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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.

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

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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