Original Research

Published on 04 Sep 2024

SpQuant-SNN: ultra-low precision membrane potential with sparse activations unlock the potential of on-device spiking neural networks applications

in Neuromorphic Engineering

Ahmed Hasssan
Jian Meng
Anupreetham Anupreetham
Jae-sun Seo

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1440000

873 views

Original Research

Published on 08 Aug 2024

SiamEFT: adaptive-time feature extraction hybrid network for RGBE multi-domain object tracking

in Neuromorphic Engineering

Shuqi Liu
Gang Wang
Yong Song
Jinxiang Huang
Yiqian Huang
Ya Zhou
Shiqiang Wang

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1453419

719 views

Original Research

Published on 06 Aug 2024

Real-time execution of SNN models with synaptic plasticity for handwritten digit recognition on SIMD hardware

in Neuromorphic Engineering

Bernardo Vallejo-Mancero
Jordi Madrenas
Mireya Zapata

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1425861

1,272 views

Original Research

Published on 05 Aug 2024

BayesianSpikeFusion: accelerating spiking neural network inference via Bayesian fusion of early prediction

in Neuromorphic Engineering

Takehiro Habara
Takashi Sato
Hiromitsu Awano

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1420119

892 views

Review

Published on 31 Jul 2024

Direct training high-performance deep spiking neural networks: a review of theories and methods

in Neuromorphic Engineering

Chenlin Zhou
Han Zhang
Liutao Yu
Yumin Ye
Zhaokun Zhou
Liwei Huang
Zhengyu Ma
Xiaopeng Fan
Huihui Zhou
Yonghong Tian

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1383844

3,715 views
3 citations

Original Research

Published on 24 Jul 2024

Paired competing neurons improving STDP supervised local learning in Spiking Neural Networks

in Neuromorphic Engineering

Gaspard Goupy
Pierre Tirilly
Ioan Marius Bilasco

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1401690

1,058 views

Original Research

Published on 23 Jul 2024

Auto-Spikformer: Spikformer architecture search

in Neuromorphic Engineering

Kaiwei Che
Zhaokun Zhou
Jun Niu
Zhengyu Ma
Wei Fang
Yanqi Chen
Shuaijie Shen
Li Yuan
Yonghong Tian

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1372257

969 views
3 citations

Original Research

Published on 10 Jul 2024

Gradient-free training of recurrent neural networks using random perturbations

in Neuromorphic Engineering

Jesús García Fernández
Sander Keemink
Marcel van Gerven

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1439155

1,733 views
1 citation

Original Research

Published on 26 Jun 2024

A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support

in Neuromorphic Engineering

Takuya Nanami
Daichi Yamada
Makoto Someya
Toshihide Hige
Hokto Kazama
Takashi Kohno

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1384336

1,195 views

Original Research

Published on 20 Jun 2024

Composing recurrent spiking neural networks using locally-recurrent motifs and risk-mitigating architectural optimization

in Neuromorphic Engineering

Wenrui Zhang
Hejia Geng
Peng Li

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1412559

991 views
2 citations

Original Research

Published on 20 Jun 2024

NoC simulation steered by NEST: McAERsim and a Noxim patch

in Neuromorphic Engineering

Markus Robens
Robert Kleijnen
Michael Schiek
Stefan van Waasen

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1371103

1,167 views

Mini Review

Published on 11 Jun 2024

Advancements in brain-machine interfaces for application in the metaverse

in Neuromorphic Engineering

Yang Liu
Ruibin Liu
Jinnian Ge
Yue Wang

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1383319

1,822 views

Original Research

Published on 15 May 2024

Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN

in Neuromorphic Engineering

Salah Daddinounou
Elena-Ioana Vatajelu

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1387339

1,080 views

Original Research

Published on 25 Apr 2024

Artificial cerebellum on FPGA: realistic real-time cerebellar spiking neural network model capable of real-world adaptive motor control

in Neuromorphic Engineering

Yusuke Shinji
Hirotsugu Okuno
Yutaka Hirata

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1220908

2,020 views
2 citations

Editorial

Published on 22 Apr 2024

Editorial: Theoretical advances and practical applications of spiking neural networks

in Neuromorphic Engineering

Gaetano Di Caterina
Malu Zhang
Jundong Liu

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1406502

1,392 views
1 citation

Perspective

Published on 17 Apr 2024

Perspective: an optoelectronic future for heterogeneous, dendritic computing

in Neuromorphic Engineering

Luis El Srouji
Mahmoud Abdelghany
Hari Rakul Ambethkar
Yun-Jhu Lee
Mehmet Berkay On
S. J. Ben Yoo

Frontiers in Neuroscience

doi 10.3389/fnins.2024.1394271

1,040 views

Navigation group

Articles

SpQuant-SNN: ultra-low precision membrane potential with sparse activations unlock the potential of on-device spiking neural networks applications

SiamEFT: adaptive-time feature extraction hybrid network for RGBE multi-domain object tracking

Real-time execution of SNN models with synaptic plasticity for handwritten digit recognition on SIMD hardware

BayesianSpikeFusion: accelerating spiking neural network inference via Bayesian fusion of early prediction

Direct training high-performance deep spiking neural networks: a review of theories and methods

Paired competing neurons improving STDP supervised local learning in Spiking Neural Networks

Auto-Spikformer: Spikformer architecture search

Gradient-free training of recurrent neural networks using random perturbations

A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support

Composing recurrent spiking neural networks using locally-recurrent motifs and risk-mitigating architectural optimization

NoC simulation steered by NEST: McAERsim and a Noxim patch

Advancements in brain-machine interfaces for application in the metaverse

Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN

Artificial cerebellum on FPGA: realistic real-time cerebellar spiking neural network model capable of real-world adaptive motor control

Editorial: Theoretical advances and practical applications of spiking neural networks

Perspective: an optoelectronic future for heterogeneous, dendritic computing