Opendata, web and dolomites

LeaRNN SIGNED

Principles of Learning in a Recurrent Neural Network

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

 LeaRNN project word cloud

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

living    models    discover    animal    first    monosynaptic    cellular    teaching    brain    unknown    circuits    larva    animals    feedback    distributed    model    functions    implementing    signals    feedforward    neurons    memories    manipulating    insect    functional    updates    obtain    computation    multilayered    generating    represented    body    building    drive    associative    upstream    larval    machine    predictions    revolutionize    memory    tractable    connections    circuitry    forming    mushroom    medicine    robotics    principles    nervous    drives    connectome    map    sufficiency    errors    theoretical    dopaminergic    constrained    form    prediction    consolidate    datasets    recurrent    extinguish    updating    fundamental    selectively    provides    time    maps    candidate    neural    algorithms    basic    kingdom    layer    drosophila    generate    synaptic    signal    neuroscience    compute    circuit    actual    match    intact    longer    preliminary    learning    resolution    motifs    uniquely    entire   

Project "LeaRNN" data sheet

The following table provides information about the project.

Coordinator
THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE 

Organization address
address: TRINITY LANE THE OLD SCHOOLS
city: CAMBRIDGE
postcode: CB2 1TN
website: www.cam.ac.uk

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 United Kingdom [UK]
 Total cost 2˙350˙000 €
 EC max contribution 2˙350˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-COG
 Funding Scheme ERC-COG
 Starting year 2019
 Duration (year-month-day) from 2019-09-01   to  2024-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE UK (CAMBRIDGE) coordinator 2˙350˙000.00

Map

 Project objective

Forming memories, generating predictions based on memories, and updating memories when predictions no longer match actual experience are fundamental brain functions. Dopaminergic neurons provide a so-called “teaching signal” that drives the formation and updates of associative memories across the animal kingdom. Many theoretical models propose how neural circuits could compute the teaching signals, but the actual implementation of this computation in real nervous systems is unknown. This project will discover the basic principles by which neural circuits compute the teaching signals that drive memory formation and updates using a tractable insect model system, the Drosophila larva. We will generate, for the first time in any animal, the following essential datasets for a distributed, multilayered, recurrent learning circuit, the mushroom body-related circuitry in the larval brain. First, building on our preliminary work that provides the synaptic-resolution connectome of the circuit, including all feedforward and feedback pathways upstream of all dopaminergic neurons, we will generate a map of functional monosynaptic connections. Second, we will obtain cellular-resolution whole-nervous system activity maps in intact living animals, as they form, extinguish, or consolidate memories to discover the features represented in each layer of the circuit (e.g. predictions, actual reinforcement, and prediction errors), the learning algorithms, and the candidate circuit motifs that implement them. Finally, we will develop a model of the circuit constrained by these datasets and test the predictions about the necessity and sufficiency of uniquely identified circuit elements for implementing learning algorithms by selectively manipulating their activity. Understanding the basic functional principles of an entire multilayered recurrent learning circuit in an animal has the potential to revolutionize, not only neuroscience and medicine, but also machine-learning and robotics.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "LEARNN" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

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

More projects from the same programme (H2020-EU.1.1.)

ENUF (2019)

Evaluation of Novel Ultra-Fast selective III-V Epitaxy

Read More  

Diverge (2019)

Generation of ultra-deep libraries of transcriptional activators for gene therapy

Read More  

MITOvTOXO (2020)

Understanding how mitochondria compete with Toxoplasma for nutrients to defend the host cell

Read More