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.

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

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.)

Resonances (2019)

Resonances and Zeta Functions in Smooth Ergodic Theory and Geometry

Read More  

Aware (2019)

Aiding Antibiotic Development with Deep Analysis of Resistance Evolution

Read More  

MUSMICRO (2020)

Causes and consequences of variation in the mammalian microbiota

Read More