GENMOD

Generative Models of Human Cognition

 Coordinatore UNIVERSITA DEGLI STUDI DI PADOVA 

Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.

 Nazionalità Coordinatore Italy [IT]
 Totale costo 492˙200 €
 EC contributo 492˙200 €
 Programma FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call ERC-2007-StG
 Funding Scheme ERC-SG
 Anno di inizio 2008
 Periodo (anno-mese-giorno) 2008-06-01   -   2013-05-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    UNIVERSITA DEGLI STUDI DI PADOVA

 Organization address address: VIA 8 FEBBRAIO 2
city: PADOVA
postcode: 35122

contact info
Titolo: Prof.
Nome: Marco
Cognome: Zorzi
Email: send email
Telefono: +39 049 8276618
Fax: +39 049 8276600

IT (PADOVA) hostInstitution 0.00
2    UNIVERSITA DEGLI STUDI DI PADOVA

 Organization address address: VIA 8 FEBBRAIO 2
city: PADOVA
postcode: 35122

contact info
Titolo: Prof.
Nome: Patrizia
Cognome: Bisiacchi
Email: send email
Telefono: + 39 049 8276586
Fax: + 39 049 8276600

IT (PADOVA) hostInstitution 0.00

Mappa


 Word cloud

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

neuron    generative    network    implausible    interactions    cognitive    models    cognition    brain    modeling    learning    connectionist    framework    neural    human    bridge    gap   

 Obiettivo del progetto (Objective)

'A fundamental issue in the study of human cognition is what computations are carried out by the brain to implement cognitive processes. The connectionist framework assumes that cognitive processes are implemented in terms of complex, nonlinear interactions among a large number of simple, neuron-like processing units that form a neural network. This approach has been used in cognitive psychology - often with some success – to develop functional models that clearly represent a great advance over previous verbal-diagrammatic models because they can produce highly detailed simulations of human skilled performance and its breakdown following brain damage. However, a crucial step for the computational modeling of cognition is to bridge the gap between function and structure. Much of the modeling work has been carried out using connectionist networks that have no biological plausibility beyond the metaphor of “neuron-like” processing. Most models have one, or more often a combination, of the following undesirable features: i) strictly feed-forward spread of activation (e.g., no feedback and/or lateral connections); ii) implausible learning procedures (e.g., error back-propagation); iii) implausible learning environment (e.g., supervised learning). Researchers have chosen to ignore these problems as it was seen as an essential compromise to achieve efficient learning of complex cognitive tasks. The aim of the present research program is to exploit the latest findings in neural network and machine learning research to develop generative connectionist models of cognition. Generative models are appealing because they represent plausible models of cortical learning that emphasize the mixing of bottom-up and top-down interactions in the brain. Moreover, generative models of cognition would offer a unified theoretical framework that encompasses classic connectionism and the emerging Bayesian approach to cognition, as well as a means to bridge the gap between neurons and behavior.'

Altri progetti dello stesso programma (FP7-IDEAS-ERC)

ICON (2014)

Integrated Real-time Feedback Control and post-processing for image Restoration

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QCDTHERMO (2008)

QCD thermodynamics on the lattice

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FRACTFRICT (2010)

Fracture and Friction: Rapid Dynamics of Material Failure

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