SOURCE-LINKED INTELLIGENCE

Dynamics underlying learning in complex environments

CORDIS · observation · Publication date unknown

to uncover the behavioral, population- level, and synaptic learning rules responsible for guiding learning in complex environments. By interweaving statistical modelling, dynamical systems theory and machine learning, DULCE will: i) Develop hierarchical models of behavior that can disentangle the rules governing simultaneously occurring learning processes. ii) Provide a unified theory of how region-specific learning rules in the cortex, cerebellum, and striatum coordinate to form a distributed learning system. iii) Develop interpretable dimensionality reduction methods to identify the rules governing how task-relevant dynamics evolve in large-scale neural data over learning. Through this three-pronged attack, DULCE aims to lay the foundation necessary to uncover the neural mechanisms controlling the Dynamics Underlying Learning in Complex Environments. learning, naturalistic behavior, neural population data, cerebellum, motor cortex, basal ganglia, machine learning, recurrent neural networks, dimensionality reduction, motor control

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recordType

award

status

SIGNED

region

EU

value

1925875

unit

EUR