Animals can quickly learn to make appropriate decisions according to their environment that can change over a wide range of timescales. Yet the neural computation underling the adaptive decision making is not well understood. To investigate basic computational principles and neural mechanisms, here we study simple neural network models for decision making with learning on multiple timescales, and we test our model's predictions in experimental data. We provide basic network models for value-based decision making under uncertainty.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8BR8Q81 |
| Date | January 2014 |
| Creators | Iigaya, Kiyohito |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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