SI-CODE

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

 Coordinatore 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

 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

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decode    algorithms    bmis    network    bidirectional    responses    dependent    bmi    real    external    time    vlsi    neuromorphic    recorded    variability    brain    stimulation    neural   

 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.

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