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On Information Processing with Networks of Nano-Scale Switching Elements

Zoran Konkoli (Institutionen för mikroteknologi och nanovetenskap, Bionanosystem) ; Göran Wendin (Institutionen för mikroteknologi och nanovetenskap, Bionanosystem)
International Journal of Unconventional Computing (1555-0281). Vol. 10 (2014), 5-6, p. 405-428.
[Artikel, refereegranskad vetenskaplig]

Unconventional computing explores multi-scale platforms connecting molecular-scale devices into networks for the development of scalable neuromorphic architectures, often based on new materials and components with new functionalities. We review some work investigating the functionalities of locally connected networks of different types of switching elements as computational substrates. In particular, we discuss reservoir computing with networks of nonlinear nanoscale components. In usual neuromorphic paradigms, the network synaptic weights are adjusted as a result of a training/learning process. In reservoir computing, the non-linear network acts as a dynamical system mixing and spreading the input signals over a large state space, and only a readout layer is trained. We illustrate the most important concepts with a few examples, featuring memristor networks with time-dependent and history dependent resistances.

Nyckelord: Reservoir computing, molecular network, memristor, dynamic system, RECURRENT NEURAL-NETWORKS, LIQUID-STATE MACHINE, MOLECULAR ELECTRONICS, MEMRISTIVE DEVICES, LOGIC GATES, SYSTEMS, MEMORY, COMPUTATION, CONNECTIVITY, JUNCTIONS



Denna post skapades 2014-11-10.
CPL Pubid: 205519

 

Institutioner (Chalmers)

Institutionen för mikroteknologi och nanovetenskap, Bionanosystem (2007-2015)

Ämnesområden

Data- och informationsvetenskap

Chalmers infrastruktur

 

Projekt

Denna publikation är ett resultat av följande projekt:


SYnaptic MOlecular NEtworks for Bio-inspired Information Processing (SYMONE) (EC/FP7/318597)