| Budget Amount *help |
¥82,160,000 (Direct Cost: ¥63,200,000、Indirect Cost: ¥18,960,000)
Fiscal Year 2020: ¥14,560,000 (Direct Cost: ¥11,200,000、Indirect Cost: ¥3,360,000)
Fiscal Year 2019: ¥14,560,000 (Direct Cost: ¥11,200,000、Indirect Cost: ¥3,360,000)
Fiscal Year 2018: ¥14,560,000 (Direct Cost: ¥11,200,000、Indirect Cost: ¥3,360,000)
Fiscal Year 2017: ¥14,560,000 (Direct Cost: ¥11,200,000、Indirect Cost: ¥3,360,000)
Fiscal Year 2016: ¥23,920,000 (Direct Cost: ¥18,400,000、Indirect Cost: ¥5,520,000)
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| Outline of Final Research Achievements |
In accordance with the aims of the project, we developed brain inspired algorithms for artificial intelligence and utilized the theories and methods of artificial intelligence for advancing brain science.
We developed data-efficient model-based reinforcement algorithms and analyzed the data-efficiency of deep reinforcement learning algorithms.
We clarified representations of different variables for sensory inference and reinforcement learning in the cerebral cortex and the basal ganglia. We also revealed that the enhancement of the patience for delayed rewards by serotonin neuron stimulation is dependent on the certainty of reward delivery and the uncertainty of delivery timing, and proposed a novel Bayesian decision model to reproduce animal behaviors.
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