B. Noheda, L. Schomaker,
J. Roerdink

Rijksuniversiteit Groningen
The Netherlands

B. Gotsmann, S. Karg, J. Fompeyrine, S. Abel

IBM Research GmbH
Switzerland

I. Stolichnov

Ecole Polytechnique Federal de Lausanne
Switzerland

I. Lukyanchuk

Université de Picardie Jules Verne
France

C. Magén

Consejo Superior de Investigaciones Científicas
Spain

R. Dittmann, R. Waser

Forschungszentrum Jülich GmbH
Germany

E. Chicca

Universität Bielefeld
Germany

E. H. Salje, M. Carpenter

University of Cambridge
United Kingdom

M. Gregg

Queen’s University of Belfast
United Kingdom

P. Zubko

University College London
United Kingdom

G. Rijnders, G. Koster

Universiteit Twente
The Netherlands

G. Indiveri

University of Zurich
Switzerland

R. Huijink, D. Bijl

SmartTip
The Netherlands

S. Tiedke

aiXACCT Systems
Germany

M. Dekkers, A. Janssens

Solmates
The Netherlands

C. Damen, R. Groenen

Twente Solid State Technologies
The Netherlands

R. Waser

RWTH Aachen
Germany

J.A. Pardo

University of Zaragoza
Spain

P. Kruizinga

Océ Technologies
The Netherlands

M. Porcu

DENS Solutions
The Netherlands

K. Peters

Crystec
Germany

S. Kocevar

HFP Consulting
Germany

D. Vercruysse, S. Birkner

Building Between Bridges
Belgium

Successes in deep learning show that the paradigm of neuromorphic computing is very attractive. However, current technology is based on the Turing/von Neumann architecture, requiring extensive communication and an excessive amount of energy for computing. A training setup requires several hundreds to thousands of Watts, while for the same tasks the human brain needs less than 100 Watt. In this multidisciplinary project, new (memristic) materials and architectures for neuromorphic computing will be investigated in order to develop materials that can learn.

The scientific aim of MANIC is to synthesize materials that can function as networks of neurons and synapses by integrating conductivity, plasticity and self-organization.

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