EVOSPIKE

Evolving Probabilistic Spiking Neural Networks for Spatio-Temporal Pattern Recognition

 Coordinatore UNIVERSITAET ZUERICH 

 Organization address address: Raemistrasse 71
city: ZURICH
postcode: 8006

contact info
Titolo: Dr.
Nome: Giacomo
Cognome: Indiveri
Email: send email
Telefono: +41 446353024
Fax: +41 44 6353052

 Nazionalità Coordinatore Switzerland [CH]
 Totale costo 121˙352 €
 EC contributo 121˙352 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-2010-IIF
 Funding Scheme MC-IIF
 Anno di inizio 2011
 Periodo (anno-mese-giorno) 2011-06-01   -   2012-05-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    UNIVERSITAET ZUERICH

 Organization address address: Raemistrasse 71
city: ZURICH
postcode: 8006

contact info
Titolo: Dr.
Nome: Giacomo
Cognome: Indiveri
Email: send email
Telefono: +41 446353024
Fax: +41 44 6353052

CH (ZURICH) coordinator 121˙352.50

Mappa


 Word cloud

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self    data    framework    pattern    neuro    probabilistic    epcngm    theoretical    networks    technologies    host    generation    neural    kasabov    recognition    snn    evolving    spiking    snns    progress    brain    prof    ini    computational    models    researcher    organisation    hardware    vlsi   

 Obiettivo del progetto (Objective)

'Spiking neural networks (SNN), considered the third generation of neural networks, are a promising paradigm for the creation of new intelligent ICT and for the study of the brain. This new generation computational models and systems are potentially capable of modelling complex information processes due to their ability to represent and integrate different information dimensions, such as time, space, frequency, phase, and to deal with large volumes of data in an adaptive, self-organising, self-learning way. The progress in this direction has been slow in the past, but now there are more opportunities for a progress to be made and this is the aim of the proposed project. The host organisation, the Institute of Neuro-Informatics (INI), Zurich, has been developing VLSI technologies for implementing SNNs for many years. As it has mainly focused on the hardware development aspects, it is still lacking a theoretical framework for configuring and applying VLSI SNNs to wider computational problems. The contribution of this project and of the incoming researcher Prof. Kasabov will be crucial for making a breakthrough in this domain. The project proposes to devise a theoretical framework and a methodology for the design of novel SNN, namely evolving probabilistic spiking neural networks (epSNN) and evolving probabilistic computational neuro-genetic models (epCNGM) along with their implementation on existing software and hardware platforms at the host organisation INI. The resulting technologies will offer a new way to efficiently solve a wide range of complex spatio-temporal pattern recognition problems, including: audio-visual pattern recognition; EEG brain data analysis; associative memories; neurogenetic cognitive systems. Further applications of the epCNGM are expected to be developed for modelling brain data related to neurodegenerative diseases, such as Alzheimer’s disease. Knowledge will be transferred from the visiting researcher Prof. Kasabov to INI and Europe.'

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