4.5 Article

Online spike-based recognition of digits with ultrafast microlaser neurons

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Electrical & Electronic

GHz Rate Neuromorphic Photonic Spiking Neural Network With a Single Vertical-Cavity Surface-Emitting Laser (VCSEL)

Dafydd Owen-Newns et al.

Summary: This study introduces and experimentally demonstrates a new GHz-rate photonic spiking neural network (SNN) built with a single VCSEL neuron. The system effectively implements a photonic VCSEL-based spiking reservoir computer and successfully applies it to a complex nonlinear classification task. With highly hardware-friendly and inexpensive realization, the proposed system shows great potential for future neuromorphic photonic spike-based processing systems.

IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS (2023)

Review Materials Science, Multidisciplinary

Photonic neuromorphic computing using vertical cavity semiconductor lasers

Anas Skalli et al.

Summary: Photonic realizations of neural network computing hardware are a promising approach for future scalable neuromorphic computing. This review provides an overview of VCSELs and their integration with other photonic hardware to achieve high-performance artificial neurons, efficient and scalable neural network connections, and strategies for adjusting network connections during learning. VCSELs are highly energy efficient and ultra-fast, making them suitable for both all-optical and electro-optical photonic neurons.

OPTICAL MATERIALS EXPRESS (2022)

Article Multidisciplinary Sciences

Ultrafast neuromorphic photonic image processing with a VCSEL neuron

Joshua Robertson et al.

Summary: The hardware-friendly neuromorphic photonic processor utilizing VCSEL technology enables high-speed all-optical image edge-feature detection. By integrating with a software-implemented spiking neural network, it provides a full platform for complex image classification tasks. This work highlights the potential of VCSEL-based platforms for novel, ultrafast, all-optical neuromorphic processors interfacing with current computation and communication systems.

SCIENTIFIC REPORTS (2022)

Article Physics, Applied

Resonant Tunneling Diode Nano-Optoelectronic Excitable Nodes for Neuromorphic Spike-Based Information Processing

Matej Hejda et al.

Summary: This work introduces an interconnected nano-optoelectronic spiking artificial neuron emitter-receiver system that operates at ultrafast rates and with low-energy consumption. The system utilizes pulse thresholding and integration for image feature recognition and demonstrates a spiking neural network model for processing spatiotemporal data at high speeds. It also showcases a supervised learning approach for the RTD-enabled photonic spiking neural network.

PHYSICAL REVIEW APPLIED (2022)

Article Neurosciences

Analyzing time-to-first-spike coding schemes: A theoretical approach

Lina Bonilla et al.

Summary: This theoretical paper focuses on information coding and decoding in spiking neural networks using time-to-first-spike (TTFS) codes. It introduces a new mathematical framework that allows the comparison of various coding schemes. The paper compares three coding schemes - ROC, NoM, and R-NoM - in terms of discriminability and finds that R-NoM has higher discriminability, especially in the early phase of responses. The paper also argues that R-NoM is more hardware-friendly compared to the original ROC proposal, although NoM remains the easiest to implement because it requires binary synapses.

FRONTIERS IN NEUROSCIENCE (2022)

Article Neurosciences

Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems

Wenzhe Guo et al.

Summary: This study compared four neural coding schemes and found that TTFS coding achieved the highest computational performance with low latency and fewer SOPs. Phase coding was most resilient to input noise, while burst coding showed the highest network compression efficacy and robustness to hardware non-idealities.

FRONTIERS IN NEUROSCIENCE (2021)

Article Mathematical & Computational Biology

Supervised Learning With First-to-Spike Decoding in Multilayer Spiking Neural Networks

Brian Gardner et al.

Summary: Experimental studies show that the brain uses spike-based neuronal information processing, which can be applied to solve real-world challenges through neuromorphic systems. A new supervised learning method for multilayer spiking neural networks is proposed to solve classification problems, supporting multiple spikes and compact data representation. Different encoding strategies are explored, such as scanline encoding for image data transformation, with implications for efficient neuromorphic applications.

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE (2021)

Article Multidisciplinary Sciences

Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification

Matteo Cucchi et al.

Summary: Early detection of malign patterns in patients' biological signals is crucial, and organic electrochemical devices are considered ideal for biosignal monitoring. The study demonstrates the potential of brain-inspired networks composed of organic electrochemical transistors for time-series predictions and classification tasks, showing promise for biofluid monitoring and biosignal analysis.

SCIENCE ADVANCES (2021)

Article Computer Science, Artificial Intelligence

Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes

Christoph Stoeckl et al.

Summary: Optimizing spiking neuron models for information transmission enhances the efficiency and accuracy of deep learning applications through reducing the number of spikes emitted per neuron. This new method improves latency and throughput of resulting spiking networks, offering a low-energy solution for edge and mobile devices in image classification tasks.

NATURE MACHINE INTELLIGENCE (2021)

Article Engineering, Electrical & Electronic

Photonic Computing With Single and Coupled Spiking Micropillar Lasers

Venkata Anirudh Pammi et al.

IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS (2020)

Article Computer Science, Artificial Intelligence

Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence

Nassim Abderrahmane et al.

NEURAL NETWORKS (2020)

Article Computer Science, Artificial Intelligence

Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron

Saeed Reza Kheradpisheh et al.

INTERNATIONAL JOURNAL OF NEURAL SYSTEMS (2020)

Review Physics, Applied

Physical reservoir computing-an introductory perspective

Kohei Nakajima

JAPANESE JOURNAL OF APPLIED PHYSICS (2020)

Article Physics, Multidisciplinary

Equalization of pulse timings in an excitable microlaser system with delay

Soizic Terrien et al.

PHYSICAL REVIEW RESEARCH (2020)

Review Computer Science, Artificial Intelligence

Recent advances in physical reservoir computing: A review

Gouhei Tanaka et al.

NEURAL NETWORKS (2019)

Article Multidisciplinary Sciences

All-optical spiking neurosynaptic networks with self-learning capabilities

J. Feldmann et al.

NATURE (2019)

Article Multidisciplinary Sciences

Neuromorphic computing with nanoscale spintronic oscillators

Jacob Torrejon et al.

NATURE (2017)

Article Physics, Fluids & Plasmas

Spike latency and response properties of an excitable micropillar laser

F. Selmi et al.

PHYSICAL REVIEW E (2016)

Article Optics

Temporal summation in a neuromimetic micropillar laser

F. Selmi et al.

OPTICS LETTERS (2015)

Article Physics, Multidisciplinary

Relative Refractory Period in an Excitable Semiconductor Laser

F. Selmi et al.

PHYSICAL REVIEW LETTERS (2014)

Article Engineering, Electrical & Electronic

A Leaky Integrate-and-Fire Laser Neuron for Ultrafast Cognitive Computing

Mitchell A. Nahmias et al.

IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS (2013)

Article Optics

Excitability in a semiconductor laser with saturable absorber

Sylvain Barbay et al.

OPTICS LETTERS (2011)

Article Multidisciplinary Sciences

A unifying basis of auditory thresholds based on temporal summation

P Heil et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2003)

Article Computer Science, Artificial Intelligence

Spike-based strategies for rapid processing

S Thorpe et al.

NEURAL NETWORKS (2001)