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DEEPEN SIGNED

Deciphering deep architectures underlying structured perception in auditory networks

Total Cost €

0

EC-Contrib. €

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Partnership

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 DEEPEN project word cloud

Explore the words cloud of the DEEPEN project. It provides you a very rough idea of what is the project "DEEPEN" about.

experimental    assays    data    models    function    fundamental    technologies    structured    tactile    recording    neural    perception    model    suggested    local    perturbation    extract    predictive    follows    categories    awake    throughput    neurons    serving    combining    chemogenetically    nonlinearities    animals    output    genetically    nonlinear    techniques    starting    precise    feedback    connections    framework    contribution    shapes    missing    recursively    generation    fail    difficulty    objects    opto    encoded    fuel    mathematical    effortlessly    structures    principles    functional    emerges    platform    computational    leaning    recoding    perceptual    brain    optical    auditory    deep    connected    free    link    approximated    emergence    machine    artificial    operations    tagged    poorly    stages    puzzle    learning    series    structural    behavioural    mouse    emerge    constrained    biologically    networks    theoretical    sensory    derive    interareal    perturbed    recurrently    population    electrophysiological    density   

Project "DEEPEN" data sheet

The following table provides information about the project.

Coordinator
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS 

Organization address
address: RUE MICHEL ANGE 3
city: PARIS
postcode: 75794
website: www.cnrs.fr

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 France [FR]
 Total cost 1˙983˙886 €
 EC max contribution 1˙983˙886 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-COG
 Funding Scheme ERC-COG
 Starting year 2018
 Duration (year-month-day) from 2018-09-01   to  2023-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS FR (PARIS) coordinator 1˙983˙886.00

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 Project objective

The principles of sensory perception are still a large experimental and theoretical puzzle. A strong difficulty is that perception emerges from networks of recurrently connected brain areas whose activity and function are poorly approximated by current generic mathematical models. These models also fail to explain many of the fundamental structures effortlessly identified by the brain (shapes, objects, auditory or tactile categories). I here propose to establish a new approach combining high-throughput population recoding methods with a tailored theoretical framework to derive computational principles operating throughout sensory systems and leading to biologically structured perception. This approach follows on the recent mathematical proposal, suggested by Deep Machine Learning methods, that complex perceptual objects emerge through series of simple nonlinear operations combining increasingly complex sensory features along the sensory pathways. Starting with the mouse auditory system as a model pathway, we will recursively extract, with model-free methods, the main nonlinear sensory features encoded in genetically tagged output and local neurons at different processing stages, using optical and electrophysiological high density recording techniques in awake animals. The role of these features in perception will be identified with behavioural assays. Specific intra- and interareal feedback connections, typically not included in Deep Leaning models, will be opto- and chemogenetically perturbed to assess their contribution to precise nonlinearities of the system and their role in the emergence of complex perceptual structures. Based on these structural, functional and perturbation data, a new generation of well-constrained and predictive sensory processing models will be built, serving as a platform to extract general computational principles missing to link neural activity to perception and to fuel artificial neural networks technologies.

 Publications

year authors and title journal last update
List of publications.
2019 Thomas Deneux, Evan R Harrell, Alexandre Kempf, Sebastian Ceballo, Anton Filipchuk, Brice Bathellier
Context-dependent signaling of coincident auditory and visual events in primary visual cortex
published pages: , ISSN: 2050-084X, DOI: 10.7554/elife.44006
eLife 8 2020-04-15
2019 Sebastian Ceballo, Jacques Bourg, Alexandre Kempf, Zuzanna Piwkowska, Aurélie Daret, Pierre Pinson, Thomas Deneux, Simon Rumpel, Brice Bathellier
Cortical recruitment determines learning dynamics and strategy
published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-019-09450-0
Nature Communications 10/1 2020-04-15
2019 Sebastian Ceballo, Zuzanna Piwkowska, Jacques Bourg, Aurélie Daret, Brice Bathellier
Targeted Cortical Manipulation of Auditory Perception
published pages: 1168-1179.e5, ISSN: 0896-6273, DOI: 10.1016/j.neuron.2019.09.043
Neuron 104/6 2020-04-15

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