW) optical signals at infrared telecom wavelengths. We experimentally demonstrate control of R&amp;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&amp;F neuron also supports optical fan-in via wavelength-division multiplexed inputs from four vertical-cavity surface-emitting lasers (VCSELs). This photonic-electronic 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.</p>

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Resonate-and-fire photonic-electronic spiking neurons for fast and efficient light-enabled neuromorphic processing systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月23日

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  • Andrew Adair,
  • Dafydd Owen-Newns,
  • Giovanni Donati,
  • Joshua Robertson,
  • José Figueiredo,
  • Edward Wasige,
  • Qusay Al-Taai,
  • Bruno Romeira,
  • Matěj Hejda,
  • Antonio Hurtado

摘要

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&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 (<100μW) optical signals at infrared telecom wavelengths. We experimentally demonstrate control of R&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&F neuron also supports optical fan-in via wavelength-division multiplexed inputs from four vertical-cavity surface-emitting lasers (VCSELs). This photonic-electronic 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.