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TU Dresden » Faculty of Mechanical Science and Engineering » Institute for Materials Science » Chair of Materials Science and Nanotechnology
» funding   » SYnaptic MOlecular NEtworks for Bio-inspired Information Processing (SYMONE)


SYnaptic MOlecular NEtworks for Bio-inspired Information Processing (SYMONE)

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title: SYnaptic MOlecular NEtworks for Bio-inspired Information Processing (SYMONE)
agency:European Union
time frame:2012-2015
web page:http://www.chalmers.se/mc2/symone-en

description:

SYMONE addresses the topic of "Embodied computation and unconventional substrates". The key idea of SYMONE is to use self-assembled networks (SAN) of nanoscale non-linear components for unconventional information processing. The vision has been around for a long time, but recent experimental developments promise to make it possible to meet the associated challenges with realistic chances for progress towards first proofs-of-concepts. Generically, our strategy is strongly hierarchical and interdisciplinary, ranging from bio-inspired approaches at the lowest level, up to neuromorphic networks for information processing at the highest levels. The unconventional aspects of information processing involve flow of information via nearestneighbour interactions through locally connected networks of non-linear elements: switches, memristors and artificial synapses. The experimental aspects encompass a range of fundamental challenges: designing and synthesizing elementary functional non-linear nanoscale molecular and solid-state elements; self-assembling them into functional multi-terminal networks; characterizing the static and dynamic properties and functionalities; developing schemes for operation and readout; achieving useful information processing with such networks and arrays. The computational studies will also face strong challenges comprising the development of compact models for the network elements as well as for the networks themselves, schemes for elementary information processing with such networks, and schemes for achieving high level (complex) computations with them, and ultimately classification of information processing abilities of these networks with regard to what they can and cannot do.

Participants


Goran Wendin
Chalmers University of Technology (Project leader)

Dominique Vuillaume
Institut d'Electronique Microelectronique et Nanotechnologie, France

Jean Roncali
Molecular Technologies, France

Michel Calame
University of Basel, Switzerland

Shlomo Yitzchaik
The Hebrew University of Jerusalem (HUJI), Israel

Christian Gamrat
Commissariat à l'Energie Atomique (CEA), France

Gianaurelio Cuniberti
Technical University of Dresden (TUD), Germany

Valeriu Beiu
United Arab Emirates University (UAEU), UAE



last modified: 2021.05.05 Wed
author: webadmin