MINIMAL

Miniature Insect Model for Active Learning

 Coordinatore THE UNIVERSITY OF EDINBURGH 

 Organization address address: Roxburgh Street 1-7
city: Edinburgh
postcode: EH8 9TA

contact info
Titolo: Ms.
Nome: Angela
Cognome: Noble
Email: send email
Telefono: +44 131 650 9024
Fax: +44 131 651 4028

 Nazionalità Coordinatore United Kingdom [UK]
 Totale costo 3˙019˙057 €
 EC contributo 2˙297˙522 €
 Programma FP7-ICT
Specific Programme "Cooperation": Information and communication technologies
 Code Call FP7-ICT-2013-C
 Funding Scheme CP
 Anno di inizio 2014
 Periodo (anno-mese-giorno) 2014-01-01   -   2016-12-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    THE UNIVERSITY OF EDINBURGH

 Organization address address: Roxburgh Street 1-7
city: Edinburgh
postcode: EH8 9TA

contact info
Titolo: Ms.
Nome: Angela
Cognome: Noble
Email: send email
Telefono: +44 131 650 9024
Fax: +44 131 651 4028

UK (Edinburgh) coordinator 0.00
2    Brainwave-Discovery Limited

 Organization address address: Main Street
city: Gartmore
postcode: FK8 3RJ

contact info
Titolo: Prof.
Nome: R Wayne
Cognome: Glasse-Davies
Email: send email
Telefono: +44 7811 498 029

UK (Gartmore) participant 0.00
3    FUNDACIO CENTRE DE REGULACIO GENOMICA

 Organization address address: Doctor Aiguader
city: BARCELONA
postcode: 8003

contact info
Titolo: Mr.
Nome: Stefan
Cognome: Pönisch
Email: send email
Telefono: 34933160264
Fax: 34933969983

ES (BARCELONA) participant 0.00
4    LEIBNIZ-INSTITUT FUER NEUROBIOLOGIE

 Organization address address: BRENNECKESTRASSE
city: MAGDEBURG
postcode: 39118

contact info
Nome: Thekla
Cognome: Thiel
Email: send email
Telefono: 49391600000000

DE (MAGDEBURG) participant 0.00

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computational    models    larval    drosophila    neural    learning    anticipatory   

 Obiettivo del progetto (Objective)

Biology provides the inspiration for a vision of small low-power devices that are able to learn rapidly and autonomously about environmental contingencies, enabling prediction and adaptive anticipatory action. Larval Drosophila have fewer than 10,000 neurons, yet express a variety of complex orientation and learning behaviours, including non-trivial anticipatory actions requiring context-dependent evaluation of the value of learned cues. Current computational learning theory cannot fully account for or replicate these capacities. We aim to develop a new foundation for understanding natural learning by developing a complete multilevel model of learning in larvae. Our aims are: (1) to analyse at a fine scale how larval olfactory behaviour is controlled and altered by associative conditioning, linked to agent-based models that ground learning capabilities in ongoing sensorimotor control; (2) to build one-to-one computational neural models that can be validated by exploiting the recent expansion of the Drosophila neurogenetic toolkit to gain unprecedented ability to characterise and manipulate neural circuits during unconstrained behaviour; (3) To derive from these models novel, generalisable algorithms and circuit architectures that can be used to enhance the learning and anticipatory capabilities of machines.

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