From Solid to Fluid: Novel Approaches in Neuromorphic Engineering

神经形态工程学 记忆电阻器 计算机科学 维数之咒 电阻式触摸屏 人工神经网络 纳米技术 人工智能 材料科学 电子工程 计算机体系结构 工程类 计算机视觉
作者
Daniil Nikitin,Hynek Biederman,А. Х. Шукуров
出处
期刊:Recent Patents on Nanotechnology 卷期号:19
标识
DOI:10.2174/0118722105305259240919074119
摘要

Neuromorphic engineering is rapidly developing as an approach to mimicking processes in brains using artificial memristors, devices that change conductivity in response to the electrical field (resistive switching effect). Memristor-based neuromorphic systems can overcome the existing problems of slow and energy-inefficient computing that conventional processors face. In the Introduction, the basic principles of memristor operation and its applications are given. The history of switching in sandwich structures and granular metals is reviewed in the Historical Overview. Particular attention is paid to the fundamental articles from the pre-memristor era (the 1960s-70s), which demonstrated the first evidence of resistive switching and predicted the filamentary mechanism of switching. Multi-dimensionality in neuromorphic systems: Despite the powerful computational abilities of traditional memristor arrays, they cannot repeat many organizational characteristics of biological neural networks, i.e., their multi-dimensionality. This part reviews the unconventional nanowire- and nanoparticle-based neuromorphic systems that demonstrate incredible potential for use in reservoir computing due to the unique spiking change in conductance similar to firing in neurons. Liquid-based neuromorphic devices: The transition of neuromorphic systems from solid to liquid state broadens the possibilities for mimicking biological processes. In this section, ionic current memristors are reviewed and, the working principles of which bring us closer to the mechanisms of information transmittance in real synapses. Nanofluids: A novel direction in neuromorphic engineering linked to the application of nanofluids for the formation of reconfigurable nanoparticle networks with memristive properties is given in this section. The Conclusion t summarizes the bullet points of the Review and provides an outlook on the future of liquid-state neuromorphic systems.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
jiusi发布了新的文献求助10
刚刚
刚刚
科研通AI2S应助RAP采纳,获得10
1秒前
11发布了新的文献求助10
1秒前
Yuan应助123采纳,获得10
2秒前
十七完成签到,获得积分10
2秒前
景飞丹发布了新的文献求助10
2秒前
3秒前
Su完成签到,获得积分10
4秒前
科研一霸完成签到,获得积分10
5秒前
科研通AI2S应助咖可乐采纳,获得10
6秒前
元谷雪应助咖可乐采纳,获得10
6秒前
害羞聋五完成签到,获得积分10
6秒前
6秒前
牟翎完成签到,获得积分10
7秒前
7秒前
8秒前
青青子衿发布了新的文献求助10
9秒前
可爱的函函应助jiusi采纳,获得10
10秒前
11秒前
史超完成签到,获得积分10
11秒前
ZML完成签到,获得积分20
12秒前
NicheFactor完成签到,获得积分10
13秒前
微笑驳完成签到 ,获得积分10
14秒前
曾广志完成签到,获得积分10
14秒前
……完成签到,获得积分10
14秒前
wll发布了新的文献求助10
15秒前
脑洞疼应助gzf采纳,获得10
16秒前
Phosphene完成签到,获得积分0
16秒前
隐形曼青应助hailiangzheng采纳,获得10
17秒前
hanxuepenyun关注了科研通微信公众号
18秒前
21秒前
24秒前
小可爱发布了新的文献求助10
24秒前
王逗逗发布了新的文献求助10
24秒前
FGG完成签到,获得积分10
25秒前
25秒前
26秒前
Jc完成签到 ,获得积分10
27秒前
完美世界应助Yxx采纳,获得10
27秒前
高分求助中
Kinetics of the Esterification Between 2-[(4-hydroxybutoxy)carbonyl] Benzoic Acid with 1,4-Butanediol: Tetrabutyl Orthotitanate as Catalyst 1000
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
Rechtsphilosophie 1000
Bayesian Models of Cognition:Reverse Engineering the Mind 888
Very-high-order BVD Schemes Using β-variable THINC Method 568
Chen Hansheng: China’s Last Romantic Revolutionary 500
XAFS for Everyone 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3138230
求助须知:如何正确求助?哪些是违规求助? 2789160
关于积分的说明 7790351
捐赠科研通 2445545
什么是DOI,文献DOI怎么找? 1300521
科研通“疑难数据库(出版商)”最低求助积分说明 625925
版权声明 601046