SI-CODE

Towards new Brain-Machine Interfaces: state-dependent information coding

Coordinatore	FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA    more  Organization address address: Corso Bettini 31 city: Rovereto postcode: 38068 contact info Titolo: Ms. Nome: Sara Cognome: Maistrelli Email: send email Telefono: 390465000000 Fax: 390465000000
Nazionalità Coordinatore	Italy [IT]
Totale costo	3˙271˙041 €
EC contributo	2˙471˙230 €
Programma	FP7-ICT Specific Programme "Cooperation": Information and communication technologies
Code Call	FP7-ICT-2011-C
Funding Scheme	CP
Anno di inizio	2012
Periodo (anno-mese-giorno)	2012-03-01   -   2015-08-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA  Organization address address: Corso Bettini 31 city: Rovereto postcode: 38068 contact info Titolo: Ms. Nome: Sara Cognome: Maistrelli Email: send email Telefono: 390465000000 Fax: 390465000000	IT (Rovereto)	coordinator	0.00
2	MAX PLANCK GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN E.V.  Organization address address: Hofgartenstrasse 8 city: MUENCHEN postcode: 80539 contact info Titolo: Mr. Nome: Patrice Cognome: Wegener Email: send email Telefono: +49 7071 601 1791 Fax: +49 7071 601 1793	DE (MUENCHEN)	participant	0.00
3	SCUOLA INTERNAZIONALE SUPERIORE DI STUDI AVANZATI DI TRIESTE  Organization address address: VIA BONOMEA 265 city: TRIESTE postcode: 34136 contact info Titolo: Mr. Nome: Luca Cognome: Bardi Email: send email Telefono: +39 040 3787201 Fax: +39 040 3787249	IT (TRIESTE)	participant	0.00
4	UNIVERSITAET ZUERICH  Organization address address: Raemistrasse 71 city: ZURICH postcode: 8006 contact info Titolo: Prof. Nome: Giacomo Cognome: Indiveri Email: send email Telefono: +41 44 635 3051	CH (ZURICH)	participant	0.00

Mappa

 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

 Obiettivo del progetto (Objective)

Brain Machine Interfaces (BMIs) are devices mediating communication between a brain and the external world, and hold the potential for a) restoring motor or sensory functions to people who lost them due to illness or injury, and b) understanding neural information processing through controlled interactions between neurons and external devices. However, the success of BMIs is hampered by the problem that neural responses to external correlates are highly variable because they depend on the internal state of the neural network. We propose to remove this obstacle by developing a radically new generation of 'bidirectional BMIs' (which decode information from the recorded neural activity and provide information to the brain by stimulation) employing neural computational strategies and neuromorphic VLSI devices that i) understand how network states influence neural responses to stimuli; ii) use this know-how to discount variability induced by state changes in real time and thus operate with increased bandwidth and performance. We gather a highly interdisciplinary team composed of both mathematical and experimental neuroscientists and of VLSI engineers. We will study the interplay between ongoing network states and stimulus-evoked responses in various nervous systems of different complexity. We will develop advanced algorithms and models of network dynamics to determine the network state variables best predicting and discounting neural variability, and to construct optimal state-dependent rules to decode neural activity. We will implement these algorithms in a new 'state-dependent bidirectional BMI' prototype using low-power neuromorphic VLSI circuits that extract in real time network state information and use it to produce outputs optimally suited for both decoding of recorded signals and delivering electrical stimulation to a neural tissue in a given state. This BMI will be tested in a benchmark experiment in rats to guide an external device with closed loop control.