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The Future of Brain-inspired Computing: A Survey on Large-scale Spiking Neural Networks

https://doi.org/10.2139/ssrn.5166170
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/aelm.201600090openalex-snapshot
Memristors for Energy‐Efficient New Computing Paradigms
inferredcites →https://doi.org/10.1007/978-3-030-01267-0_44openalex-snapshot
Bi-Real Net: Enhancing the Performance of 1-Bit CNNs with Improved Representational Capability and Advanced Training Algorithm
inferredcites →https://doi.org/10.1007/s11263-014-0788-3openalex-snapshot
Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1016/j.neunet.2021.09.022openalex-snapshot
Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing
inferredcites →https://doi.org/10.1016/j.neunet.2024.106172openalex-snapshot
Efficient spiking neural network design via neural architecture search
inferredcites →https://doi.org/10.1016/j.neunet.2024.106393openalex-snapshot
Efficient tensor decomposition-based filter pruning
inferredcites →https://doi.org/10.1016/s0042-6989(02)00298-5openalex-snapshot
Surfing a spike wave down the ventral stream
inferredcites →https://doi.org/10.1016/s0893-6080(97)00011-7openalex-snapshot
Networks of spiking neurons: The third generation of neural network models
inferredcites →https://doi.org/10.1038/376033a0openalex-snapshot
Pattern recognition computation using action potential timing for stimulus representation
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1038/nature16961openalex-snapshot
Mastering the game of Go with deep neural networks and tree search
inferredcites →https://doi.org/10.1038/nn1643openalex-snapshot
The tempotron: a neuron that learns spike timing–based decisions
inferredcites →https://doi.org/10.1038/s41586-018-0180-5openalex-snapshot
Equivalent-accuracy accelerated neural-network training using analogue memory
inferredcites →https://doi.org/10.1038/s41586-019-1677-2openalex-snapshot
Towards spike-based machine intelligence with neuromorphic computing
inferredcites →https://doi.org/10.1038/s43588-021-00184-yopenalex-snapshot
Opportunities for neuromorphic computing algorithms and applications
inferredcites →https://doi.org/10.1088/2634-4386/ac8828openalex-snapshot
Meta-learning spiking neural networks with surrogate gradient descent
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/access.2022.3179968openalex-snapshot
Spiking Neural Networks: A Survey
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.574openalex-snapshot
Deep Learning with Low Precision by Half-Wave Gaussian Quantization
inferredcites →https://doi.org/10.1109/cvpr.2017.761openalex-snapshot
Weighted-Entropy-Based Quantization for Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.01167openalex-snapshot
Regularizing Activation Distribution for Training Binarized Deep Networks
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00232openalex-snapshot
Forward and Backward Information Retention for Accurate Binary Neural Networks
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01357openalex-snapshot
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network
inferredcites →https://doi.org/10.1109/cvpr52688.2022.00042openalex-snapshot
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks
inferredcites →https://doi.org/10.1109/cvpr52729.2023.00762openalex-snapshot
Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation
inferredcites →https://doi.org/10.1109/iccv48922.2021.00266openalex-snapshot
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
inferredcites →https://doi.org/10.1109/iccv48922.2021.00515openalex-snapshot
ReCU: Reviving the Dead Weights in Binary Neural Networks
inferredcites →https://doi.org/10.1109/iccv48922.2021.00521openalex-snapshot
Improving Low-Precision Network Quantization via Bin Regularization
inferredcites →https://doi.org/10.1109/iccv48922.2021.00532openalex-snapshot
Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss
inferredcites →https://doi.org/10.1109/iedm.2015.7409623openalex-snapshot
Neuromorphic architectures for spiking deep neural networks
inferredcites →https://doi.org/10.1109/ijcnn.2015.7280696openalex-snapshot
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
inferredcites →https://doi.org/10.1109/iscas.2010.5537164openalex-snapshot
Event-based 64-channel binaural silicon cochlea with Q enhancement mechanisms
inferredcites →https://doi.org/10.1109/iscas.2018.8351295openalex-snapshot
Conversion of analog to spiking neural networks using sparse temporal coding
inferredcites →https://doi.org/10.1109/jproc.2015.2496679openalex-snapshot
To Spike or Not to Spike: That Is the Question
inferredcites →https://doi.org/10.1109/jproc.2020.3045625openalex-snapshot
Brain-Inspired Learning on Neuromorphic Substrates
inferredcites →https://doi.org/10.1109/jssc.2012.2230553openalex-snapshot
A 128$\,\times$128 1.5% Contrast Sensitivity 0.9% FPN 3 µs Latency 4 mW Asynchronous Frame-Free Dynamic Vision Sensor Using Transimpedance Preamplifiers
inferredcites →https://doi.org/10.1109/jssc.2014.2342715openalex-snapshot
A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor
inferredcites →https://doi.org/10.1109/mm.2018.112130359openalex-snapshot
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
inferredcites →https://doi.org/10.1109/msp.2012.2205597openalex-snapshot
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
inferredcites →https://doi.org/10.1109/tcds.2020.2971655openalex-snapshot
Deep Spiking Neural Networks With Binary Weights for Object Recognition
inferredcites →https://doi.org/10.1109/tcds.2021.3073846openalex-snapshot
An Efficient Learning Algorithm for Direct Training Deep Spiking Neural Networks
inferredcites →https://doi.org/10.1109/tcds.2023.3327081openalex-snapshot
Transformer-Based Spiking Neural Networks for Multimodal Audiovisual Classification
inferredcites →https://doi.org/10.1109/tetci.2024.3359539openalex-snapshot
IM-LIF: Improved Neuronal Dynamics With Attention Mechanism for Direct Training Deep Spiking Neural Network
inferredcites →https://doi.org/10.1109/tnnls.2021.3111897openalex-snapshot
DIET-SNN: A Low-Latency Spiking Neural Network With D irect I nput E ncoding and Leakage and T hreshold Optimization
inferredcites →https://doi.org/10.1109/tnnls.2023.3263008openalex-snapshot
Backpropagation-Based Learning Techniques for Deep Spiking Neural Networks: A Survey
inferredcites →https://doi.org/10.1109/tnnls.2024.3355393openalex-snapshot
Advancing Spiking Neural Networks Toward Deep Residual Learning
inferredcites →https://doi.org/10.1109/tnnls.2024.3370294openalex-snapshot
Enhanced Network Compression Through Tensor Decompositions and Pruning
inferredcites →https://doi.org/10.1109/tpami.2013.71openalex-snapshot
Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate Coding and Coincidence Processing--Application to Feedforward ConvNets
inferredcites →https://doi.org/10.1109/tpami.2023.3241201openalex-snapshot
Attention Spiking Neural Networks
inferredcites →https://doi.org/10.1113/jphysiol.1952.sp004764openalex-snapshot
A quantitative description of membrane current and its application to conduction and excitation in nerve
inferredcites →https://doi.org/10.1126/sciadv.1701329openalex-snapshot
Ultralow power artificial synapses using nanotextured magnetic Josephson junctions
inferredcites →https://doi.org/10.1126/science.1254642openalex-snapshot
A million spiking-neuron integrated circuit with a scalable communication network and interface
inferredcites →https://doi.org/10.1145/3387902.3392613openalex-snapshot
Verified instruction-level energy consumption measurement for NVIDIA GPUs
inferredcites →https://doi.org/10.1145/3571155openalex-snapshot
Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware
inferredcites →https://doi.org/10.1145/3664647.3680620openalex-snapshot
Towards High-performance Spiking Transformers from ANN to SNN Conversion
inferredcites →https://doi.org/10.1152/jn.1968.31.4.574openalex-snapshot
Synaptic noise and other sources of randomness in motoneuron interspike intervals.
inferredcites →https://doi.org/10.1162/neco.2009.11-08-901openalex-snapshot
Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting
inferredcites →https://doi.org/10.1162/neco_a_01086openalex-snapshot
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks
inferredcites →https://doi.org/10.1523/jneurosci.18-24-10464.1998openalex-snapshot
Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type
inferredcites →https://doi.org/10.1609/aaai.v33i01.33011311openalex-snapshot
Direct Training for Spiking Neural Networks: Faster, Larger, Better
inferredcites →https://doi.org/10.1609/aaai.v34i07.6900openalex-snapshot
Sparsity-Inducing Binarized Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v35i3.16306openalex-snapshot
SA-BNN: State-Aware Binary Neural Network
inferredcites →https://doi.org/10.1609/aaai.v38i15.29616openalex-snapshot
Memory-Efficient Reversible Spiking Neural Networks
inferredcites →https://doi.org/10.24963/ijcai.2023/335openalex-snapshot
Learnable Surrogate Gradient for Direct Training Spiking Neural Networks
inferredcites →https://doi.org/10.24963/ijcai.2024/348openalex-snapshot
One-step Spiking Transformer with a Linear Complexity
inferredcites →https://doi.org/10.3389/fnins.2013.00178openalex-snapshot
Real-time classification and sensor fusion with a spiking deep belief network
inferredcites →https://doi.org/10.3389/fnins.2016.00508openalex-snapshot
Training Deep Spiking Neural Networks Using Backpropagation
inferredcites →https://doi.org/10.3389/fnins.2017.00682openalex-snapshot
Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification
inferredcites →https://doi.org/10.3389/fnins.2021.773954openalex-snapshot
Revisiting Batch Normalization for Training Low-Latency Deep Spiking Neural Networks From Scratch
inferredcites →https://doi.org/10.48550/arxiv.1412.4564openalex-snapshot
MatConvNet - Convolutional Neural Networks for MATLAB
inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
inferredcites →https://doi.org/10.48550/arxiv.1511.06393openalex-snapshot
Fixed Point Quantization of Deep Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.2002.10085openalex-snapshot
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2103.00476openalex-snapshot
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2201.12738openalex-snapshot
AutoSNN: Towards Energy-Efficient Spiking Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2205.00459openalex-snapshot
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation
inferredcites →https://doi.org/10.48550/arxiv.2406.03470openalex-snapshot
SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN

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inferred← citeshttps://doi.org/10.1016/j.compeleceng.2026.111330openalex-snapshot
Evidence based rehabilitation enhancement through surface electromyography and wearable sensor technologies
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