In a changing environment the ability to continually and rapidly learn new behaviors is crucial for humans and animals to survive. How the brain stores and integrates new information without forgetting (continual learning, CL) and utilizes prior knowledge to quickly adapt to new situations (fast forward generalization, FFG) remains a mystery. To shed light on this crucial question I propose a novel interdisciplinary approach that derives inspiration from a theoretical machine-learning approach for artificial neural networks (ANN) and simultaneously addresses FFG and CL. In this dual-network model, a so-called meta-network learns to identify the context for all previous tasks and modulates a second, stimulus-processing network, in a task/context specific manner to efficiently solve the task. As a result, similar task contexts elicit the selection of similar stimulus processing networks thereby enabling FFG. In the brain, the medial prefrontal cortex (mPFC) is known to receive contextual information (environment, setting), to store task information and to project to sensory areas. This makes it a promising candidate to function as a meta-network in the brain. In the proposed study, I will investigate if and how the mPFC affects sensory processing in a task-specific manner during CL and FFG, functioning as a meta-network. I will utilize cutting-edge experimental techniques including miniaturized microscopes in freely behaving mice to record neuronal populations in mPFC during FFG and CL combined with chemo- and optogenetic tools. To investigate how mPFC affects stimulus processing on the single neuron level, I will combine calcium and voltage imaging in brain slices. This interdisciplinary project will provide novel insights into the possible implementation of meta-learning in the brain to address CL and FFG. Completion of this innovative project in the multidisciplinary environment of my host lab will provide me with the ideal setting to reach scientific independence.
Fields of science
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