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EvoSpike

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Evolving Probabilistic Spiking Neural Networks for Spatio Temporal Pattern Recog...

Evolving Probabilistic Spiking Neural Networks for Spatio Temporal Pattern Recognition 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 syste... ver más

31/05/2012

UZH

121K€

Presupuesto del proyecto: 121K€

Líder del proyecto

University of Zurich No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2012-05-31 No tenemos la información de la convocatoria

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1 Participantes

UZH

121.35K€ | Lider

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Líder del proyecto

University of Zurich No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 121K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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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