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SPATEMP SIGNED

From spatial relationships to temporal correlations: New vistas on predictive coding

Total Cost €

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EC-Contrib. €

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Partnership

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Project "SPATEMP" data sheet

The following table provides information about the project.

Coordinator
ERNST STRUNGMANN INSTITUTE GGMBH 

Organization address
address: DEUTSCHORDENSTRASSE 46
city: FRANKFURT AM MAIN
postcode: 60528
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Total cost 1˙750˙000 €
 EC max contribution 1˙750˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2019-STG
 Funding Scheme ERC-STG
 Starting year 2020
 Duration (year-month-day) from 2020-02-01   to  2025-01-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    ERNST STRUNGMANN INSTITUTE GGMBH DE (FRANKFURT AM MAIN) coordinator 1˙750˙000.00

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 Project objective

During active wakefulness, cortical activity organizes itself into highly coherent patterns of gamma waves (30-80Hz). These waves are believed to be essential for cortical communication and synaptic plasticity. Their impairment is a hallmark of neurological and psychiatric disorders. Yet, it remains heavily debated what gamma waves encode, and what their precise role in information transmission is. I have recently proposed a new theory about gamma in visual cortex, building on the predictive coding theory. The predictive coding theory holds that the brain makes active top-down predictions about its own sensory inputs. By comparing these, it generates bottom-up prediction errors to drive learning and the updating of priors. The standard view in predictive coding theories is that gamma waves carry prediction errors. However, I recently hypothesized the opposite: 1) Gamma waves signal a match between predictions and sensory inputs (i.e. predictability), and 2) Columns that predict each other's visual input engage in long-range gamma-synchronization. To test this hypothesis, it is critical to develop a new method to quantify predictions and prediction errors in the context of natural vision. I will solve this by using recently developed deep-learning networks for prediction. By making multi-areal recordings from visual cortex in marmosets and humans (MEG), I will test if predictability indeed determines gamma waves and their synchronization pattern across space. Because stimulus priors have to be acquired through learning, I will further determine whether gamma waves depend on experience and perceptual learning. In marmosets, I will develop an optogenetics approach to test whether gamma waves drive perceptual learning, and test the prediction that V1 gamma waves depend on top-down feedback. In sum, I expect to provide evidence for a new, unified theory about the role of gamma waves in information transmission and the integration of sensory evidence with predictions.

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The information about "SPATEMP" are provided by the European Opendata Portal: CORDIS opendata.

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