Abstract
Two theoretical studies reveal how networks of neurons may behave during reward-based learning.
Highlights
T o thrive in their environments, animals must learn how to process lots of inputs and take appropriate actions (Figure 1A)
While the latest algorithms for reinforcement learning achieve human-level performance on many problems, we still do not fully understand how brains learn from rewards
Actions are generated by a recurrent network that is composed of hundreds of interconnected neurons that continuously influence each others’ activity (Figure 1)
Summary
T o thrive in their environments, animals must learn how to process lots of inputs and take appropriate actions (Figure 1A). Rewardbased training of recurrent neural networks for cognitive and value-based tasks. The studies address two complementary aspects of reward-based learning in recurrent neuronal networks – artificial networks of neurons that exhibit dynamic, temporallyvarying activity.
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