Original Research

Published on 23 Jun 2023

SENECA: building a fully digital neuromorphic processor, design trade-offs and challenges

in Neuromorphic Engineering

Guangzhi Tang
Kanishkan Vadivel
Yingfu Xu
Refik Bilgic
Kevin Shidqi
Paul Detterer
Stefano Traferro
Mario Konijnenburg
Manolis Sifalakis
Gert-Jan van Schaik

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1187252

7,917 views
10 citations

Review

Published on 16 Jun 2023

Direct learning-based deep spiking neural networks: a review

in Neuromorphic Engineering

Yufei Guo
Xuhui Huang
Zhe Ma

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1209795

7,993 views
22 citations

Original Research

Published on 15 Jun 2023

Oscillatory neural network learning for pattern recognition: an on-chip learning perspective and implementation

in Neuromorphic Engineering

Madeleine Abernot
Nadine Azemard
Aida Todri-Sanial

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1196796

4,333 views

Original Research

Published on 14 Jun 2023

Spiking neural network with working memory can integrate and rectify spatiotemporal features

in Neuromorphic Engineering

Yi Chen
Hanwen Liu
Kexin Shi
Malu Zhang
Hong Qu

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1167134

2,616 views
3 citations

Original Research

Published on 07 Jun 2023

Dual functional states of working memory realized by memristor-based neural network

in Neuromorphic Engineering

Hongzhe Wang
Xinqiang Pan
Junjie Wang
Mingyuan Sun
Chuangui Wu
Qi Yu
Zhen Liu
Tupei Chen
Yang Liu

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1192993

1,635 views

Original Research

Published on 23 May 2023

VTSNN: a virtual temporal spiking neural network

in Neuromorphic Engineering

Xue-Rui Qiu
Zhao-Rui Wang
Zheng Luan
Rui-Jie Zhu
Xiao Wu
Ma-Lu Zhang
Liang-Jian Deng

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1091097

2,670 views
2 citations

Original Research

Published on 18 May 2023

Approaching the mapping limit with closed-loop mapping strategy for deploying neural networks on neuromorphic hardware

in Neuromorphic Engineering

Song Wang
Qiushuang Yu
Tiantian Xie
Cheng Ma
Jing Pei

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1168864

1,029 views
1 citation

Correction

Published on 16 May 2023

Corrigendum: SCTN: event-based object tracking with energy-efficient deep convolutional spiking neural networks

in Neuromorphic Engineering

Mingcheng Ji
Ziling Wang
Rui Yan
Qingjie Liu
Shu Xu
Huajin Tang

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1204334

1,186 views

Original Research

Published on 12 May 2023

Meta-SpikePropamine: learning to learn with synaptic plasticity in spiking neural networks

in Neuromorphic Engineering

Samuel Schmidgall
Joe Hays

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1183321

3,996 views
5 citations

Original Research

Published on 11 May 2023

Optical flow estimation from event-based cameras and spiking neural networks

in Neuromorphic Engineering

Javier Cuadrado
Ulysse Rançon
Benoit R. Cottereau
Francisco Barranco
Timothée Masquelier

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1160034

6,438 views
22 citations

Original Research

Published on 05 May 2023

A TTFS-based energy and utilization efficient neuromorphic CNN accelerator

in Neuromorphic Engineering

Miao Yu
Tingting Xiang
Srivatsa P.
Kyle Timothy Ng Chu
Burin Amornpaisannon
Yaswanth Tavva
Venkata Pavan Kumar Miriyala
Trevor E. Carlson

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1121592

2,456 views
2 citations

Review

Published on 04 May 2023

A survey and perspective on neuromorphic continual learning systems

in Neuromorphic Engineering

Richa Mishra
Manan Suri

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1149410

3,943 views

Editorial

Published on 04 May 2023

Editorial: Powering the next-generation IoT applications: new tools and emerging technologies for the development of Neuromorphic System of Systems

in Neuromorphic Engineering

Gianvito Urgese
Antonio Rios-Navarro
Alejandro Linares-Barranco
Terrence C. Stewart
Konstantinos Michmizos

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1197918

1,095 views
3 citations

Original Research

Published on 21 Apr 2023

Boost event-driven tactile learning with location spiking neurons

in Neuromorphic Engineering

Peng Kang
Srutarshi Banerjee
Henry Chopp
Aggelos Katsaggelos
Oliver Cossairt

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1127537

2,000 views
4 citations

Original Research

Published on 20 Apr 2023

STDP-based adaptive graph convolutional networks for automatic sleep staging

in Neuromorphic Engineering

Yuan Zhao
Xianghong Lin
Zequn Zhang
Xiangwen Wang
Xianrun He
Liu Yang

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1158246

2,334 views
2 citations

Original Research

Published on 20 Apr 2023

Dynamical memristive neural networks and associative self-learning architectures using biomimetic devices

in Neuromorphic Engineering

Bill Zivasatienraj
W. Alan Doolittle

Frontiers in Neuroscience

doi 10.3389/fnins.2023.1153183

2,256 views
2 citations

Navigation group

Articles

SENECA: building a fully digital neuromorphic processor, design trade-offs and challenges

Direct learning-based deep spiking neural networks: a review

Oscillatory neural network learning for pattern recognition: an on-chip learning perspective and implementation

Spiking neural network with working memory can integrate and rectify spatiotemporal features

Dual functional states of working memory realized by memristor-based neural network

VTSNN: a virtual temporal spiking neural network

Approaching the mapping limit with closed-loop mapping strategy for deploying neural networks on neuromorphic hardware

Corrigendum: SCTN: event-based object tracking with energy-efficient deep convolutional spiking neural networks

Meta-SpikePropamine: learning to learn with synaptic plasticity in spiking neural networks

Optical flow estimation from event-based cameras and spiking neural networks

A TTFS-based energy and utilization efficient neuromorphic CNN accelerator

A survey and perspective on neuromorphic continual learning systems

Editorial: Powering the next-generation IoT applications: new tools and emerging technologies for the development of Neuromorphic System of Systems

Boost event-driven tactile learning with location spiking neurons

STDP-based adaptive graph convolutional networks for automatic sleep staging

Dynamical memristive neural networks and associative self-learning architectures using biomimetic devices