Superconducting optoelectronic loop neurons

Shainline, Jeffrey M.; Buckley, Sonia; McCaughan, Adam N.; Chiles, Jeff; Salim, Amir Jafari; Castellanos-Beltran, Manuel; Donnelly, Christine A.; Schneider, Michael L.; Mirin, Richard P.; Nam, Sae Woo

doi:10.1063/1.5096403

Cited by 68 publications

(85 citation statements)

References 232 publications

(624 reference statements)

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“…12 and described in more detail in Ref. 13. The circuit comprises an initial receiver/transducer section, consisting of a superconducting-nanowire single-photon detector (SPD) [26][27][28][29] in parallel with a Josephson junction (JJ) [30][31][32].…”

Section: Photon-to-fluxon Transduction At a Synapsementioning

confidence: 99%

“…This is necessary to enable activitybased plasticity mechanisms [35,36], which have been explored in the context of these circuits in Ref. 13. Third, the bias I sy can be bounded so synaptic strength never exceeds a certain limit, and runaway activity is not possible.…”

Section: Photon-to-fluxon Transduction At a Synapsementioning

confidence: 99%

“…We have argued elsewhere that light is promising for communication in neural systems because it enables the fan-out and energy efficiency necessary for large neural systems, and that utilization of superconducting single-photon detectors enables communication at the lowest possible light levels [11]. Subsequent work considered specific synaptic and neuronal circuits suitable for point-neuron behavior, introducing circuits capable of transducing single-photon communication events to the electronic domain for subsequent information processing [12,13]. References 12 and 13 discussed basic synaptic functionality, plasticity, neuronal integration, thresholding, and the production of light during a neuronal firing event-all functions necessary for point neurons implemented with superconducting optoelectronic hardware.…”

Section: Introductionmentioning

confidence: 99%

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Fluxonic Processing of Photonic Synapse Events

Shainline

2020

IEEE J. Select. Topics Quantum Electron.

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Much of the information processing performed by a neuron occurs in the dendritic tree. For neural systems using light for communication, it is advantageous to convert signals to the electronic domain at synaptic terminals so dendritic computation can be performed with electrical circuits. Here we present circuits based on Josephson junctions and mutual inductors that act as dendrites, processing signals from synapses receiving single-photon communication events with superconducting detectors. We show simulations of circuits performing basic temporal filtering, logical operations, and nonlinear transfer functions. We further show how the synaptic signal from a single-photon can fan out locally in the electronic domain to enable the dendrites of the receiving neuron to process a photonic synapse event or pulse train in multiple different ways simultaneously. Such a technique makes efficient use of photons, energy, space, and information.

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Section: Photon-to-fluxon Transduction At a Synapsementioning

confidence: 99%

Section: Photon-to-fluxon Transduction At a Synapsementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fluxonic Processing of Photonic Synapse Events

Shainline

2020

IEEE J. Select. Topics Quantum Electron.

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“…1 represents selected results from the past 6 months of SOEN research. The latest conception of the neuron signal pathway [2] is shown in Fig. 1(a).…”

Section: Superconducting Optoelectronic Neural Networkmentioning

confidence: 99%

“…Recent results on Superconducting Optoelectronic Networks (SOENs). a) Latest conception of the SOEN neuron, adapted from[2]…”

mentioning

confidence: 99%

Microresonator-enhanced, Waveguide-coupled Emission from Silicon Defect Centers for Superconducting Optoelectronic Networks

Tait

Buckley

McCaughan

et al. 2020

Optical Fiber Communication Conference (OFC) 2020

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Artificial Intelligence and Advanced Materials

López

2023

Advanced Materials

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Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information‐carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.

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Superconducting optoelectronic loop neurons

Cited by 68 publications

References 232 publications

Fluxonic Processing of Photonic Synapse Events

Fluxonic Processing of Photonic Synapse Events

Microresonator-enhanced, Waveguide-coupled Emission from Silicon Defect Centers for Superconducting Optoelectronic Networks

Artificial Intelligence and Advanced Materials

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