CEMNET

A unified framework for Perceptual Inference in Sensory cortices

Coordinatore	UNIVERSIDAD POMPEU FABRA    more  Organization address address: PLACA DE LA MERCE 10-12 city: BARCELONA postcode: 8002 contact info Titolo: Ms. Nome: Eva Cognome: Martin Email: send email Telefono: 34935422140
Nazionalità Coordinatore	Spain [ES]
Totale costo	166˙336 €
EC contributo	166˙336 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2013-IEF
Funding Scheme	MC-IEF
Anno di inizio	2014
Periodo (anno-mese-giorno)	2014-03-01   -   2016-02-29

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	UNIVERSIDAD POMPEU FABRA  Organization address address: PLACA DE LA MERCE 10-12 city: BARCELONA postcode: 8002 contact info Titolo: Ms. Nome: Eva Cognome: Martin Email: send email Telefono: 34935422140	ES (BARCELONA)	coordinator	166˙336.20

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 Obiettivo del progetto (Objective)

'The neural mechanisms that underlie perceptual inference in cortex are a matter of intense research. Neural models implementing Bayesian models have proved useful to uncover neural computations in a variety of paradigms. Still no existing neurocomputational model provides a general framework for understanding perception in naturalistic conditions. Indeed these models suffer from three shortcomings: i) they cannot perform accurate inference in naturalistic environments where numerous objects interact in complex fashion; ii) they cannot simultaneously represent likelihood estimates over the presence of several objects in the same graphical model; iii) they cannot deal with correlated sensory evidence, though such correlations are ubiquitous in sensory systems. We propose a new unifying model of perception called Constrained Entropy Maximization Network (CEMNet) that provides a theoretical framework for inference in complex naturalistic environments. CEMNet stores an internal model of the environment by representing regularities across stochastic variables as constraints; those constraints shape the response of the network. The aim of the project is to develop the computational model of CEMNet and test it empirically. We will first study how CEMNet can be implemented in a realistic neural network. Second, we will simulate the network and show that, unlike existing neural models, CEMNet can deal with the difficulties of inference in complex environments. Third, a behavioral experiment will monitor how human subjects integrate correlated sensory evidence. Last, we will probe the ability of human subjects to simultaneously maintain likelihood estimates over multiple interacting variables in an audiovisual motion integration paradigm. We will record neural signals in MagnetoEncephaloGraphy while subjects perform the task and look for a specific neural signature of CEMNet signals.'