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Deciphering deep architectures underlying structured perception in auditory networks

Total Cost €


EC-Contrib. €






 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.

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

Project "DEEPEN" data sheet

The following table provides information about the project.


Organization address
address: RUE MICHEL ANGE 3
city: PARIS
postcode: 75794

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


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 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.


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

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