Neuromorphic Computing

Journal

IEEE Transactions on Human-Machine Systems (THMS)

IEEE Transactions on Affective Computing (TAC)

IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

Neural Networks (NN)

Journal

International Journal of Robotics Research {IJRR}

Journal of Field Robotics (JFR)

IEEE Transactions on Robotics (TR)

IEEE Transactions on Cybernetics (TC)

IEEE Transactions on Systems Man Cybernetics-Systems (TSMC)

Business

西井科技
[Homepage]

北京灵汐科技有限公司
[Homepage]

Group

中国科学院脑科学与智能技术卓越创新中心
[Homepage]

类脑智能研究中心---自动化研究所
[Homepage]

中国科学技术大学类脑智能技术及应用国家工程实验室
[Homepage]

复旦大学类脑智能科学与技术研究院
[Homepage]

Luping Shi
清华大学类脑计算研究中心
[Homepage] [Google Scholar]

Paul R. Prucnal
Princeton-Lightwave Communications Research Lab
[Homepage] [Google Scholar]

Shuiying Xiang
Xidian University
[Google Scholar]

Resource

Spiking Neural Network
[Github]

Event-based Vision Resources
[Github]

Review

Using neuroscience to develop artificial intelligence.
S Ullman.
Science, 2019.

A system hierarchy for brain-inspired computing.
Y Zhang, P Qu, Y Ji, W Zhang, G Gao, G Wang, S Song, et al.
Nature, 2020.

Physics for neuromorphic computing.
D Marković, A Mizrahi, D Querlioz, J Grollier.
Nature Reviews Physics, 2020.

Spiking Neural Network

Unsupervised learning of digit recognition using spike-timing-dependent plasticity.
PU Diehl, M Cook.
Frontiers in computational neuroscience, 2015. [Github1] [Github2]

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.
B Rueckauer, IA Lungu, Y Hu, M Pfeiffer, et al.
Front. Neurosci., 2017. [Github]

A comprehensive analysis on adversarial robustness of spiking neural networks.
S Sharmin, P Panda, SS Sarwar, C Lee, et al.
IJCNN, 2019.

Towards spike-based machine intelligence with neuromorphic computing.
K Roy, A Jaiswal, P Panda.
Nature, 2019.

Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures.
C Lee, SS Sarwar, P Panda, G Srinivasan, et al.
Frontiers in Neuroscience, 2020. [Github]

Deep spiking neural networks for large vocabulary automatic speech recognition.
J Wu, E Yılmaz, M Zhang, H Li, KC Tan.
Frontiers in Neuroscience, 2020. [Github]

Spiking-yolo: spiking neural network for energy-efficient object detection.
S Kim, S Park, B Na, S Yoon.
AAAI, 2020.

A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration.
Y Li, S Deng, X Dong, R Gong, et al.
ICML, 2021.

spikingjelly.
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch..
[Github]

Pure python implementation of SNN.
Pure python implementation of SNN.
[Github]

Optical Spiking Neural Network

A Leaky Integrate-and-Fire Laser Neuron for Ultrafast Cognitive Computing.
MA Nahmias, BJ Shastri, AN Tait, et al.
IEEE Journal of Selected Topics in Quantum Electronics, 2013, 19(5):1800212.

Recent progress in semiconductor excitable lasers for photonic spike processing.
PR Prucnal, BJ Shastri, TF de Lima, et al.
Advances in Optics and Photonics, 2016.

Machine learning with neuromorphic photonics.
TF De Lima, HT Peng, AN Tait, et al.
JLT, 2019.

All-optical spiking neurosynaptic networks with self-learning capabilities.
J Feldmann, N Youngblood, CD Wright, H Bhaskaran, et al.
Nature, 2019.

Integrated Neuromorphic Photonics: Synapses, Neurons, and Neural Networks.
X Guo, J Xiang, Y Zhang, Y Su.
Advanced Photonics Research, 2021.

Training a multi-layer photonic spiking neural network with modified supervised learning algorithm based on photonic STDP.
S Xiang, Z Ren, Y Zhang, Z Song, X Guo, et al.
JSTQE, 2021.

Hybrid Neural Network

Towards artificial general intelligence with hybrid Tianjic chip architecture.
J Pei, L Deng, S Song, M Zhao, Y Zhang, S Wu, et al.
Nature, 2019.

Unsupervised learning of a hierarchical spiking neural network for optical flow estimation: From events to global motion perception.
F Paredes-Vallés, KYW Scheper, et al.
PAMI, 2019.

Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks.
C Lee, AK Kosta, AZ Zhu, K Chaney, et al.
ECCV, 2020. [Github]

Rethinking the performance comparison between SNNS and ANNS.
L Deng, Y Wu, X Hu, L Liang, Y Ding, G Li, G Zhao, P Li.
Neural networks, 2020.

A Hybrid Compact Neural Architecture for Visual Place Recognition.
M Chancán, L Hernandez-Nunez, et al.
ICRA, 2020. [Github]

Reservoir Computing

Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach.
J Pathak, B Hunt, M Girvan, Z Lu, E Ott.
PRL, 2018.

Optical Reservoir Computing Using Multiple Light Scattering for Chaotic Systems Prediction.
J Dong, M Rafayelyan, F Krzakalaó, et al.
JSTQE, 2019. [Github]

Human action recognition with a large-scale brain-inspired photonic computer.
P Antonik, N Marsal, D Brunner, et al.
NMI, 2019.

Photonic neuromorphic information processing and reservoir computing.
A Lugnan, A Katumba, F Laporte, M Freiberger, et al.
ACS Photonics, 2020.

EchoTorch: Reservoir Computing with pyTorch.
A Python toolkit for Reservoir Computing and Echo State Network experimentation based on pyTorch. EchoTorch is the only Python module available to easily create Deep Reservoir Computing models.
[Github]

Bio-Computing

DNA-based programmable gate arrays for general-purpose DNA computing.
H Lv, N **e, M Li, M Dong, C Sun, Q Zhang, L Zhao, et al.
Nature, 2023. [Paper]

Fast and secure data accessing by using DNA computing for the cloud environment.
S Namasudra.
IEEE Transactions on Services Computing, 2020. [Paper]

An image encryption scheme based on a hybrid model of DNA computing, chaotic systems and hash functions.
EZ Zefreh.
Multimedia Tools and Applications, 2020. [Paper]

Memristor

Circuit elements with memory: memristors, memcapacitors, and meminductors.
M Di Ventra, YV Pershin, LO Chua.
Proceedings of the IEEE, 2009.

Memristors-From In-Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio-Inspired Computing.
A Mehonic, A Sebastian, B Rajendran, et al.
Advanced Intelligent Systems, 2020.

Fully hardware-implemented memristor convolutional neural network.
P Yao, H Wu, B Gao, J Tang, Q Zhang, W Zhang, et al.
Nature, 2020.

Event Camera

Video to Events: Recycling Video Datasets for Event Cameras.
D Gehrig, M Gehrig, J Hidalgo-Carrió, et al.
CVPR, 2020. [Github]

Comparing SNNs and RNNs on neuromorphic vision datasets: Similarities and differences.
W He, YJ Wu, L Deng, G Li, H Wang, Y Tian, W Ding, et al.
Neural Networks, 2020.

Event-based Vision: A Survey.
G Gallego, T Delbrück, G Orchard.
PAMI, 2020.

BCI

EEG-based user identification system using 1D-convolutional long short-term memory neural networks.
Y Sun, FPW Lo, B Lo.
Expert Systems with Applications, 2019.

Deep learning for electroencephalogram (EEG) classification tasks: a review.
A Craik, Y He, JL Contreras-Vidal.
Journal of neural engineering, 2019.

Reconfigurable nanophotonic silicon probes for sub-millisecond deep-brain optical stimulation.
A Mohanty, Q Li, MA Tadayon, SP Roberts, et al.
Nature Biomedical Engineering, 2020.

Hardware-software co-design for brain-computer interfaces.
I Karageorgos, K Sriram, J Veselý, M Wu, et al.
SC, 2020.

Brain–Computer Interface Software:A Review and Discussion.
P Stegman, CS Crawford, M Andujar, et al.
IEEE Transactions on Human-Machine Systems, 2020.

Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?
V Salari, S Rodrigues, E Saglamyurek, C Simon, et al.
ArXiv, 2021.