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LeaRNN SIGNED

Principles of Learning in a Recurrent Neural Network

Total Cost €

0

EC-Contrib. €

0

Partnership

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 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.

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

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.

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

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