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Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-21735-7_8openalex-snapshot
Error-Backpropagation in Networks of Fractionally Predictive Spiking Neurons
inferredcites →https://doi.org/10.1016/j.conb.2014.01.004openalex-snapshot
To spike, or when to spike?
inferredcites →https://doi.org/10.1016/j.neunet.2016.07.006openalex-snapshot
A theory of local learning, the learning channel, and the optimality of backpropagation
inferredcites →https://doi.org/10.1016/j.neunet.2018.12.002openalex-snapshot
Deep learning in spiking neural networks
inferredcites →https://doi.org/10.1016/j.neuron.2014.03.026openalex-snapshot
Learning Precisely Timed Spikes
inferredcites →https://doi.org/10.1016/s0925-2312(01)00658-0openalex-snapshot
Error-backpropagation in temporally encoded networks of spiking neurons
inferredcites →https://doi.org/10.1017/cbo9781107447615openalex-snapshot
Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition
inferredcites →https://doi.org/10.1038/ncomms13276openalex-snapshot
Random synaptic feedback weights support error backpropagation for deep learning
inferredcites →https://doi.org/10.1038/nn.4241openalex-snapshot
Building functional networks of spiking model neurons
inferredcites →https://doi.org/10.1038/s41467-017-01827-3openalex-snapshot
Supervised learning in spiking neural networks with FORCE training
inferredcites →https://doi.org/10.1073/pnas.1604850113openalex-snapshot
Convolutional networks for fast, energy-efficient neuromorphic computing
inferredcites →https://doi.org/10.1109/ijcnn.2015.7280618openalex-snapshot
NormAD - Normalized Approximate Descent based supervised learning rule for spiking neurons
inferredcites →https://doi.org/10.1109/iscas.2017.8050529openalex-snapshot
Event-driven random backpropagation: Enabling neuromorphic deep learning machines
inferredcites →https://doi.org/10.1109/tnnls.2017.2726060openalex-snapshot
Supervised Learning Based on Temporal Coding in Spiking Neural Networks
inferredcites →https://doi.org/10.1162/neco.1989.1.2.270openalex-snapshot
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
inferredcites →https://doi.org/10.1162/neco.2006.18.6.1318openalex-snapshot
Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning
inferredcites →https://doi.org/10.1162/neco_a_00790openalex-snapshot
Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks
inferredcites →https://doi.org/10.1162/neco_a_01080openalex-snapshot
A Learning Framework for Winner-Take-All Networks with Stochastic Synapses
inferredcites →https://doi.org/10.1162/neco_a_01086openalex-snapshot
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks
inferredcites →https://doi.org/10.1207/s15516709cog0901_7openalex-snapshot
A Learning Algorithm for Boltzmann Machines*
inferredcites →https://doi.org/10.12751/nncn.bc2019.0013openalex-snapshot
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets
inferredcites →https://doi.org/10.1523/jneurosci.4098-12.2013openalex-snapshot
Matching Recall and Storage in Sequence Learning with Spiking Neural Networks
inferredcites →https://doi.org/10.3389/fncom.2014.00038openalex-snapshot
Stochastic variational learning in recurrent spiking networks
inferredcites →https://doi.org/10.3389/fnins.2016.00508openalex-snapshot
Training Deep Spiking Neural Networks Using Backpropagation
inferredcites →https://doi.org/10.3389/fnins.2017.00324openalex-snapshot
Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines
inferredcites →https://doi.org/10.3389/fnins.2018.00608openalex-snapshot
Deep Supervised Learning Using Local Errors
inferredcites →https://doi.org/10.48550/arxiv.1308.3432openalex-snapshot
Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
inferredcites →https://doi.org/10.48550/arxiv.1510.08829openalex-snapshot
Spiking Deep Networks with LIF Neurons
inferredcites →https://doi.org/10.48550/arxiv.1602.02830openalex-snapshot
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
inferredcites →https://doi.org/10.48550/arxiv.1609.01596openalex-snapshot
Direct Feedback Alignment Provides Learning in Deep Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1706.04159openalex-snapshot
Temporally Efficient Deep Learning with Spikes
inferredcites →https://doi.org/10.48550/arxiv.1810.08646openalex-snapshot
SLAYER: Spike Layer Error Reassignment in Time
inferredcites →https://doi.org/10.7554/elife.22901openalex-snapshot
Towards deep learning with segregated dendrites
inferredcites →https://doi.org/10.7554/elife.28295openalex-snapshot
Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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inferred← citeshttps://doi.org/10.5281/zenodo.20664819zenodo-depositor-declared
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inferred← citeshttps://doi.org/10.1007/978-3-031-19775-8_4openalex-snapshot
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Biomimetic oculomotor control with spiking neural networks
inferred← citeshttps://doi.org/10.1007/s00339-022-06365-4openalex-snapshot
Unconventional computing based on magnetic tunnel junction
inferred← citeshttps://doi.org/10.1007/s00371-021-02237-9openalex-snapshot
Multi-domain collaborative feature representation for robust visual object tracking
inferred← citeshttps://doi.org/10.1007/s00371-026-04460-8openalex-snapshot
SNN-driven aesthetic assessment: integrating color perception with spatiotemporal deep features
inferred← citeshttps://doi.org/10.1007/s00521-021-05832-yopenalex-snapshot
Quantized STDP-based online-learning spiking neural network
inferred← citeshttps://doi.org/10.1007/s00521-022-07910-1openalex-snapshot
Time-encoded multiplication-free spiking neural networks: application to data classification tasks
inferred← citeshttps://doi.org/10.1007/s00521-023-09135-2openalex-snapshot
Unsupervised character recognition with graphene memristive synapses
inferred← citeshttps://doi.org/10.1007/s00521-025-11760-yopenalex-snapshot
SPASRNN: boosting spiking recurrent neural networks using sparse gradient decent for semantic representation learning
inferred← citeshttps://doi.org/10.1007/s10278-023-00776-2openalex-snapshot
Skin Cancer Classification Using Deep Spiking Neural Network
inferred← citeshttps://doi.org/10.1007/s10489-023-04966-xopenalex-snapshot
Surrogate gradient scaling for directly training spiking neural networks
inferred← citeshttps://doi.org/10.1007/s10489-023-05056-8openalex-snapshot
An efficient pruning and fine-tuning method for deep spiking neural network
inferred← citeshttps://doi.org/10.1007/s10489-024-06128-zopenalex-snapshot
Adaptive spiking neuron with population coding for a residual spiking neural network
inferred← citeshttps://doi.org/10.1007/s10489-025-06354-zopenalex-snapshot
Precise spiking neurons for fitting any activation function in ANN-to-SNN Conversion
inferred← citeshttps://doi.org/10.1007/s10827-023-00861-zopenalex-snapshot
Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification
inferred← citeshttps://doi.org/10.1007/s10958-026-08653-3openalex-snapshot
BARRIER CROSSING PROBABILITY PREDICTION USING SPIKING NEURAL NETWORKS UNDER THE KOU JUMP-DIFFUSION MODEL
inferred← citeshttps://doi.org/10.1007/s11042-024-19665-zopenalex-snapshot
Multi-level brain-guided fusion to reinforce spiking neural network in image classification
inferred← citeshttps://doi.org/10.1007/s11042-026-21480-7openalex-snapshot
Spiking GAN for underwater image enhancement
inferred← citeshttps://doi.org/10.1007/s11063-023-11247-8openalex-snapshot
Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
inferred← citeshttps://doi.org/10.1007/s11063-023-11274-5openalex-snapshot
CIRM-SNN: Certainty Interval Reset Mechanism Spiking Neuron for Enabling High Accuracy Spiking Neural Network
inferred← citeshttps://doi.org/10.1007/s11063-025-11832-zopenalex-snapshot
A Four-Stage Structural Evolution Framework for Spiking Neural Networks: A Review and Perspective from Binary ANN to Event-Driven Models
inferred← citeshttps://doi.org/10.1007/s11071-024-09525-8openalex-snapshot
Spiking SiamFC++: deep spiking neural network for object tracking
inferred← citeshttps://doi.org/10.1007/s11071-025-11727-7openalex-snapshot
A Review of Recent Developments in Neuromorphic Computing Based on Emerging Memory Devices
inferred← citeshttps://doi.org/10.1007/s11227-026-08924-5openalex-snapshot
ALSM: a parallel dual-stream attentive liquid state machine for robust spatio-temporal visual recognition
inferred← citeshttps://doi.org/10.1007/s11263-024-02046-2openalex-snapshot
Error-Aware Conversion from ANN to SNN via Post-training Parameter Calibration
inferred← citeshttps://doi.org/10.1007/s11263-024-02236-yopenalex-snapshot
Towards Ultra High-Speed Hyperspectral Imaging by Integrating Compressive and Neuromorphic Sampling
inferred← citeshttps://doi.org/10.1007/s11263-026-02927-8openalex-snapshot
Rethinking Neuromorphic Object Detection with Hybrid Dynamic Interaction Transformers
inferred← citeshttps://doi.org/10.1007/s11265-022-01772-5openalex-snapshot
Efficient Neuromorphic Signal Processing with Resonator Neurons
inferred← citeshttps://doi.org/10.1007/s11432-023-4000-xopenalex-snapshot
Spiking neural networks in intelligent control systems: a perspective
inferred← citeshttps://doi.org/10.1007/s11432-023-4565-6openalex-snapshot
SpikeCV: open a continuous computer vision era
inferred← citeshttps://doi.org/10.1007/s11571-024-10133-wopenalex-snapshot
Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks
inferred← citeshttps://doi.org/10.1007/s11571-024-10199-6openalex-snapshot
Sg-snn: a self-organizing spiking neural network based on temporal information
inferred← citeshttps://doi.org/10.1007/s11571-025-10384-1openalex-snapshot
Discrete memristive spiking neural networks: investigating information flow, synchronization, and emergent intelligence
inferred← citeshttps://doi.org/10.1007/s11571-026-10470-yopenalex-snapshot
Redefining spiking neural networks through the lens of dynamical superspace
inferred← citeshttps://doi.org/10.1007/s11760-023-02574-3openalex-snapshot
Spike representation of depth image sequences and its application to hand gesture recognition with spiking neural network
inferred← citeshttps://doi.org/10.1007/s12065-026-01169-4openalex-snapshot
Training-free multi-objective evolutionary search for efficient spiking neural networks
inferred← citeshttps://doi.org/10.1007/s12083-026-02212-yopenalex-snapshot
SCNNTraffic: A lightweight and energy-efficient traffic classification method based on spiking convolution neural networks
inferred← citeshttps://doi.org/10.1007/s12293-022-00373-wopenalex-snapshot
A hierarchical taxonomic survey of spiking neural networks
inferred← citeshttps://doi.org/10.1007/s12530-025-09755-0openalex-snapshot
Spiking neural networks: a comprehensive review of diverse applications, research progress, challenges and future research directions
inferred← citeshttps://doi.org/10.1007/s12559-022-09994-2openalex-snapshot
Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation
inferred← citeshttps://doi.org/10.1007/s12559-026-10637-zopenalex-snapshot
Sequence-SOD: Bio-inspired Sequence-aware Spiking Object Detection for Event Cameras
inferred← citeshttps://doi.org/10.1007/s13042-025-02788-6openalex-snapshot
TL-SNN: event-driven visual-tactile learning with temporal and location spiking neurons
inferred← citeshttps://doi.org/10.1007/s13534-024-00404-0openalex-snapshot
Spiking neural networks for physiological and speech signals: a review
inferred← citeshttps://doi.org/10.1007/s40747-023-00983-yopenalex-snapshot
BPLC + NOSO: backpropagation of errors based on latency code with neurons that only spike once at most
inferred← citeshttps://doi.org/10.1007/s40747-023-01245-7openalex-snapshot
Dynamic layer-span connecting spiking neural networks with backpropagation training
inferred← citeshttps://doi.org/10.1007/s40747-024-01607-9openalex-snapshot
Theoretical understanding of gradients of spike functions as boolean functions
inferred← citeshttps://doi.org/10.1007/s40815-022-01395-9openalex-snapshot
Adaptive Fuzzy Population Coding Method for Spiking Neural Networks
inferred← citeshttps://doi.org/10.1007/s40998-025-00808-3openalex-snapshot
Medical Image Classification Using Lightweight Deep Spiking Neural Network
inferred← citeshttps://doi.org/10.1007/s41965-024-00156-xopenalex-snapshot
Evolutionary spiking neural networks: a survey
inferred← citeshttps://doi.org/10.1007/s42064-024-0256-yopenalex-snapshot
Energy efficiency analysis of Spiking Neural Networks for space applications
inferred← citeshttps://doi.org/10.1016/b978-0-323-98829-2.00006-2openalex-snapshot
Neuromorphic computing
inferred← citeshttps://doi.org/10.1016/b978-0-44-332820-6.00014-8openalex-snapshot
An ANN–SNN algorithm suitable for ultra energy efficient image classification
inferred← citeshttps://doi.org/10.1016/j.aei.2025.103273openalex-snapshot
Denoising autoencoder multilayer perceptron spiking neural network for isonicotinic acid yield prediction on real industrial dataset
inferred← citeshttps://doi.org/10.1016/j.asoc.2024.112120openalex-snapshot
Adjusted SpikeProp algorithm for recurrent spiking neural networks with LIF neurons
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.113471openalex-snapshot
An empirical study on optimizing binary spiking neural networks for neuromorphic computing
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.114345openalex-snapshot
TactiCore: An event-driven haptic recognition framework with KD-tree and Z-order fusion for high-precision robotic tactile perception
inferred← citeshttps://doi.org/10.1016/j.asoc.2026.114984openalex-snapshot
Homeostatically-regulated liquid state machines for robust and efficient arrhythmia classification: A neuromorphic approach
inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106745openalex-snapshot
motorSRNN: A spiking recurrent neural network inspired by brain topology for the effective and efficient decoding of cortical spike trains
inferred← citeshttps://doi.org/10.1016/j.bspc.2024.107000openalex-snapshot
Constructing lightweight and efficient spiking neural networks for EEG-based motor imagery classification
inferred← citeshttps://doi.org/10.1016/j.bspc.2025.109418openalex-snapshot
Combining hybrid spiking neuron with inception module for motor imagery classification
inferred← citeshttps://doi.org/10.1016/j.bspc.2026.109684openalex-snapshot
SCAF-Net: A spiking cross-modal attention fusion network for multimodal emotion recognition
inferred← citeshttps://doi.org/10.1016/j.bspc.2026.111215openalex-snapshot
Efficient sEMG decoding with hybrid, multi-timescale neural networks
inferred← citeshttps://doi.org/10.1016/j.celrep.2024.113785openalex-snapshot
Functional subtypes of synaptic dynamics in mouse and human
inferred← citeshttps://doi.org/10.1016/j.chaos.2025.117663openalex-snapshot
Understanding the training dynamics of CoLaNET by its simplified model
inferred← citeshttps://doi.org/10.1016/j.chaos.2026.119046openalex-snapshot
Progressive multi-level self-distillation for spatiotemporal information fusion in spiking neural networks
inferred← citeshttps://doi.org/10.1016/j.cmpb.2023.107927openalex-snapshot
EESCN: A novel spiking neural network method for EEG-based emotion recognition
inferred← citeshttps://doi.org/10.1016/j.cmpbup.2024.100171openalex-snapshot
A sustainable neuromorphic framework for disease diagnosis using digital medical imaging
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.109225openalex-snapshot
Real-time sub-milliwatt epilepsy detection implemented on a spiking neural network edge inference processor
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110173openalex-snapshot
Pediatric chest X-ray diagnosis using neuromorphic models
inferred← citeshttps://doi.org/10.1016/j.conb.2020.04.002openalex-snapshot
New light on cortical neuropeptides and synaptic network plasticity
inferred← citeshttps://doi.org/10.1016/j.craph.2026.100041openalex-snapshot
EAVS-TF: A bio-inspired spiking neural network for energy-efficient multimodal emotion recognition
inferred← citeshttps://doi.org/10.1016/j.cviu.2026.104873openalex-snapshot
Dynamic training of spiking neural networks with loss-based stochastic latency
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109415openalex-snapshot
Spiking neural networks for autonomous driving: A review
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109756openalex-snapshot
Data-driven spiking neural networks for intelligent fault detection in vehicle lithium-ion battery systems
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111055openalex-snapshot
Toward transforming space exploration with artificial intelligence neuromorphic computing
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.123758openalex-snapshot
Data-efficient surrogate modeling using meta-learning and physics-informed deep learning approaches
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.128850openalex-snapshot
MMT-SNN: Markovian decision and multi-threshold spike delivery integrated adaptive spiking neural network for tactile object recognition
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.132570openalex-snapshot
STCA-SNN: Spatio-temporal coordinate attention for spiking neural networks
inferred← citeshttps://doi.org/10.1016/j.future.2026.108805openalex-snapshot
Distributed neuromorphic computing with temporal co-optimization of spiking dynamics
inferred← citeshttps://doi.org/10.1016/j.icte.2025.07.006openalex-snapshot
FTP: Filtered Temporal-Population for time series encoding in Spiking Neural Network
inferred← citeshttps://doi.org/10.1016/j.ijepes.2026.112020openalex-snapshot
Energy-efficient quantum-spiking multi-agent reinforcement learning for adaptive energy management in microgrid networks
inferred← citeshttps://doi.org/10.1016/j.inffus.2023.101943openalex-snapshot
A novel Out-of-Distribution detection approach for Spiking Neural Networks: Design, fusion, performance evaluation and explainability
inferred← citeshttps://doi.org/10.1016/j.inffus.2026.104322openalex-snapshot
Wavelets as multiscale physical priors for deep learning: A taxonomy of fusion paths from structural embedding to neural redefinition
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