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Principles of Learning in a Recurrent Neural Network

Project description

Unravelling memory formation in the brain

Animals have the unique ability to form memories and update them according to experience. We know that dopaminergic neurons provide teaching signals to accommodate this, but we lack information on the computation process. The EU-funded LeaRNN project aims to identify the neural networks responsible for these teaching signals in the Drosophila brain. Researchers will generate a map of all dopaminergic functional monosynaptic connections and combine it with nervous system activity maps in living animals during memory formation. This will help them unravel the learning circuit in animals and in the process advance neuroscience, machine-learning and robotics.


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.


Net EU contribution
€ 1 635 240,15
Trinity lane the old schools
CB2 1TN Cambridge
United Kingdom

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East of England East Anglia Cambridgeshire CC
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00

Beneficiaries (2)