SOURCE-LINKED INTELLIGENCE

Toward a new understanding of learning in the brain: dynamic parallel circuit loops for complex learning

CORDIS · observation · Publication date unknown

nding of learning in the brain: dynamic parallel circuit loops for complex learning The brain’s ability to learn is arguably its most exceptional capacity. Learning in biological brains far surpasses machine learning and requires much less training. How does the brain accomplish this? Why is biological learning still better than the most advanced machine learning algorithms to date? According to the standard model of reward-based learning in the brain, a single error signal is broadcast from the dopamine system and used to update the entire network, implementing a simple form of reinforcement learning. However, the standard model fails to predict several recent experimental findings, leaving open the question of how learning is implemented in the brain. In this project, I propose a new theory of how the brain learns: learning is implemented by multiple dopamine-based learning systems working in parallel circuit loops. These loops relay partial error signals to specific processing areas and permit independent evaluation of the value of different features in the external environment as

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recordType

award

status

SIGNED

region

EU

value

1499375

unit

EUR