CIFINE
Controlling information flow in multi-layered neuronal networks
| Coordinatore | ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Organization address
address: BATIMENT CE 3316 STATION 1 contact info |
| Nazionalità Coordinatore | Switzerland [CH] |
| Totale costo | 100˙000 € |
| EC contributo | 100˙000 € |
| 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-2010-RG |
| Funding Scheme | MC-IRG |
| Anno di inizio | 2010 |
| Periodo (anno-mese-giorno) | 2010-09-01 - 2014-08-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Organization address
address: BATIMENT CE 3316 STATION 1 contact info |
CH (LAUSANNE) | coordinator | 100˙000.00 |
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Obiettivo del progetto (Objective)
'The neural representation of the physical world is an often deliberated concept whose concrete rules still elude our understanding. Behavioral responses must be generated from a myriad of different stimulus combinations, but it is still not clear which factors contribute to the brain’s ability to target small cell populations for distinctive stimuli, and reliably route a signal to its appropriate targets. Previous work has made considerable progress on the sub ject of stable propagation of temporally and rate encoded signals in neuronal networks. However, simple signal propagation does not suffice to explain cortical processing because at any given instant most stimuli are ignored, not propagated, and signal paths have to change dynamically with the task at hand. I have recently shown that rate encoded signals can be turned on or off by a mechanism called detailed balance, making it a very good candidate to control functional connectivity in neuronal networks. I want to implement this mechanism in large networks to unite or separate multiple simultaneous signal streams to filter and propagate task-specific information. The often talked-about cocktail party problem, in which a listener must gather a single multi-faceted signal from a rich and noisy background, is an intuitive example of such a task and should be generally solvable by the networks we will implement. The results will, for the first time, give rise to a canonical, generally applicable yet stimulus specific processing circuit in cortical networks. This will be of tremendous help for the experimentally complex task of observing real signal processing and provide for a platform for the discussion of dynamic cortical processing.'