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| Content Provider | PubMed Central |
|---|---|
| Author | Clarke, Aaron Michael Friedrich, Johannes Tartaglia, Elisa M. Silvia, Marchesotti Senn, Walter Herzog, Michael H. |
| Editor | Eleni, Vasilaki |
| Copyright Year | 2015 |
| Abstract | Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Markovian decision processes, where the next state depends on only the current state and action. Little is known about non-Markovian decision making, where the next state depends on more than the current state and action. Learning is non-Markovian, for example, when there is no unique mapping between actions and feedback. We have produced a model based on spiking neurons that can handle these non-Markovian conditions by performing policy gradient descent [1]. Here, we examine the model’s performance and compare it with human learning and a Bayes optimal reference, which provides an upper-bound on performance. We find that in all cases, our population of spiking neurons model well-describes human performance. |
| Related Links | http://dx.doi.org/10.1371/journal.pone.0123105 |
| Starting Page | 123105 |
| File Format | |
| ISSN | 19326203 |
| e-ISSN | 19326203 |
| Journal | PLoS ONE |
| Issue Number | 4 |
| Volume Number | 10 |
| Language | English |
| Publisher | Public Library of Science |
| Publisher Date | 2015-04-01 |
| Access Restriction | Open |
| Rights Holder | Public Library of Science |
| Subject Keyword | Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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