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Conversion of analog to spiking neural networks using sparse temporal coding
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.neunet.2014.09.003openalex-snapshot
Deep learning in neural networks: An overview
inferredcites →https://doi.org/10.1016/s0893-6080(01)00083-1openalex-snapshot
Spike-based strategies for rapid processing
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
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.2017.8050527openalex-snapshot
Fast classification using sparsely active spiking networks
inferredcites →https://doi.org/10.1109/isscc.2014.6757323openalex-snapshot
1.1 Computing's energy problem (and what we can do about it)
inferredcites →https://doi.org/10.1109/jssc.2007.914337openalex-snapshot
A 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor
inferredcites →https://doi.org/10.1109/tnnls.2014.2362542openalex-snapshot
Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network
inferredcites →https://doi.org/10.1109/tnnls.2017.2726060openalex-snapshot
Supervised Learning Based on Temporal Coding in Spiking Neural Networks
inferredcites →https://doi.org/10.1109/tnnls.2018.2852335openalex-snapshot
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps
inferredcites →https://doi.org/10.1109/tpami.2015.2392947openalex-snapshot
HFirst: A Temporal Approach to Object Recognition
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/10.1162/08997660152002852openalex-snapshot
Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex
inferredcites →https://doi.org/10.13140/rg.2.2.10967.06566openalex-snapshot
Training Spiking Deep Networks for Neuromorphic Hardware
inferredcites →https://doi.org/10.3389/fncom.2015.00099openalex-snapshot
Unsupervised learning of digit recognition using spike-timing-dependent plasticity
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/10.48550/arxiv.1609.02053openalex-snapshot
Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1609.07061openalex-snapshot
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
inferredcites →https://doi.org/10.48550/arxiv.1612.04052openalex-snapshot
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
inferredcites →https://doi.org/10.5167/uzh-132639openalex-snapshot
Supervised learning based on temporal coding in spiking neural networks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4113829openalex-extracted
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inferred← citeshttps://doi.org/10.2139/ssrn.4876812openalex-extracted
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inferred← citeshttps://doi.org/10.1002/aisy.202300383openalex-snapshot
Toward a Lossless Conversion for Spiking Neural Networks with Negative‐Spike Dynamics
inferred← citeshttps://doi.org/10.1007/978-3-030-58607-2_23openalex-snapshot
Deep Spiking Neural Network: Energy Efficiency Through Time Based Coding
inferred← citeshttps://doi.org/10.1007/978-3-030-64556-4_5openalex-snapshot
Biologically Inspired Sleep Algorithm for Variational Auto-Encoders
inferred← citeshttps://doi.org/10.1007/978-3-030-92525-3_3openalex-snapshot
Learning in Neuromorphic Systems
inferred← citeshttps://doi.org/10.1007/978-3-031-16035-6_7openalex-snapshot
Biologically Inspired Variational Auto-Encoders for Adversarial Robustness
inferred← citeshttps://doi.org/10.1007/978-3-031-55673-9_1openalex-snapshot
SNN vs. CNN Implementations on FPGAs: An Empirical Evaluation
inferred← citeshttps://doi.org/10.1007/978-3-031-72359-9_27openalex-snapshot
Learning in Recurrent Spiking Neural Networks with Sparse Full-FORCE Training
inferred← citeshttps://doi.org/10.1007/978-3-031-72949-2_1openalex-snapshot
FARSE-CNN: Fully Asynchronous, Recurrent and Sparse Event-Based CNN
inferred← citeshttps://doi.org/10.1007/978-3-032-02728-3_20openalex-snapshot
Comparative Analysis of Spiking Neurons Mathematical Models Training Using Surrogate Gradients Techniques
inferred← citeshttps://doi.org/10.1007/978-3-032-07448-5_24openalex-snapshot
Benchmarking ANN-to-SNN Conversion: Dataset-Dependent Analysis of Accuracy, Latency, and Spike Efficiency
inferred← citeshttps://doi.org/10.1007/978-981-97-6125-8_2openalex-snapshot
Toward Efficient Deep Spiking Neuron Networks: A Survey on Compression
inferred← citeshttps://doi.org/10.1007/978-981-99-8067-3_7openalex-snapshot
RMPE:Reducing Residual Membrane Potential Error for Enabling High-Accuracy and Ultra-low-latency Spiking Neural Networks
inferred← citeshttps://doi.org/10.1007/s00521-023-08567-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00521-024-09529-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11063-021-10562-2openalex-snapshot
A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks
inferred← citeshttps://doi.org/10.1007/s11063-021-10680-xopenalex-snapshot
BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning
inferred← citeshttps://doi.org/10.1007/s11071-024-10146-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11633-022-1388-2openalex-snapshot
Symmetric-threshold ReLU for Fast and Nearly Lossless ANN-SNN Conversion
inferred← citeshttps://doi.org/10.1007/s12559-024-10355-4openalex-snapshot
A Novel Multi-Type Image Coding Method Acting on Supervised Hierarchical Deep Spiking Convolutional Neural Networks for Image Classification
inferred← citeshttps://doi.org/10.1007/s40820-023-01289-xopenalex-snapshot
Chalcogenide Ovonic Threshold Switching Selector
inferred← citeshttps://doi.org/10.1016/j.neucom.2019.07.009openalex-snapshot
Fast and robust learning in Spiking Feed-forward Neural Networks based on Intrinsic Plasticity mechanism
inferred← citeshttps://doi.org/10.1016/j.neucom.2020.11.052openalex-snapshot
STiDi-BP: Spike time displacement based error backpropagation in multilayer spiking neural networks
inferred← citeshttps://doi.org/10.1016/j.neucom.2021.05.020openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.neucom.2021.10.080openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.neucom.2023.126234openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128655openalex-snapshot
A two-stage strategy for brain-inspired unsupervised learning in spiking neural networks
inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128662openalex-snapshot
Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks
inferred← citeshttps://doi.org/10.1016/j.sse.2022.108481openalex-snapshot
Demonstration of integrate-and-fire neuron circuit for spiking neural networks
inferred← citeshttps://doi.org/10.1017/s1759078723001575openalex-snapshot
RT-SCNNs: real-time spiking convolutional neural networks for a novel hand gesture recognition using time-domain mm-wave radar data
inferred← citeshttps://doi.org/10.1038/s41467-024-51110-5openalex-snapshot
High-performance deep spiking neural networks with 0.3 spikes per neuron
inferred← citeshttps://doi.org/10.1038/s41467-024-53827-9openalex-snapshot
The backpropagation algorithm implemented on spiking neuromorphic hardware
inferred← citeshttps://doi.org/10.1038/s41928-025-01433-yopenalex-snapshot
A biologically inspired artificial neuron with intrinsic plasticity based on monolayer molybdenum disulfide
inferred← citeshttps://doi.org/10.1038/s42256-021-00311-4openalex-snapshot
Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes
inferred← citeshttps://doi.org/10.1088/1361-6528/ac7241openalex-snapshot
A complementary resistive switching neuron
inferred← citeshttps://doi.org/10.1088/2634-4386/ac24f5openalex-snapshot
A system design perspective on neuromorphic computer processors
inferred← citeshttps://doi.org/10.1088/2634-4386/ac5ac5openalex-snapshot
mlGeNN: accelerating SNN inference using GPU-enabled neural networks
inferred← citeshttps://doi.org/10.1088/2634-4386/ade183openalex-snapshot
NeuBridge: bridging quantized activations and spiking neurons for ANN-SNN conversion
inferred← citeshttps://doi.org/10.1101/2021.07.14.452347openalex-snapshot
Spike-based symbolic computations on bit strings and numbers
inferred← citeshttps://doi.org/10.1109/access.2020.2994360openalex-snapshot
Efficient Spiking Neural Networks With Logarithmic Temporal Coding
inferred← citeshttps://doi.org/10.1109/access.2021.3083056openalex-snapshot
Spiking Neural Networks With Time-to-First-Spike Coding Using TFT-Type Synaptic Device Model
inferred← citeshttps://doi.org/10.1109/access.2022.3149577openalex-snapshot
Neuron Circuits for Low-Power Spiking Neural Networks Using Time-To-First-Spike Encoding
inferred← citeshttps://doi.org/10.1109/access.2022.3179968openalex-snapshot
Spiking Neural Networks: A Survey
inferred← citeshttps://doi.org/10.1109/access.2022.3187033openalex-snapshot
Spiking Neural Networks Trained via Proxy
inferred← citeshttps://doi.org/10.1109/access.2023.3278098openalex-snapshot
Direct Signal Encoding With Analog Resonate-and-Fire Neurons
inferred← citeshttps://doi.org/10.1109/access.2024.3479968openalex-snapshot
Event-Based Spiking Neural Networks for Object Detection: A Review of Datasets, Architectures, Learning Rules, and Implementation
inferred← citeshttps://doi.org/10.1109/access.2025.3580582openalex-snapshot
Implementing Holographic Reduced Representations for Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/aicas.2019.8771624openalex-snapshot
Conversion of Synchronous Artificial Neural Network to Asynchronous Spiking Neural Network using sigma-delta quantization
inferred← citeshttps://doi.org/10.1109/asap61560.2024.00041openalex-snapshot
A Convolutional Spiking Neural Network Accelerator with the Sparsity-Aware Memory and Compressed Weights
inferred← citeshttps://doi.org/10.1109/coins51742.2021.9524272openalex-snapshot
A Unipolar-based Stochastic LIF Neuron Design for Low-cost Spiking Neural Network
inferred← citeshttps://doi.org/10.1109/dac18072.2020.9218689openalex-snapshot
T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
inferred← citeshttps://doi.org/10.1109/dac18074.2021.9586195openalex-snapshot
Noise-Robust Deep Spiking Neural Networks with Temporal Information
inferred← citeshttps://doi.org/10.1109/ddecs54261.2022.9770138openalex-snapshot
Dependability of Alternative Computing Paradigms for Machine Learning: hype or hope?
inferred← citeshttps://doi.org/10.1109/dsd64264.2024.00032openalex-snapshot
DSL-Based SNN Accelerator Design Using Chisel
inferred← citeshttps://doi.org/10.1109/dslw51110.2021.9523415openalex-snapshot
BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning
inferred← citeshttps://doi.org/10.1109/embc46164.2021.9629621openalex-snapshot
Demonstrating the Viability of Mapping Deep Learning Based EEG Decoders to Spiking Networks on Low-powered Neuromorphic Chips
inferred← citeshttps://doi.org/10.1109/i5cps67958.2026.11451769openalex-snapshot
Efficient Human Facial Emotion Recognition by SNN inspired CNN Features Based model on Video data
inferred← citeshttps://doi.org/10.1109/icassp43922.2022.9746792openalex-snapshot
A Hybrid Learning Framework for Deep Spiking Neural Networks with One-Spike Temporal Coding
inferred← citeshttps://doi.org/10.1109/icc45855.2022.9838849openalex-snapshot
Policy-based Fully Spiking Reservoir Computing for Multi-Agent Distributed Dynamic Spectrum Access
inferred← citeshttps://doi.org/10.1109/icicm66614.2025.11315984openalex-snapshot
A Neuron-and-Synapse Unit Circuit with Information Propagation Function for Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/icons62911.2024.00012openalex-snapshot
Stochastic Spiking Neural Networks with First-to-Spike Coding
inferred← citeshttps://doi.org/10.1109/iconscept57958.2023.10170596openalex-snapshot
Spike-Aware Training and Timing Window Optimization for Energy-Efficient Inference in Conversion-Based Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/icpr48806.2021.9413206openalex-snapshot
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner- Take-All Circuits
inferred← citeshttps://doi.org/10.1109/ijcnn48605.2020.9207452openalex-snapshot
Minibatch Processing for Speed-up and Scalability of Spiking Neural Network Simulation
inferred← citeshttps://doi.org/10.1109/ijcnn52387.2021.9533837openalex-snapshot
Temporal Pattern Coding in Deep Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/ijcnn55064.2022.9891951openalex-snapshot
Deep Phasor Networks: Connecting Conventional and Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/ijcnn55064.2022.9892262openalex-snapshot
Spiking-GAN: A Spiking Generative Adversarial Network Using Time-To-First-Spike Coding
inferred← citeshttps://doi.org/10.1109/iscas45731.2020.9180918openalex-snapshot
A Novel Conversion Method for Spiking Neural Network using Median Quantization
inferred← citeshttps://doi.org/10.1109/iscas48785.2022.9937951openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iscas51556.2021.9401667openalex-snapshot
Reducing Latency in a Converted Spiking Video Segmentation Network
inferred← citeshttps://doi.org/10.1109/iscmi53840.2021.9654818openalex-snapshot
RetinaNet Object Detector Based on Analog-to-Spiking Neural Network Conversion
inferred← citeshttps://doi.org/10.1109/islped.2019.8824897openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/isocc47750.2019.9078507openalex-snapshot
An RRAM-based Analog Neuron Design for the Weighted Spiking Neural network
inferred← citeshttps://doi.org/10.1109/jetcas.2019.2951121openalex-snapshot
Asynchronous Spiking Neurons, the Natural Key to Exploit Temporal Sparsity
inferred← citeshttps://doi.org/10.1109/jiot.2024.3349533openalex-snapshot
SNNAuth: Sensor-Based Continuous Authentication on Smartphones Using Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/jproc.2025.3581995openalex-snapshot
EMG Acquisition and Processing for Hand Movement Decoding on Embedded Systems: State of the Art and Challenges
inferred← citeshttps://doi.org/10.1109/jsen.2024.3453927openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/lcomm.2021.3050207openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/lmwc.2021.3125959openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/mc.2022.3144397openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/micro61859.2024.00085openalex-snapshot
ActiveN: A Scalable and Flexibly-Programmable Event-Driven Neuromorphic Processor
inferred← citeshttps://doi.org/10.1109/msp.2019.2935234openalex-snapshot
An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
inferred← citeshttps://doi.org/10.1109/radarconf2351548.2023.10149797openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tbcas.2022.3181808openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tc.2022.3162708openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tcad.2022.3197512openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tcad.2023.3264167openalex-snapshot
HyperSpikeASIC: Accelerating Event-Based Workloads With HyperDimensional Computing and Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/tcasai.2024.3520011openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tcds.2021.3139444openalex-snapshot
Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/tcds.2023.3334010openalex-snapshot
LC-TTFS: Toward Lossless Network Conversion for Spiking Neural Networks With TTFS Coding
inferred← citeshttps://doi.org/10.1109/tcds.2024.3383428openalex-snapshot
BitSNNs: Revisiting Energy-Efficient Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/tcsi.2024.3450966openalex-snapshot
SYNtzulu: A Tiny RISC-V-Controlled SNN Processor for Real-Time Sensor Data Analysis on Low-Power FPGAs
inferred← citeshttps://doi.org/10.1109/tcsii.2023.3277784openalex-snapshot
Design of Time-Encoded Spiking Neural Networks in 7-nm CMOS Technology
inferred← citeshttps://doi.org/10.1109/ted.2022.3207707openalex-snapshot
Highly Linear Analog Spike Processing Block Integrated With an AND-Type Flash Array and CMOS Neuron Circuits
inferred← citeshttps://doi.org/10.1109/tetc.2024.3386893openalex-snapshot
One-Spike SNN: Single-Spike Phase Coding With Base Manipulation for ANN-to-SNN Conversion Loss Minimization
inferred← citeshttps://doi.org/10.1109/tnnls.2021.3071976openalex-snapshot
Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation
inferred← citeshttps://doi.org/10.1109/tnnls.2021.3084955openalex-snapshot
Event-Driven Intrinsic Plasticity for Spiking Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1109/tnnls.2021.3110991openalex-snapshot
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/tnnls.2023.3263008openalex-snapshot
Backpropagation-Based Learning Techniques for Deep Spiking Neural Networks: A Survey
inferred← citeshttps://doi.org/10.1109/tnnls.2025.3622893openalex-snapshot
SNN-FT: Temporal-Coded Spiking Neural Networks for Fourier Transform
inferred← citeshttps://doi.org/10.1109/tpami.2023.3339211openalex-snapshot
A Hybrid Neural Coding Approach for Pattern Recognition With Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/tvlsi.2022.3208191openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/twc.2025.3591117openalex-snapshot
Energy-Efficient Dynamic and Spatiotemporal Spectrum Access via Spiking Reservoir Computing
inferred← citeshttps://doi.org/10.1109/vlsi-soc54400.2022.9939654openalex-snapshot
Energy-Efficient SNN Implementation Using RRAM-Based Computation In-Memory (CIM)
inferred← citeshttps://doi.org/10.1117/1.jei.31.1.010901openalex-snapshot
Review of spike-based neuromorphic computing for brain-inspired vision: biology, algorithms, and hardware
inferred← citeshttps://doi.org/10.1142/s0129065720500276openalex-snapshot
Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron
inferred← citeshttps://doi.org/10.1142/s0129065727500092openalex-snapshot
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inferred← citeshttps://doi.org/10.1145/3304103openalex-snapshot
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inferred← citeshttps://doi.org/10.1145/3445814.3446738openalex-snapshot
NeuroEngine: a hardware-based event-driven simulation system for advanced brain-inspired computing
inferred← citeshttps://doi.org/10.1145/3477145.3477155openalex-snapshot
Neko: a Library for Exploring Neuromorphic Learning Rules
inferred← citeshttps://doi.org/10.1145/3477145.3477156openalex-snapshot
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
inferred← citeshttps://doi.org/10.1145/3501770openalex-snapshot
NxTF: An API and Compiler for Deep Spiking Neural Networks on Intel Loihi
inferred← citeshttps://doi.org/10.1145/3531437.3539708openalex-snapshot
Layerwise Disaggregated Evaluation of Spiking Neural Networks
inferred← citeshttps://doi.org/10.1371/journal.pone.0327513openalex-snapshot
BN-SNN: Spiking neural networks with bistable neurons for object detection
inferred← citeshttps://doi.org/10.14569/ijacsa.2023.0140803openalex-snapshot
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inferred← citeshttps://doi.org/10.1609/aaai.v33i01.33011319openalex-snapshot
TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding
inferred← citeshttps://doi.org/10.1609/aaai.v35i11.17180openalex-snapshot
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inferred← citeshttps://doi.org/10.1609/aaai.v35i8.16870openalex-snapshot
Deep Spiking Neural Network with Neural Oscillation and Spike-Phase Information
inferred← citeshttps://doi.org/10.20868/upm.thesis.81685openalex-snapshot
Evaluación de un sistema de identificación de fluctuaciones (estado ON/OFF) en enfermos de Parkinson
inferred← citeshttps://doi.org/10.21203/rs.3.rs-701752/v1openalex-snapshot
The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware
inferred← citeshttps://doi.org/10.2139/ssrn.5166170openalex-snapshot
The Future of Brain-inspired Computing: A Survey on Large-scale Spiking Neural Networks
inferred← citeshttps://doi.org/10.2139/ssrn.5339085openalex-snapshot
Energy-Efficient and Fault-Tolerant Spiking Neural Networks
inferred← citeshttps://doi.org/10.2197/ipsjtsldm.12.53openalex-snapshot
Neuromorphic Computing Systems: From CMOS To Emerging Nonvolatile Memory
inferred← citeshttps://doi.org/10.23919/date51398.2021.9473964openalex-snapshot
Exploring Spike-Based Learning for Neuromorphic Computing: Prospects and Perspectives
inferred← citeshttps://doi.org/10.23919/date56975.2023.10137023openalex-snapshot
The CNN vs. SNN Event-camera Dichotomy and Perspectives For Event-Graph Neural Networks
inferred← citeshttps://doi.org/10.23919/date56975.2023.10137112openalex-snapshot
Lossless Sparse Temporal Coding for SNN-based Classification of Time-Continuous Signals
inferred← citeshttps://doi.org/10.23919/date64628.2025.10992893openalex-snapshot
Exploring the Sparsity-Quantization Interplay on a Novel Hybrid SNN Event-Driven Architecture
inferred← citeshttps://doi.org/10.3389/fncom.2023.1148284openalex-snapshot
Reservoir based spiking models for univariate Time Series Classification
inferred← citeshttps://doi.org/10.3389/fncom.2023.1250908openalex-snapshot
Enhanced representation learning with temporal coding in sparsely spiking neural networks
inferred← citeshttps://doi.org/10.3389/fninf.2018.00089openalex-snapshot
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python
inferred← citeshttps://doi.org/10.3389/fnins.2018.00987openalex-snapshot
Sparse Computation in Adaptive Spiking Neural Networks
inferred← citeshttps://doi.org/10.3389/fnins.2021.629000openalex-snapshot
Low-Latency Spiking Neural Networks Using Pre-Charged Membrane Potential and Delayed Evaluation
inferred← citeshttps://doi.org/10.3389/fnins.2021.694170openalex-snapshot
A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware
inferred← citeshttps://doi.org/10.3389/fnins.2022.760298openalex-snapshot
Backpropagation With Sparsity Regularization for Spiking Neural Network Learning
inferred← citeshttps://doi.org/10.3389/fnins.2022.873935openalex-snapshot
Benchmarking Neuromorphic Hardware and Its Energy Expenditure
inferred← citeshttps://doi.org/10.3389/fnins.2022.971937openalex-snapshot
Analyzing time-to-first-spike coding schemes: A theoretical approach
inferred← citeshttps://doi.org/10.3389/fnins.2022.991851openalex-snapshot
BSNN: Towards faster and better conversion of artificial neural networks to spiking neural networks with bistable neurons
inferred← citeshttps://doi.org/10.3389/fnins.2022.999029openalex-snapshot
Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task
inferred← citeshttps://doi.org/10.3389/fnins.2023.1121592openalex-snapshot
A TTFS-based energy and utilization efficient neuromorphic CNN accelerator
inferred← citeshttps://doi.org/10.3389/fnins.2024.1346805openalex-snapshot
Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding
inferred← citeshttps://doi.org/10.3390/brainsci13091316openalex-snapshot
From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?
inferred← citeshttps://doi.org/10.3390/e24111526openalex-snapshot
A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation
inferred← citeshttps://doi.org/10.3390/e27040333openalex-snapshot
Encrypted Spiking Neural Networks Based on Adaptive Differential Privacy Mechanism
inferred← citeshttps://doi.org/10.3390/electronics10192441openalex-snapshot
A Low-Cost Hardware-Friendly Spiking Neural Network Based on Binary MRAM Synapses, Accelerated Using In-Memory Computing
inferred← citeshttps://doi.org/10.3390/electronics11182867openalex-snapshot
Topology-Aware Mapping of Spiking Neural Network to Neuromorphic Processor
inferred← citeshttps://doi.org/10.3390/electronics12020310openalex-snapshot
Spiking Neural-Networks-Based Data-Driven Control
inferred← citeshttps://doi.org/10.3390/eng6110304openalex-snapshot
A Practical Tutorial on Spiking Neural Networks: Comprehensive Review, Models, Experiments, Software Tools, and Implementation Guidelines
inferred← citeshttps://doi.org/10.3390/jlpea11020023openalex-snapshot
A Review of Algorithms and Hardware Implementations for Spiking Neural Networks
inferred← citeshttps://doi.org/10.3390/jlpea12040059openalex-snapshot
Towards Low-Power Machine Learning Architectures Inspired by Brain Neuromodulatory Signalling
inferred← citeshttps://doi.org/10.3390/ma12172745openalex-snapshot
Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
inferred← citeshttps://doi.org/10.3390/math11234777openalex-snapshot
A Visually Inspired Computational Model for Recognition of Optic Flow
inferred← citeshttps://doi.org/10.3390/s22197248openalex-snapshot
Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI
inferred← citeshttps://doi.org/10.3390/s23146548openalex-snapshot
EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing
inferred← citeshttps://doi.org/10.3390/s23187701openalex-snapshot
Neuromorphic Sentiment Analysis Using Spiking Neural Networks
inferred← citeshttps://doi.org/10.48550/arxiv.1812.03929openalex-snapshot
An Introduction to Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
inferred← citeshttps://doi.org/10.48550/arxiv.1909.02549openalex-snapshot
Minibatch Processing in Spiking Neural Networks
inferred← citeshttps://doi.org/10.48550/arxiv.2002.00860openalex-snapshot
Optimized spiking neurons classify images with high accuracy through temporal coding with two spikes
inferred← citeshttps://doi.org/10.48550/arxiv.2003.13750openalex-snapshot
Extending BrainScaleS OS for BrainScaleS-2
inferred← citeshttps://doi.org/10.48550/arxiv.2006.02642openalex-snapshot
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks
inferred← citeshttps://doi.org/10.48550/arxiv.2006.09982openalex-snapshot
You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy
inferred← citeshttps://doi.org/10.48550/arxiv.2007.05606openalex-snapshot
Neuromorphic Processing and Sensing: Evolutionary Progression of AI to Spiking
inferred← citeshttps://doi.org/10.48550/arxiv.2008.02189openalex-snapshot
SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks
inferred← citeshttps://doi.org/10.48550/arxiv.2011.12338openalex-snapshot
PeleNet: A Reservoir Computing Framework for Loihi
inferred← citeshttps://doi.org/10.48550/arxiv.2105.00324openalex-snapshot
Neko: a Library for Exploring Neuromorphic Learning Rules
inferred← citeshttps://doi.org/10.5204/thesis.eprints.253913openalex-snapshot
Spiking Neural Networks for Scalable Visual Place Recognition
inferred← citeshttps://doi.org/10.5772/intechopen.113050openalex-snapshot
Spiking Neural Encoding and Hardware Implementations for Neuromorphic Computing
inferred← citeshttps://doi.org/10.70675/1aa2c5b8z3a7bz4b40z91c5z9063afec1f1copenalex-snapshot
Theoretical framework for Time-To-First-Spike coding in Spiking Neural Networks
inferred← citeshttps://doi.org/10.70675/25b97a6dzc185z429czac72z56bfdedbd8b6openalex-snapshot
Neuromorphic algorithms and hardware for event-based processing
inferred← citeshttps://doi.org/10.70675/32a83d65z43ebz4318zab2dzb1e874da36d6openalex-snapshot
Deep learning in event-based neuromorphic systems
inferred← citeshttps://doi.org/10.70675/39a95f62z4a96z486azbdc1z8ebf1379c1b1openalex-snapshot
Study and design of an energy efficient perception module combining event-based image sensors and spiking neural network with 3D integration technologies
inferred← citeshttps://doi.org/10.70675/4aa220eaz87e6z4ba0za31az4b4a4bd2b065openalex-snapshot
Unsupervised STDP-based Feature Learning for Video Analysis with Spiking Neural Networks
inferred← citeshttps://doi.org/10.70675/6dbfbf1ez611az4dcbzbb53z05d8e855e376openalex-snapshot
Modélisation et exploration d'architectures neuromorphiques pour les systèmes embarqués haute-performance
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