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Ji Hyun Bak

3 accepted papers

2020

Inferring learning rules from animal decision-making

NeurIPS 2020poster

How do animals learn? This remains an elusive question in neuroscience. Whereas reinforcement learning often focuses on the design of algorithms that enable artificial agents to efficiently learn new tasks, here we develop a modeling framework to directly infer the empirical learning rules that anim…

2018

Efficient inference for time-varying behavior during learning

NeurIPS 2018poster

The process of learning new behaviors over time is a problem of great interest in both neuroscience and artificial intelligence. However, most standard analyses of animal training data either treat behavior as fixed or track only coarse performance statistics (e.g., accuracy, bias), providing limite…

Cited by 31SourcePDFScholar
2016

Adaptive optimal training of animal behavior

NeurIPS 2016poster

Neuroscience experiments often require training animals to perform tasks designed to elicit various sensory, cognitive, and motor behaviors. Training typically involves a series of gradual adjustments of stimulus conditions and rewards in order to bring about learning. However, training protocols ar…

Cited by 38SourcePDFScholar