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Visual explanations from spiking neural networks using inter-spike intervals

Scientific reports, 2021-09, Vol.11 (1), p.19037-19037, Article 19037

2021

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Revisiting Batch Normalization for Training Low-Latency Deep Spiking Neural Networks From Scratch

Frontiers in neuroscience, 2021-12, Vol.15, p.773954-773954

2021

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Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning

Frontiers in neuroscience, 2018-08, Vol.12, p.435-435

2018

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Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures

Frontiers in neuroscience, 2020-02, Vol.14, p.119-119

2020

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Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization

Frontiers in neuroscience, 2020-06, Vol.14, p.653-653

2020

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Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons

Scientific reports, 2016-07, Vol.6 (1), p.30039-30039, Article 30039

2016

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Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines

Frontiers in neuroscience, 2019-05, Vol.13, p.504-504

2019

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Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model

Frontiers in neuroscience, 2018-03, Vol.12, p.126-126

2018

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Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks

Frontiers in neuroscience, 2017-12, Vol.11, p.693-693

2017

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Discretization Based Solutions for Secure Machine Learning Against Adversarial Attacks

IEEE access, 2019, Vol.7, p.70157-70168

2019

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A Low Effort Approach to Structured CNN Design Using PCA

IEEE access, 2020, Vol.8, p.1347-1360

2020

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Efficient human activity recognition with spatio-temporal spiking neural networks

Frontiers in neuroscience, 2023-09, Vol.17, p.1233037-1233037

2023

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Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing

Neural networks, 2021-12, Vol.144, p.686-698

2021

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SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition

Frontiers in neuroscience, 2018-08, Vol.12, p.524-524

2018

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Sharing leaky-integrate-and-fire neurons for memory-efficient spiking neural networks

Frontiers in neuroscience, 2023-07, Vol.17, p.1230002-1230002

2023

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Synthesizing Images From Spatio-Temporal Representations Using Spike-Based Backpropagation

Frontiers in neuroscience, 2019-06, Vol.13, p.621

2019

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Habituation based synaptic plasticity and organismic learning in a quantum perovskite

Nature communications, 2017-08, Vol.8 (1), p.240-7, Article 240

2017

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Towards spike-based machine intelligence with neuromorphic computing

Nature (London), 2019-11, Vol.575 (7784), p.607-617

2019

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Editorial: Focus on algorithms for neuromorphic computing

Neuromorphic computing and engineering, 2023-09, Vol.3 (3), p.30402

2023

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Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding

Frontiers in neuroscience, 2024-02, Vol.18 (_), p.1346805-1346805

2024

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Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning

Neural networks, 2020-01, Vol.121, p.148-160

2020

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Implicit adversarial data augmentation and robustness with Noise-based Learning

Neural networks, 2021-09, Vol.141, p.120-132

2021

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Organismic materials for beyond von Neumann machines

Applied physics reviews, 2020-03, Vol.7 (1)

2020

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DetectX-Adversarial Input Detection Using Current Signatures in Memristive XBar Arrays

IEEE transactions on circuits and systems. I, Regular papers, 2021-11, Vol.68 (11), p.4482-4494

2021

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SwitchX: Gmin-Gmax Switching for Energy-efficient and Robust Implementation of Binarized Neural Networks on ReRAM Xbars

ACM transactions on design automation of electronic systems, 2023-07, Vol.28 (4), p.1-21, Article 60

2023

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Visual explanations from spiking neural networks using inter-spike intervals

Revisiting Batch Normalization for Training Low-Latency Deep Spiking Neural Networks From Scratch

Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning

Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures

Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization

Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons

Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines

Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model

Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks

Discretization Based Solutions for Secure Machine Learning Against Adversarial Attacks

A Low Effort Approach to Structured CNN Design Using PCA

Efficient human activity recognition with spatio-temporal spiking neural networks

Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing

SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition

Sharing leaky-integrate-and-fire neurons for memory-efficient spiking neural networks

Synthesizing Images From Spatio-Temporal Representations Using Spike-Based Backpropagation

Habituation based synaptic plasticity and organismic learning in a quantum perovskite

Towards spike-based machine intelligence with neuromorphic computing

Editorial: Focus on algorithms for neuromorphic computing

Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding

Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning

Implicit adversarial data augmentation and robustness with Noise-based Learning

Organismic materials for beyond von Neumann machines

DetectX-Adversarial Input Detection Using Current Signatures in Memristive XBar Arrays

SwitchX: Gmin-Gmax Switching for Energy-efficient and Robust Implementation of Binarized Neural Networks on ReRAM Xbars

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