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REVIEW 5 major objections 6 minor 42 references

High-Speed Time Series Prediction with a GHz-rate Photonic Spiking Neural Network built with a single VCSEL

T0 review · 5 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A single VCSEL-based photonic spiking neural network can forecast the chaotic Mackey-Glass time series at 4 GHz input rates by pairing each value with a 10-step-delayed copy of itself.

desk verdict A clean first demonstration of delayed-input memory in a single-VCSEL photonic spiking network for chaotic time-series prediction, undercut by missing baselines and repeatability. read the letter →

arxiv 2412.09233 v1 pith:UHJSYXZH submitted 2024-12-12 physics.comp-ph physics.optics

Dafydd Owen-Newns (1) , Lina Jaurigue (2) , Josh Robertson (1) , Andrew Adair (1) , Jonnel Anthony Jaurigue (2) , Kathy Lüdge (2) , Antonio Hurtado (1) ((1) University of Strathclyde , (2) Technische Universität Ilmenau) This is my paper · ORCID
classification physics.comp-phphysics.optics
keywords photonicspikingneuralnetworkVCSELtimeseriespredictionMackey-Glassneuromorphicphotonicsmultiplexingopticalspikesreservoircomputing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a photonic spiking neural network built from a single telecom-wavelength VCSEL can perform multi-step-ahead prediction of the chaotic Mackey-Glass time series, the first memory-requiring task demonstrated with such a spiking photonic network. The system feeds each time-series value together with its 10-step-delayed copy into a time-multiplexed network of hundreds of virtual spiking neurons and trains only the output weights by ridge regression. Reported test errors are NMSE 0.021 for one-step-ahead and 0.051 for ten-step-ahead forecasts, with correlations up to 0.97. The result matters because it shows that a feedforward spiking photonic device, without optical feedback loops, can hold enough memory for useful forecasting at GHz rates and microwatt optical power.

What carries the argument

The central object is a time-multiplexed photonic spiking neural network (p-SNN): one 1300 nm VCSEL treated as many virtual neurons by sampling its optically injected spike output every θ = 250 ps, giving hundreds of virtual nodes. The memory-inducing mechanism is delayed-input encoding: each scalar time-series value is paired with the value 10 steps earlier, multiplied by a random 2 × N_v mask, normalized, and intensity-modulated into the VCSEL. The output is binarized into spike/no-spike vectors, and a ridge-regression readout maps the binary state to the forecast. This machinery replaces recurrent optical feedback with input-side memory while keeping the network feedforward.

What would settle it

Repeat the identical Mackey-Glass forecasting experiment several times on the same VCSEL setup with the same inputs and training data, and compare the binarized spike patterns and the test NMSE using the same trained weights; if the patterns differ between runs or the NMSE degrades sharply, the spike code is not stable enough to support the prediction claim. A second check would be to vary the binarization threshold slightly and see whether accuracy collapses.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that a feedforward VCSEL-based photonic spiking neural network can solve a task usually requiring recurrent memory if the input is pre-processed into pairs (x_t, x_{t-10}) through a random mask. The VCSEL's excitable optical spikes transform these pairs into high-dimensional binary spike patterns, and a linear ridge-regression readout maps the binary state to the future time-series value. The paper reports NMSE values of 0.021 for one-step-ahead and 0.051 for ten-step-ahead predictions on 8000 Mackey-Glass points, with 1500 test points, across node counts of 256, 512, and 1024 and training fractions of 20%, 50%, and 80%. The local error minimum near the 10-step horizon matches the input delay, which the authors attribute to the delayed-input memory technique and to the ultrafast nonlinear spike code of the laser.

Load-bearing premise

The load-bearing premise is that the binarized optical spike pattern produced by the VCSEL is a stable and reproducible nonlinear encoding of the two input values; the paper reports a single set of runs without showing repeatability, noise, or threshold sensitivity, so if the patterns vary between runs the trained readout weights would not transfer.

Editorial extensions

If this is right

  • A feedforward spiking photonic network without optical feedback can perform memory-requiring tasks when the input contains a delayed copy of the signal, shifting the memory bottleneck from hardware recurrence to input encoding.
  • Because the network has no recurrent connections, its node count can be changed by adjusting the temporal spread of the input, letting users trade prediction accuracy against processing speed without modifying the hardware.
  • The system achieves NMSE 0.051 for 10-step-ahead Mackey-Glass forecasts with a 512-node network trained on only 1600 points, indicating that spike-based photonic readouts can work with relatively small training sets.
  • At 250 ps per node, the input data rate is 4 GHz, implying that chaotic time-series forecasting can run far faster than typical electronic neuromorphic systems.
  • The absence of external optical delay lines keeps the implementation simple and compatible with standard telecom components.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the binarized spike patterns prove repeatable across repeated experiments, the same delayed-input trick could plausibly be transferred to other feedforward photonic nonlinearities, such as modulators or micro-ring resonators, that currently lack memory.
  • A single 10-step delay creates a matching local minimum in the error curve, so using multiple delays or a learned delay distribution might smooth and extend the useful prediction horizon, though this paper tests only one delay.
  • The binary spike/no-spike readout discards spike timing and amplitude information, so using analog or temporal features as additional readout dimensions might improve accuracy without changing the optical hardware.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper reports an experimental demonstration of a GHz-rate photonic spiking neural network (p-SNN) built from a single VCSEL, using time multiplexing to create virtual nodes, and applies it to multi-step-ahead prediction of the chaotic Mackey-Glass time series. To supply the network with memory, the input is formed from the current time-series value and a value delayed by 10 steps, and the network's optical spiking output is binarized and trained with ridge regression. The authors report NMSE values as low as 0.021 for one-step-ahead and 0.051 for ten-step-ahead prediction, across networks with 256, 512, and 1024 virtual nodes, and they also investigate the effect of training set size.

Significance. If the demonstration is robust, it is a useful advance in VCSEL-based neuromorphic photonics: it extends p-SNNs from classification tasks to analog time-series prediction, shows that a feedforward spiking network can perform a task usually associated with recurrent reservoirs, and avoids external optical feedback loops. The paper also provides openly available data, which supports reproducibility. However, the significance is tempered because the memory is provided externally through input preprocessing rather than being intrinsic to the network, and because the reported accuracy is not benchmarked against simple baselines or standard reservoir computing results.

major comments (5)
  1. [Methods, Training and Testing; Fig. 6] The regularisation parameter λ is described as chosen to minimize the NMSE, but no validation procedure is stated. If λ is selected on the same data used to compute the reported NMSE, the error values are optimistically biased and do not reflect generalization. Please clarify whether a separate validation set was used for λ selection, and report the NMSE over a range of λ to show sensitivity.
  2. [Experimental Results, Fig. 5] The binarization of the VCSEL output into spike/no-spike node states is central to the readout, yet the spike-detection threshold (or the exact criterion used to assign a '1') is never specified. Without this detail, the results cannot be reproduced, and the reported NMSE values may depend on the chosen threshold. Please provide the threshold and a sweep over threshold values to demonstrate that the prediction performance is robust to this choice.
  3. [Experimental Results, Figs. 6-8 and Tables 1-3] All reported NMSE and correlation values appear to come from a single experimental run per configuration, with no error bars, no repeated measurements, and no train/test resampling. For an experimental demonstration claiming high accuracy, some measure of run-to-run variability is needed to establish that the binarized spike patterns are stable and that the learned readout weights transfer. Please either repeat the experiment a number of times or, at minimum, split the data into multiple train/test folds and report the spread of the results.
  4. [Experimental Results, Fig. 6] The paper does not compare the p-SNN's performance against any baseline predictor, such as linear regression on the same two input components, a trivial persistence forecast, or a standard photonic reservoir computer. Because Mackey-Glass time series are strongly autocorrelated and the input already contains the value delayed by 10 steps, a baseline comparison is necessary to establish that the nonlinear spiking transformation of the p-SNN is responsible for the reported accuracy. Please add such comparisons or discuss the expected performance of these baselines.
  5. [Introduction and Conclusion] The paper repeatedly frames the task as a 'memory-requiring' one and claims the p-SNN 'lacks internal system memory' but then introduces memory by concatenating a delayed copy of the input. This means the network itself remains feedforward and memoryless; the temporal information is provided externally by the input encoding. Please either rephrase the novelty claim to emphasize that the demonstration is of a feedforward p-SNN with external delayed-input memory, or provide a variant where the prediction horizon is not matched to the input delay, which would more directly test whether the network learns temporal relationships.
minor comments (6)
  1. [Chaotic Time Series Prediction, Eq. (1)] Equation (1) is referenced in the text as defining the Mackey-Glass equation, but the equation itself is not displayed in the manuscript; please include it.
  2. [Experimental Results, paragraph on Fig. 5] There is a typo: 'The spiking patterns produced by by p-SNN' should read 'by the p-SNN'.
  3. [References] References [38] and [39] appear in the reference list but are not cited in the main text; please cite them where appropriate or remove them.
  4. [Experimental Results and Methods] The frequency detuning is given as -3.6 GHz in the Experimental Results section and as -3.2 GHz in the Methods section; please reconcile these values.
  5. [Fig. 6 caption] The caption states 'training set size is 6000, testing set size is 1500', but the total number of data points is 8000, leaving 500 points unaccounted for; clarify the train/test split used for Fig. 6.
  6. [Fig. 8 and Fig. 6] The train/test split described for Fig. 8 (4000 training, 4000 test) differs from that in Fig. 6 (6000 training, 1500 test); please make the splits consistent or explain the difference in the main text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an experimental demonstration whose readout is trained on one portion of the data and tested on a disjoint portion, and whose memory mechanism is applied rather than derived from the claimed result.

full rationale

The paper's central claim is experimental: a single time-multiplexed VCSEL, operated as a photonic spiking neural network, can perform multi-step-ahead prediction of the Mackey-Glass time series when the input is augmented with a delayed copy of the series. The prediction pipeline is standard supervised reservoir/readout training: the binary spike-state matrix S is measured from the hardware, the output weights are computed by ridge regression in Eq. (4), and the reported NMSE values are evaluated on test data points that were not used to fit those weights. This is genuine prediction rather than a fitted quantity renamed as a prediction. The delayed-input memory technique is cited to prior work by overlapping authors (refs. [36,42]), but it is used here as a preprocessing choice, not as a theorem that forces the experimental outcome; the paper even notes the approach is similar to typical feedforward and nonlinear vector regression methods. The local NMSE minimum near 10 steps ahead is explicitly attributed to the chosen 10-step input delay, which is a transparent experimental design choice rather than a hidden equivalence between input and target. No equation in the paper reduces to its own inputs by construction, and no uniqueness argument or self-citation chain is invoked to forbid alternative explanations. Concerns about the unspecified spike-detection threshold and lack of repeatability runs are experimental reproducibility issues, not circularity. The derivation chain is therefore self-contained with respect to circularity.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The reported performance depends on several hand-chosen implementation parameters (lambda, input delay, node time, random mask, operating point) and on prior-work assumptions about the delayed-input technique and the p-SNN nonlinearity. No new entities are introduced.

free parameters (5)
  • Ridge regularisation parameter lambda = 0.03
    Chosen to minimize NMSE (Methods: 'The regularisation parameter lambda was chosen to minimize the NMSE'); no validation procedure described, so reported errors may be optimistic.
  • Input delay (steps) = 10
    Chosen to make multi-step-ahead predictions with horizon about 10 steps; creates the local NMSE minimum at 10-step-ahead (Fig 6).
  • Virtual node time theta = 250 ps
    Chosen as about one quarter of the VCSEL refractory period; sets the 4 GHz node rate and per-point processing time (64/128/256 ns for 256/512/1024 nodes).
  • Input mask entries = Random in [0,1), no seed reported
    2 x Nv random weights fixed before training; results depend on the particular realization, so exact replication requires the seed or mask.
  • VCSEL operating point = 3.51 mA bias, ~120 uW injection, -3.2 GHz detuning
    Selected from prior work [35] to produce excitable spiking; not fitted to the task, but it defines the nonlinear feature map.
assumptions (5)
  • domain assumption The Mackey-Glass series generated with RK4, step 0.01, downsampled to step 1 is a valid chaotic benchmark.
    The task and all reported NMSE values depend on this data; generation parameters are given in Methods but the specific equation parameters (e.g., tau) are not stated in the text.
  • domain assumption Delayed-input preprocessing provides the memory needed for prediction in a feedforward network.
    This is the core method taken from the authors' prior works [36,42]; the paper does not re-derive or compare it to alternatives.
  • domain assumption The VCSEL spiking dynamics provide a fixed nonlinear feature map suitable for ridge regression readout.
    The p-SNN architecture is from [34,35]; the paper assumes the binarized spike patterns are sufficiently rich and stable to separate input pairs.
  • domain assumption Binarizing the photodetector output into spike/no-spike per virtual node preserves task-relevant information.
    All readout is from binary vectors; no analysis of noise or threshold sensitivity is provided.
  • standard math Ridge regression least-squares readout is a valid training method.
    Equation (4) is a standard regularized linear readout; no proof needed.

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Cite this review

Pith. "Pith review of High-Speed Time Series Prediction with a GHz-rate Photonic Spiking Neural Network built with a single VCSEL." pith.science (2026). https://pith.science/paper/UHJSYXZH

@misc{pith2026241209233,
  author       = {Pith},
  title        = {Pith review of: High-Speed Time Series Prediction with a GHz-rate Photonic Spiking Neural Network built with a single VCSEL},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UHJSYXZH}},
  note         = {Machine review of arXiv:2412.09233}
}
read the original abstract

Photonic technologies hold significant potential for creating innovative, high-speed, efficient and hardware-friendly neuromorphic computing platforms. Neuromorphic photonic methods leveraging ubiquitous, technologically mature and cost-effective Vertical-Cavity Surface Emitting Lasers (VCSELs) are of notable interest. VCSELs have demonstrated the capability to replicate neuronal optical spiking responses at ultrafast rates. These characteristics have triggered research into applying these key-enabling devices in spike-based photonic computing. Here, a GHz-rate photonic Spiking Neural Network (p-SNN) using a single VCSEL is reported, and its application to a complex time-series prediction task is demonstrated for the first time. The VCSEL p-SNN combined with a technique to induce network memory, is applied to perform multi-step-ahead predictions of a chaotic time-series. By providing the feedforward p-SNN with only two temporally separated inputs excellent accuracy is experimentally demonstrated over a range of prediction horizons. VCSEL-based p-SNNs therefore offer ultrafast, efficient operation in complex predictive tasks whilst enabling hardware implementations. The inherent attributes and performance of VCSEL p-SNNs hold great promise for use in future light-enabled neuromorphic computing hardware.

Figures

Figures reproduced from arXiv: 2412.09233 by the authors.

Figure 1
Figure 1. (a) Diagram of the experimental photonic spiking neural network. A tuneable laser (TL) provides light which is [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Time-trace providing the 8000 data points of the chaotic Mackey-Glass time-series used for the experimental [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Operation of the p-SNN in the time series prediction task. The time series defined by the Mackey-Glass equations [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Photodetector reading of the intensity of light injected (top, orange) and resulting intensity output (bottom, blue) [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Temporal maps depicting the optical spike patterns delivered by the VCSEL p-SNN in response to the input data [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Multi-step-ahead prediction performance of the VCSEL p-SNN using input pairs comprised of the current value [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Performance of the VCSEL p-SNN in the 10-step-ahead prediction of the Mackey-Glass time-series as a function [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Performance of the p-SNN in the prediction of step-ahead values of the Mackey-Glass time series using a network [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 1
Figure 1. Figure 1: At its core, the p-SNN employs a single telecom-wavelength 1300nm-VCSEL as a non-linear element [PITH_FULL_IMAGE:figures/full_fig_p011_1.png]

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Works this paper leans on

42 extracted references · 26 canonical work pages

  1. [1]

    Efficient Neuromorphic Signal Processing with Resonator Neurons,

    E. P. Frady et al., “Efficient Neuromorphic Signal Processing with Resonator Neurons,” Journal of Signal Processing Systems, vol. 94, no. 10, pp. 917–927, Oct. 2022, issn: 1939-8018. doi: 10.1007/s11265-022- 01772-5. [Online]. Available: https://link.springer.com/10.1007/s11265-022-01772-5

    work page doi:10.1007/s11265-022- 2022

  2. [2]

    TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years,

    M. V. DeBole et al., “TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years,”Computer, vol. 52, no. 5, pp. 20–29, May 2019, issn: 0018-9162. doi: 10.1109/MC.2019. 2903009. [Online]. Available: https://ieeexplore.ieee.org/document/8713821/

    arXiv 2019

  3. [3]

    The SpiNNaker 2 Processing Element Architecture for Hybrid Digital Neuromorphic Computing,

    S. Höppner et al., “The SpiNNaker 2 Processing Element Architecture for Hybrid Digital Neuromorphic Computing,” Mar. 2021. arXiv: 2103 . 08392. [Online]. Available: http://arxiv.org/abs/2103.08392

    arXiv 2021

  4. [4]

    The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity,

    C. Pehle et al., “The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity,” Jan. 2022. arXiv: 2201.11063. [Online]. Available: http://arxiv.org/abs/2201.11063

    arXiv 2022

  5. [5]

    Opportunities for neuromorphic computing algorithms and applications,

    C. D. Schuman, S. R. Kulkarni, M. Parsa, J. P. Mitchell, P. Date, and B. Kay, “Opportunities for neuromorphic computing algorithms and applications,” Nature Computational Science, vol. 2, no. 1, pp. 10–19, Jan. 2022, issn:2662-8457. doi:10.1038/s43588-021-00184-y. [Online]. Available: https://www.nature.com/articles/s43588-021-00184-y

    doi:10.1038/s43588-021-00184-y 2022

  6. [6]

    2022 roadmap on neuromorphic computing and engineering,

    D. V. Christensen et al., “2022 roadmap on neuromorphic computing and engineering,” Neuromorphic Computing and Engineering, vol. 2, no. 2, p. 022 501, Jun. 2022, issn: 2634-4386. doi: 10.1088/2634- 4386/ac4a83. [Online]. Available: https://iopscience.iop.org/article/10.1088/2634-4386/ac4a83

    doi:10.1088/2634- 2022

  7. [7]

    An optical neural chip for implementing complex-valued neural network,

    H. Zhang et al., “An optical neural chip for implementing complex-valued neural network,” Nature Communications, vol. 12, no. 1, p. 457, Dec. 2021, issn: 2041-1723. doi: 10.1038/s41467- 020- 20719- 7. [Online]. Available: http://www.nature.com/articles/s41467-020-20719-7

    doi:10.1038/s41467- 2021

  8. [9]

    All-optical spiking neurosynaptic networks with self-learning capabilities,

    J. Feldmann, N. Youngblood, C. D. Wright, H. Bhaskaran, and W. H. P. Pernice, “All-optical spiking neurosynaptic networks with self-learning capabilities,” Nature, vol. 569, no. 7755, pp. 208–214, May 2019, issn: 0028-0836. doi: 10 . 1038 / s41586 - 019 - 1157 - 8. [Online]. Available: http://www.nature.com/articles/s41586-019-1157-8

    work page 2019

Show all 42 references
  1. [10]

    Photonic Computing With Single and Coupled Spiking Micropillar Lasers,

    V. A. Pammi, K. Alfaro-Bittner, M. G. Clerc, and S. Barbay, “Photonic Computing With Single and Coupled Spiking Micropillar Lasers,” IEEE Journal of Selected Topics in Quantum Electronics, vol. 26, no. 1, pp. 1– 7, Jan. 2020, issn: 1077-260X. doi: 10.1109/JSTQE.2019.2929187. [...

    2020

  2. [11]

    Deep Learning with Coherent VCSEL Neural Networks,

    Z. Chen et al., “Deep Learning with Coherent VCSEL Neural Networks,” Jul. 2022. arXiv: 2207.05329. [Online]. Available: http://arxiv.org/abs/2207.05329

    2022 arXiv

  3. [12]

    Controllable spiking patterns in long-wavelength vertical cavity surface emitting lasers for neuromorphic photonics systems,

    A. Hurtado and J. Javaloyes, “Controllable spiking patterns in long-wavelength vertical cavity surface emitting lasers for neuromorphic photonics systems,” Applied Physics Letters, vol. 107, no. 24, p. 241 103, 2015, issn: 0003-6951. doi: 10.1063/1.4937730. [Online]. Available...

    2015 doi

  4. [13]

    Towards Neuromorphic Photonic Networks of Ultrafast Spiking Laser Neurons,

    J. Robertson, E.Wade, Y. Kopp, J. Bueno, and A. Hurtado, “Towards Neuromorphic Photonic Networks of Ultrafast Spiking Laser Neurons,” IEEE Journal of Selected Topics in Quantum Electronics, 2019. doi: 10.1109/JSTQE.2019.2931215

    2019

  5. [14]

    Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons,

    J. Robertson, M. Hejda, J. Bueno, and A. Hurtado, “Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons,” Scientific Reports, vol. 10, no. 1, 2020. doi: 10.1038/s41598-020-62945-5

    2020 doi

  6. [15]

    Spike-based information encoding in vertical cavity surface emitting lasers for neuromorphic photonic systems,

    M. Hejda, J. Robertson, J. Bueno, and A. Hurtado, “Spike-based information encoding in vertical cavity surface emitting lasers for neuromorphic photonic systems,” JPhys Photonics, vol. 2, no. 4, p. 44 001, 2020. doi: 10.1088/2515-7647/aba670. [Online]. Available: https://doi.o...

    2020 doi

  7. [16]

    Experimental demonstration of pyramidal neuron-like dynamics dominated by dendritic action potentials based on a VCSEL for all-optical XOR classification task,

    Y. Zhang et al., “Experimental demonstration of pyramidal neuron-like dynamics dominated by dendritic action potentials based on a VCSEL for all-optical XOR classification task,” Photonics Research, vol. 9, no. 6, p. 1055, Jun. 2021, issn: 2327-9125. doi: 10.1364/PRJ.422628. [...

    2021 doi

  8. [18]

    All-optical neuromorphic binary convolution with a spiking VCSEL neuron for image gradient magnitudes,

    Y. Zhang, J. Robertson, S. Xiang, M. Hejda, J. Bueno, and A. Hurtado, “All-optical neuromorphic binary convolution with a spiking VCSEL neuron for image gradient magnitudes,” Photon. Res., vol. 9, no. 5, B201– B209, 2021. doi: 10.1364/PRJ.412141. [Online]. Available: http://ww...

    2021 doi

  9. [19]

    Image edge detection with a photonic spiking VCSEL-neuron,

    J. Robertson, Y. Zhang, M. Hejda, J. Bueno, S. Xiang, and A. Hurtado, “Image edge detection with a photonic spiking VCSEL-neuron,” Optics Express, vol. 28, no. 25, pp. 37 526–37 537, Dec. 2020, issn: 1094-

    2020

  10. [20]

    Neuromorphic encoding of image pixel data into rate-coded optical spike trains with a photonic VCSEL-neuron,

    M. Hejda, J. Robertson, J. Bueno, J. A. Alanis, and A. Hurtado, “Neuromorphic encoding of image pixel data into rate-coded optical spike trains with a photonic VCSEL-neuron,” APL Photonics, vol. 6, no. 6, p. 60 802, 2021. doi: 10.1063/5.0048674. [Online]. Available: https://ai...

    2021 doi

  11. [21]

    Information processing using a single dynamical node as complex system,

    L. Appeltant et al., “Information processing using a single dynamical node as complex system,” Nature Communications, vol. 2, no. 1, p. 468, Sep. 2011, issn: 2041-1723. doi: 10.1038/ncomms1476. [Online]. Available: http://www.nature.com/articles/ncomms1476

    2011 doi

  12. [22]

    Reservoir computing based on a silicon microring and time multiplexing for binary and analog operations,

    M. Borghi, S. Biasi, and L. Pavesi, “Reservoir computing based on a silicon microring and time multiplexing for binary and analog operations,” Scientific Reports, vol. 11, no. 1, p. 15 642, Dec. 2021, issn: 2045-2322. doi: 10.1038/s41598-021-94952-5. [Online]. Available: https...

    2021 doi

  13. [23]

    Microring resonators with external optical feedback for time delay reservoir computing,

    G. Donati, C. R. Mirasso, M. Mancinelli, L. Pavesi, and A. Argyris, “Microring resonators with external optical feedback for time delay reservoir computing,” Optics Express, vol. 30, no. 1, p. 522, Jan. 2022, issn: 1094-4087. doi: 10 . 1364 / OE . 444063. [Online]. Available: ...

    2022

  14. [24]

    Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing,

    B. J. G. Castro, C. Peucheret, D. Zibar, and F. Da Ros, “Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing,” Oct. 2023. doi: https://doi.org/10.48550/arXiv.2310.09433. arXiv: 2310.09433. [Online]. Available: http://arxiv.org/abs/...

    2023 doi arXiv

  15. [25]

    Compact reservoir computing with a photonic integrated circuit,

    K. Takano et al., “Compact reservoir computing with a photonic integrated circuit,” Optics Express, vol. 26, no. 22, p. 29 424, Oct. 2018, issn: 1094-4087. doi: 10.1364/OE.26.029424. [Online]. Available: https://opg.optica.org/abstract.cfm?URI=oe-26-22-29424

    2018 doi

  16. [26]

    Scalable reservoir computing on coherent linear photonic processor,

    M. Nakajima, K. Tanaka, and T. Hashimoto, “Scalable reservoir computing on coherent linear photonic processor,” Communications Physics, vol. 4, no. 1, p. 20, Feb. 2021, issn: 2399-3650. doi: 10.1038/s42005- 021-00519-1. [Online]. Available: https://www.nature.com/articles/s420...

    2021 doi

  17. [27]

    Enhanced performance of a reservoir computer using polarization dynamics in VCSELs,

    J. Vatin, D. Rontani, and M. Sciamanna, “Enhanced performance of a reservoir computer using polarization dynamics in VCSELs,” Optics Letters, vol. 43, no. 18, p. 4497, Sep. 2018, issn: 0146-9592. doi: 10.1364/OL.43.004497. [Online]. Available: https://opg.optica.org/abstract.c...

    2018 doi

  18. [28]

    Enhanced performance of reservoir computing using multiple self-injection and mutual injection vcsels,

    Y. Huang, P. Zhou, Y. G. Yang, D. Y. Cai, and N. Q. Li, “Enhanced performance of reservoir computing using multiple self-injection and mutual injection vcsels,” IEEE Journal of Selected topics in Quantum Electronics, vol. 29, no. 2, pp. 1–9, 2023. doi: 10.1109/JSTQE.2022. 3216...

    2023

  19. [29]

    A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser,

    X. Porte, A. Skalli, N. Haghighi, S. Reitzenstein, J. A. Lott, and D. Brunner, “A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser,” Journal of Physics: Photonics, vol. 3, no. 2, p. 024 017, Apr. 2021, issn: 2515-7647. doi: 10.1088/2...

    2021 doi

  20. [30]

    Computational metrics and parameters of an injection-locked large area semiconductor laser for neural network computing,

    A. Skalli, X. Porte, N. Haghighi, S. Reitzenstein, J. A. Lott, and D. Brunner, “Computational metrics and parameters of an injection-locked large area semiconductor laser for neural network computing,” arXiv e- prints, arXiv:2112.08947, arXiv:2112.08947, Dec. 2021. arXiv:2112....

    2021 arXiv

  21. [31]

    Experimental realization of dual task processing with a photonic reservoir computer,

    J. Vatin, D. Rontani, and M. Sciamanna, “Experimental realization of dual task processing with a photonic reservoir computer,” APL Photonics, vol. 5, no. 8, p. 086 105, 2020. doi:10.1063/5.0017574. eprint: https://doi.org/10.1063/5.0017574. [Online]. Available:https://doi.org/...

    2020 doi

  22. [32]

    Comprehensive Performance Analysis of a VCSEL- Based Photonic Reservoir Computer,

    J. Bueno, J. Robertson, M. Hejda, and A. Hurtado, “Comprehensive Performance Analysis of a VCSEL- Based Photonic Reservoir Computer,” IEEE Photonics Technology Letters, vol. 33, no. 16, pp. 920–923, Aug. 2021, issn: 1041-1135. doi: 10.1109/LPT.2021.3075095. [Online]. Available...

    2021

  23. [33]

    Photonic neuromorphic computing using vertical cavity semiconductor lasers,

    A. Skalli et al., “Photonic neuromorphic computing using vertical cavity semiconductor lasers,” Optical Materials Express, vol. 12, no. 6, p. 2395, Jun. 2022, issn: 2159-3930. doi: 10.1364/OME.450926. [Online]. Available: https://opg.optica.org/abstract.cfm?URI=ome-12-6-2395

    2022 doi

  24. [34]

    Ghz rate neuromorphic photonic spiking neural network with a single vertical-cavity surface-emitting laser (vcsel),

    D. Owen-Newns, J. Robertson, M. Hejda, and A. Hurtado, “Ghz rate neuromorphic photonic spiking neural network with a single vertical-cavity surface-emitting laser (vcsel),” IEEE Journal of Selected Topics in Quantum Electronics, vol. 29, no. 2: Optical Computing, pp. 1–10, 202...

    2023

  25. [35]

    Photonic spiking neural networks with highly efficient training protocols for ultrafast neuromorphic computing systems,

    D. Owen-Newns, J. Robertson, M. Hejda, and A. Hurtado, “Photonic spiking neural networks with highly efficient training protocols for ultrafast neuromorphic computing systems,” Intelligent Computing, vol. 2, p. 0031, 2023. doi: 10.34133/icomputing.0031. eprint: https://spj.sci...

    2023 doi

  26. [36]

    Reservoir Computing with Delayed Input for Fast and Easy Optimisation,

    L. Jaurigue, E. Robertson, J. Wolters, and K. Lüdge, “Reservoir Computing with Delayed Input for Fast and Easy Optimisation,” Entropy, vol. 23, no. 12, p. 1560, Nov. 2021, issn: 1099-4300. doi: 10.3390/e23121560. [Online]. Available: https://www.mdpi.com/1099-4300/23/12/1560

    2021 doi

  27. [37]

    Role of delay-times in delay-based photonic reservoir computing [Invited],

    T. Hülser, F. Köster, L. Jaurigue, and K. Lüdge, “Role of delay-times in delay-based photonic reservoir computing [Invited],” Optical Materials Express, vol. 12, no. 3, p. 1214, Mar. 2022, issn: 2159-3930. doi: 10.1364/OME.451016. [Online]. Available: https://opg.optica.org/ab...

    2022 doi

  28. [38]

    Improving time series recognition and prediction with networks and ensembles of passive photonic reservoirs,

    M. Freiberger, S. Sackesyn, C. Ma, A. Katumba, P. Bienstman, and J. Dambre, “Improving time series recognition and prediction with networks and ensembles of passive photonic reservoirs,” IEEE Journal of Selected Topics in Quantum Electronics, vol. 26, no. 1, pp. 1–11, 2020. do...

    2020

  29. [39]

    Enhanced performances of photonic reservoir computing using a semiconductor laser with random distributed optical feedback,

    D. Cai, Y. Huang, Y. Yang, P. Zhou, and N. Li, “Enhanced performances of photonic reservoir computing using a semiconductor laser with random distributed optical feedback,” Opt. Lett., vol. 48, no. 24, pp. 6392–6395, Dec. 2023. doi: 10.1364/OL.506633. [Online]. Available: http...

    2023 doi

  30. [40]

    Next generation reservoir computing,

    D. J. Gauthier, B. Erik, A. Griffith, and S. Barbosa Wendson A., “Next generation reservoir computing,” English, Nature Communications, vol. 12, no. 1, 2021

    2021

  31. [41]

    Hülser, F

    T. Hülser, F. Köster, K. Lüdge, and L. Jaurigue, Nanophotonics, vol. 12, no. 5, pp. 937–947, 2023. doi: doi:10.1515/nanoph-2022-0415. [Online]. Available: https://doi.org/10.1515/nanoph-2022-0415

    2023 doi

  32. [42]

    Reducing reservoir computer hyperparameter dependence by external timescale tailoring,

    L. C. Jaurigue and K. Lüdge, “Reducing reservoir computer hyperparameter dependence by external timescale tailoring,” Neuromorph. Comput. Eng., vol. 4, no. 1, p. 014001, 2024

    2024

  33. [43]

    Mackey M C and Glass L 1977 Science 197 287–9

    1977

  34. [4087]

    [Online]

    doi: 10.1364/OE.408747. [Online]. Available: https://doi.org/10.1364/OE.408747%20https://opg.optica.org/abstract.cfm?URI=oe-28-25-37526

    doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.