Results 81 to 90 of about 68,187 (284)

A Spiking Neuron and Population Model Based on the Growth Transform Dynamical System

Frontiers in Neuroscience, 2020
In neuromorphic engineering, neural populations are generally modeled in a bottom-up manner, where individual neuron models are connected through synapses to form large-scale spiking networks.
Ahana Gangopadhyay, Darshit Mehta, Shantanu Chakrabartty   +2 more
doaj   +1 more source

Optoelectronic Synaptic Devices Using Molecular Telluride Phase‐Change Inks for Three‐Factor Learning

Advanced Functional Materials, EarlyView.
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner, Dhananjeya Kumaar, Giacomo Torti, Killian Keller, Till Zellweger, Florian M. Schenk, Christoph Weilenmann, Daniel Chelladurai, Vara Prasad Jonnalagadda, Ghazi Sarwat Syed, Vanessa Wood, Jürg Leuthold, Alexandros Emboras, Maksym Yarema, Mathieu Luisier   +14 more
wiley   +1 more source

Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection

, 2019
Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications. As we try to solve more advanced problems, increasing demands for computing and power resources has become inevitable. Spiking neural
Kim, Seijoon, Na, Byunggook, Park, Seongsik, Yoon, Sungroh   +3 more
core   +1 more source

Photon Avalanching Nanoparticles: The Next Generation of Upconverting Nanomaterials?

Advanced Functional Materials, EarlyView.
This Perspective outlines the mechanistic foundations that enable photon‐avalanche (PA) behavior in lanthanide nanomaterials and contrasts them with emerging application spaces and forward‐looking design strategies. By bridging threshold engineering, energy‐transfer dynamics, and materials engineering, we provide a coherent roadmap for advancing the ...
Kimoon Lee, Yunseo Kim, Hyeong‐ku Jo, Dongjoon Yoon, Byeonghyeok Im, WonJeong Yu, Eui‐Rim On, Changhwan Lee   +7 more
wiley   +1 more source

Comparison of FORCE trained spiking and rate neural networks shows spiking networks learn slowly with noisy, cross-trial firing rates.

PLoS Computational Biology
Training spiking recurrent neural networks (SRNNs) presents significant challenges compared to standard recurrent neural networks (RNNs) that model neural firing rates more directly.
Thomas Robert Newton, Wilten Nicola
doaj   +1 more source

Evolving unipolar memristor spiking neural networks [PDF]

Connection Science, 2015
Neuromorphic computing --- brainlike computing in hardware --- typically requires myriad CMOS spiking neurons interconnected by a dense mesh of nanoscale plastic synapses. Memristors are frequently citepd as strong synapse candidates due to their statefulness and potential for low-power implementations.
Howard, David, Bull, Larry, Costello, Ben De Lacy   +2 more
openaire   +2 more sources

Bio‐Inspired Multimodal Hardware Front‐End Enabled by 2D Floating‐Gate Memory for UAV Perception

Advanced Functional Materials, EarlyView.
A MoS2/h‐BN /graphene floating‐gate memory underpins a bio‐inspired multimodal front end that integrates visual, inertial, and airflow cues. A 4 × 4 FG memory array encodes temporal intensity differences, while IMU‐ and airflow‐driven threshold modulation suppresses self‐motion artifacts, enabling fast, low‐power, robust autonomous UAV tracking and ...
Lianghao Guo, Yue Cheng, Cong Li, Xiaoyu Guo, Jiankai Yin, Dadong Fan, Zhenyu Xu, Chenyu Tang, Arokia Nathan, Shuo Gao, Li Tao, Luigi G. Occhipinti   +11 more
wiley   +1 more source

A neural circuit for navigation inspired by C. elegans Chemotaxis [PDF]

, 2014
We develop an artificial neural circuit for contour tracking and navigation inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to harness the computational advantages spiking neural networks promise over their non-spiking ...
Rajendran, Bipin, Santurkar, Shibani
core  

Evolving spiking neural network—a survey [PDF]

Evolving Systems, 2013
This paper provides a comprehensive literature survey on the evolving Spiking Neural Network (eSNN) architecture since its introduction in 2006 as a further extension of the ECoS paradigm introduced by Kasabov in 1998. We summarize the functioning of the method, discuss several of its extensions and present a number of applications in which the eSNN ...
Schliebs, S, Kasabov, N
openaire   +3 more sources

Multi‐Scale Interface Engineering of MXenes for Multifunctional Sensory Systems

Advanced Functional Materials, EarlyView.
MXenes, as two‐dimensional transition metal carbides and nitrides, demonstrate remarkable capabilities for multifunctional sensing applications. This review systematically examines multi‐scale interface engineering approaches that enhance sensing performance, enable diverse detection functionalities, and improve system‐level compatibility in MXene ...
Jiaying Liao, Sin‐Yi Pang, Jianhua Hao
wiley   +1 more source