Biologically-Inspired Neuromorphic Computing

被引:4
|
作者
Olin-Ammentorp, Wilkie [1 ]
Cady, Nathaniel [1 ]
机构
[1] SUNY Polytech Inst, Coll Nanoscale Sci & Engn, 257 Fuller Rd, Albany, NY 12203 USA
关键词
Neuromorphic; spiking neural networks; neuromorphic engineering; emerging technology; computation; post-Moore's law; CORTICAL-NEURONS; COMPUTATION; NETWORKS; NOISE; MODEL;
D O I
10.1177/0036850419850394
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Advances in integrated circuitry from the 1950s to the present day have enabled a revolution in technology across the world. However, fundamental limits of circuitry make further improvements through historically successful methods increasingly challenging. It is becoming clear that to address new challenges and applications, new methods of computation will be required. One promising field is neuromorphic engineering, a broad field which applies biologically inspired principles to create alternative computational architectures and methods. We address why neuromorphic engineering is one of the most promising fields within emerging computational technology, elaborating on its common principles and models, and discussing its current state and future challenges.
引用
收藏
页码:261 / 276
页数:16
相关论文
共 50 条
  • [1] Biologically-Inspired Massively-Parallel Computing
    Furber, Steve
    2012 18TH IEEE INTERNATIONAL SYMPOSIUM ON ASYNCHRONOUS CIRCUITS AND SYSTEMS (ASYNC), 2012, : XV - XV
  • [2] The Effect of Biologically-Inspired Mechanisms in Spiking Neural Networks for Neuromorphic Implementation
    Schuman, Catherine D.
    2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2017, : 2636 - 2643
  • [3] Biologically-inspired training of spiking recurrent neural networks with neuromorphic hardware
    Bohnstingl, Thomas
    Surina, Anja
    Fabre, Maxime
    Demirag, Yigit
    Frenkel, Charlotte
    Payvand, Melika
    Indiveri, Giacomo
    Pantazi, Angeliki
    2022 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS 2022): INTELLIGENT TECHNOLOGY IN THE POST-PANDEMIC ERA, 2022, : 218 - 221
  • [4] SymbioticSphere: A Biologically-inspired Network Architecture for Autonomic Grid Computing
    Champrasert, Paskorn
    Itao, Tomoko
    Suzuki, Junichi
    2ND INTERNATIONAL CONFERENCE ON BROADBAND NETWORKS (BROADNETS 2005), 2005, : 472 - +
  • [5] Biologically-inspired computing. Part 1 - the general principles
    Winter, Chris
    Database and Network Journal, 1997, 27 (02):
  • [6] Biologically-inspired Soft Exosuit
    Asbeck, Alan T.
    Dyer, Robert J.
    Larusson, Arnar F.
    Walsh, Conor J.
    2013 IEEE 13TH INTERNATIONAL CONFERENCE ON REHABILITATION ROBOTICS (ICORR), 2013,
  • [7] A Biologically-Inspired Nanoantenna Array
    Yusuf, Yazid
    Behdad, Nader
    2012 IEEE ANTENNAS AND PROPAGATION SOCIETY INTERNATIONAL SYMPOSIUM (APSURSI), 2012,
  • [8] A BIOLOGICALLY-INSPIRED IMPROVED MAXNET
    YADIDPECHT, O
    GUR, M
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 1995, 6 (03): : 757 - 759
  • [9] Learning slow features with reservoir computing for biologically-inspired robot localization
    Antonelo, Eric
    Schrauwen, Benjamin
    NEURAL NETWORKS, 2012, 25 : 178 - 190
  • [10] Arithmetic and Biologically-Inspired Computing Using Phase-Change Materials
    Wright, C. David
    Liu, Yanwei
    Kohary, Krisztian I.
    Aziz, Mustafa M.
    Hicken, Robert J.
    ADVANCED MATERIALS, 2011, 23 (30) : 3408 - +