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Principles of Learning in a Recurrent Neural Network

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.

Field of science

  • /social sciences/educational sciences/pedagogy/teaching

Call for proposal

ERC-2018-COG
See other projects for this call

Funding Scheme

ERC-COG - Consolidator Grant

Host institution

THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE
Address
Trinity Lane The Old Schools
CB2 1TN Cambridge
United Kingdom
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 1 635 240,15

Beneficiaries (2)

THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE
United Kingdom
EU contribution
€ 1 635 240,15
Address
Trinity Lane The Old Schools
CB2 1TN Cambridge
Activity type
Higher or Secondary Education Establishments
UNITED KINGDOM RESEARCH AND INNOVATION
United Kingdom
EU contribution
€ 714 759,85
Address
Polaris House North Star Avenue
SN2 1FL Swindon
Activity type
Research Organisations