Results 101 to 110 of about 60,411 (286)

Evolutionary spiking neural networks: a survey

Journal of Membrane Computing
Spiking neural networks (SNNs) are gaining increasing attention as potential computationally efficient alternatives to traditional artificial neural networks(ANNs). However, the unique information propagation mechanisms and the complexity of SNN neuron models pose challenges for adopting traditional methods developed for ANNs to SNNs.
Shuaijie Shen, Rui Zhang 0042, Chao Wang, Renzhuo Huang, Aiersi Tuerhong, Qinghai Guo, Zhichao Lu, Jianguo Zhang, Luziwei Leng   +8 more
openaire   +2 more sources

Ferroelectric Quantum Dots for Retinomorphic In‐Sensor Computing

Advanced Materials, EarlyView.
This work has provided a protocol for fabricating retinomorphic phototransistors by integrating ferroelectric ligands with quantum dots. The resulting device combines ferroelectricity, optical responsiveness, and low‐power operation to enable adaptive signal amplification and high recognition accuracy under low‐light conditions, while supporting ...
Tingyu Long, Huanyu Zhou, Jaewan Ko, Hongwei Tan, Jaemin Lim, Yanfei Zhao, Daehan Kang, Eojin Yoon, Gyeong‐Tak Go, Somin Kim, Seung‐Woo Lee, Chan‐Yul Park, Hyojun Choi, Hyeran Kim, Hyung Joong Yun, Sung Hyuk Park, Kwan Sik Park, Jeong Woo Park, Mungeun Kim, Yong Soo Cho, Ho Won Jang, Wenqiang Yang, Min Hyuk Park, Wan Ki Bae, Sebastiaan van Dijken, Joona Bang, Tae‐Woo Lee   +26 more
wiley   +1 more source

Spike-based computation using classical recurrent neural networks

Neuromorphic Computing and Engineering
Spiking neural networks (SNNs) are a type of artificial neural networks in which communication between neurons is only made of events, also called spikes.
Florent De Geeter, Damien Ernst, Guillaume Drion   +2 more
doaj   +1 more source

Simulation of networks of spiking neurons: A review of tools and strategies

, 2007
We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented.
Beeman, D., Boustani, S. El, Bower, J. M., Brette, R., Carnevale, T., Davison, A. P., Destexhe, A., Diesmann, M., Djurfeldt, M., Ermentrout, B., Goodman, P. H., Harris Jr., F. C., Hines, M., Lansner, A., Morrison, A., Muller, E., Natschlager, T., Pecevski, D., Rochel, O., Rudolph, M., Vieville, T., Zirpe, M.   +21 more
core   +3 more sources

Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application

Advanced Materials, EarlyView.
This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong, You Zhou, Qingxiu Li, Wenyu Zhao, Sunyingyue Geng, Hangfei Li, Yan‐Bing Leng, Shirui Zhu, Guanglong Ding, Yongbiao Zhai, Ziyu Lv, Ye Zhou, Su‐Ting Han   +12 more
wiley   +1 more source

Efficient Deep Spiking Neural Network for Complex EEG Signals

IEEE Access
Spiking neural networks (SNNs) offer a biologically inspired, energy-efficient alternative to conventional artificial neural networks (ANNs). However, deep SNNs struggle to process complex EEG signals because their spike-based representations are sparse ...
Elham Amirizadeh, Reza Boostani
doaj   +1 more source

Label‐Free SERS Fingerprinting of Neuroprotein Conformational Dynamics in Human Saliva

Advanced Materials, EarlyView.
Galvanic molecular entrapment (GME) is a label‐free method for detecting and quantifying neuroprotein conformational states. This technique enables direct surface binding and in situ hotspot generation around molecules, effectively overcoming challenges related to target localization and mismatched hotspot geometries.
Muhammad Shalahuddin Al Ja'farawy, Jun‐Yeong Yang, Chaewon Mun, Seunghun Lee, Chul Bum Cho, Yoo Hyun Um, Min‐Young Lee, Sung‐Gyu Park, Dong‐Ho Kim, Seung Ho Yang, Ho Sang Jung   +10 more
wiley   +1 more source

Light‐Induced Entropy for Secure Vision

Advanced Materials, EarlyView.
This work realized a ternary true random number generator by exploiting stochastic traps emerging within multiple junction interfaces, and quantitatively validated the generation of high‐quality random numbers. Furthermore, it successfully demonstrated diverse applications, including AI‐resilient image security, thereby providing a valuable guide for ...
Juhyung Seo, Seungme Kang, Chaehyun Kim, Taehyun Park, Youngwoo Yoo, Yeong Kwon Kim, Wonjun Shin, Byung Chul Jang, Young‐Joon Kim, Hocheon Yoo   +9 more
wiley   +1 more source

Research on SNN Learning Algorithms and Networks Based on Biological Plausibility

IEEE Access
Spiking Neural Networks, inspired by the brain’s neuronal information processing mech- anisms, utilize sparse, event-based spike signals to emulate biological computation.
Bingqiang Huo, Fang Li, Siyu Peng, Hongwei Chen, Sudan Xin, Hongjun Wang   +5 more
doaj   +1 more source

Logic Negation with Spiking Neural P Systems

, 2019
Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable.
Borrego-Díaz, Joaquín, Gutiérrez-Naranjo, Miguel A., Rodríguez-Chavarría, Daniel   +2 more
core   +1 more source