neuronsXnets — Network Analysis in Neocortex during Passive and Active Learning

Results in brief

Network Analysis in Neocortex during Passive and Active Learning

How does the brain compute to enable learning and interaction with the environment? Rapid advances in optical imaging, statistical/machine-learning methods, and computational resources create a timely opportunity to address this question. NeuronsXnets builds an international, multidisciplinary, intersectoral network spanning neuroscience, neuromorphic computing, data science, systems, optical imaging, and hospitals. It leverages this environment to deepen understanding of neural circuit function and translate findings into deep learning and neuromorphic circuits, enabling computing technologies inspired by nervous-system organizing principles and optimized for cognition. NeuronsXnets transfers knowledge through hands-on training across secondments, courses, workshops, and seminars, preparing a new generation of systems neuroscientists and computer scientists/engineers. Partners benefit via cross-fertilization, integrating results into existing and new solutions and strengthening long-term links among business, research, higher education, and hospitals, while mentoring students, raising public awareness, and pursuing research excellence in bio-inspired technologies.

Data: CORDIS, © European Union

Project objective

How does the brain perform the complicated computations that allow us to learn about and interact with the environment? The rapid advances in new optical imaging, the powerful statistical analysis/machine-learning techniques, and the availability of computational resources, provide a unique opportunity to decipher this fundamental question. NeuronsXnets forms an international, multidisciplinary and intersectoral collaboration network involving leading groups in neuroscience, neuromorphic computing, data science, systems, optical tools/imaging, and hospitals. It takes advantage of this unique environment to improve the understanding of neural circuit function and integrate its findings in deep learning architectures and in neuromorphic circuits, aiming to develop a new generation of computing technologies based on the organizing principles of the biological nervous system, optimized for higher levels of cognition. NeuronsXnets will perform knowledge transfer through hands-on training and research activities during 196 secondments, courses, 4 international workshops, and seminars, to train a new generation of highly skilled systems neuroscientists and computer scientists/engineers. All partners, especially the SMEs, will benefit from the cross-fertilization, by integrating the findings and systems they have developed in their existing solutions or in new systems, capitalizing on the research results that will be achieved, and creating a long-term link between business, research, higher education & hospitals. Through open access to the developed tools and collected data, NeuronsXnets can make significant impact on the scientific community. We are committed to educate and mentor young students and raise the public knowledge about the fascinating field of neuroscience. We will strive for research excellence, to develop innovative systems, and pursue entrepreneurial objectives, leading to a new era of collaborative research in neuroscience & bio-inspired technologies.

Original text from CORDIS.

Participants

IDRYMA TECHNOLOGIAS KAI EREVNAS · IRAKLEIOCoordinatorGreece
BRIGHAM INC · BostonUnited States
BRUKER FRANCE SAS · WISSEMBOURGFrance
IBM RESEARCH GMBH · RUESCHLIKONSwitzerland
NEUROCOM LUXEMBOURG SA · LuxembourgLuxembourg
NEUROLINGO TSALIDIS CHR. & SIA E.E. · MAROUSIGreece
PANEPISTIMIAKO GENIKO NOSOKOMEIO IRAKLEIOU · IRAKLEIOGreece
PROMETRONICS UG (HAFTUNGSBESCHRANKT) · BerlinGermany
THE REGENTS OF THE UNIVERSITY OF CALIFORNIA · OaklandUnited States
UNIVERSITAT ZURICH · ZurichSwitzerland
UNIVERSITE DE LIEGE · LIEGEBelgium
UNIVERSITEIT ANTWERPEN · AntwerpenBelgium

neuronsXnets · Network Analysis in Neocortex during Passive and Active Learning

Results in brief

Network Analysis in Neocortex during Passive and Active Learning

Project objective

Participants

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