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Section: Partnerships and Cooperations

European Initiatives

FP7 Projects

BRAINSCALES

  • Title: BrainScaleS: Brain-inspired multiscale computation in neuromorphic hybrid systems

  • Type: COOPERATION (ICT)

  • Defi: Brain-inspired multiscale computation in neuromorphic hybrid systems

  • Instrument: Integrated Project (IP)

  • Duration: January 2011 - December 2014

  • Coordinator: Universitaet Ruprecht- Karls Heidelberg (Germany)

  • See also: http://brainscales.kip.uni-heidelberg.de/

  • Abstract: The BrainScaleS project aims at understanding function and interaction of multiple spatial and temporal scales in brain information processing. The fundamentally new approach of BrainScaleS lies in the in-vivo biological experimentation and computational analysis. Spatial scales range from individual neurons over larger neuron populations to entire functional brain areas. Temporal scales range from milliseconds relevant for event based plasticity mechanisms to hours or days relevant for learning and development. In the project generic theoretical principles will be extracted to enable an artificial synthesis of cortical-like cognitive skills. Both, numerical simulations on petaflop supercomputers and a fundamentally different non-von Neumann hardware architecture will be employed for this purpose. Neurobiological data from the early perceptual visual and somatosensory systems will be combined with data from specifically targeted higher cortical areas. Functional databases as well as novel project-specific experimental tools and protocols will be developed and used. New theoretical concepts and methods will be developed for understanding the computational role of the complex multi-scale dynamics of neural systems in-vivo. Innovative in-vivo experiments will be carried out to guide this analytical understanding. Multiscale architectures will be synthesized into a non-von Neumann computing device realised in custom designed electronic hardware. The proposed Hybrid Multiscale Computing Facility (HMF) combines microscopic neuromorphic physical model circuits with numerically calculated mesoscopic and macroscopic functional units and a virtual environment providing sensory, decision-making and motor interfaces. The project also plans to employ petaflop supercomputing to obtain new insights into the specific properties of the different hardware architectures. A set of demonstration experiments will link multiscale analysis of biological systems with functionally and architecturally equivalent synthetic systems and offer the possibility for quantitative statements on the validity of theories bridging multiple scales. The demonstration experiments will also explore non-von Neumann computing outside the realm of brain-science. BrainScaleS will establish close links with the EU Brain-i-Nets and the Blue Brain project at the EPFL Lausanne. The consortium consists of a core group of 10 partners with 13 individual groups. Together with other projects and groups the BrainScaleS consortium plans to make important contributions to the preparation of a future FET flagship project. This project will address the understanding and exploitation of information processing in the human brain as one of the major intellectual challenges of humanity with vast potential applications.

    This project started on January 1st, 2011 and is funded for four years.

FACETS-ITN
SEARISE

  • Title: SEARISE

  • Defi: Smart Eyes, Attending and Recognizing Instances of Salient Events

  • Duration: March 2008 - February 2011

  • Coordinator: Fraunhofer Institute for Applied Information Technology FIT (Germany)

  • Other partners:

    • Institution: Ulm University (Germany)

    • Laboratory: Department of Neural Information Processing

    • Researcher: Heiko Neumann

  • See also: http://www.searise.eu/web/doku.php

  • Abstract: The SEARISE project developed a trinocular active cognitive vision system, the Smart-Eyes, for detection, tracking and categorization of salient events and behaviours. Inspired by the human visual system, a cyclopean camera performs wide range monitoring of the visual field while active binocular stereo cameras will fixate and track salient objects, mimicking a focus of attention that switches between different interesting locations. The core of this artificial cognitive visual system is a dynamic hierarchical neural architecture – a computational model of visual processing in the brain. Information processing in Smart-Eyes is highly efficient due to a multi-scale design: Controlled by the cortically plausible neural model, the active cameras provide a multi-scale video record of salient events. The processing self-organizes to adapt to scale variations and to assign the majority of computational resources to the informative parts of the scene. The Smart-Eyes system has been tested in real-life scenarios featuring the activity of people in different scales. In a long-range distance scenario, the system analysed crowd behaviour of sport fans in a football arena. In a short range scenario, the system analysed the behaviour of small groups of people and single individuals.

Collaborations in European Programs, except FP7

ERC NerVi

  • Program: ERC IDEAS

  • Project acronym: NerVi

  • Project title: From single neurons to visual perception

  • Duration: January 2009 - December 2013

  • Coordinator: Olivier Faugeras

  • Abstract: The project is to develop a formal model of information representation and processing in the part of the neocortex that is mostly concerned with visual information. This model will open new horizons in a well-principled way in the fields of artificial and biological vision as well as in computational neuroscience. Specifically the goal is to develop a universally accepted formal framework for describing complex, distributed and hierarchical processes capable of processing seamlessly a continuous flow of images. This framework features notably computational units operating at several spatiotemporal scales on stochastic data arising from natural images. Mean- field theory and stochastic calculus are used to harness the fundamental stochastic nature of the data, functional analysis and bifurcation theory to map the complexity of the behaviours of these assemblies of units. In the absence of such foundations, the development of an understanding of visual information processing in man and machines could be greatly hindered. Although the proposal addresses fundamental problems, its goal is to serve as the basis for ground-breaking future computational development for managing visual data and as a theoretical framework for a scientific understanding of biological vision.