| Paper: | PS-2B.22 | ||
| Session: | Poster Session 2B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Neural Likelihood | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1233-0 | ||
| Authors: | Christoph Blessing, Edgar Y. Walker, Katrina R. Quinn, University Tuebingen, Germany; R. James Cotton, Shirley Ryan Ability Lab, United States; Wei Ji Ma, New York University, United States; Andreas S. Tolias, Baylor College of Medicine, United States; Hendrikje Nienborg, Fabian H. Sinz, University Tuebingen, Germany | ||
| Abstract: | A large body of evidence shows that perceptual decision making in humans and animals accounts for uncertainty in the relevant stimulus variable. This suggests that the decision is based on a distribution over stimuli given the neuronal activity rather than single point estimates. The likelihood over the stimuli captures this uncertainty for a fixed neuronal response. Because the neuronal population response can be high dimensional, estimating a per-trial likelihood can be challenging. Previous work has thus focused on parametric models, which can introduce a bias by ignoring noise correlations. Here, we present a simple yet general method to decode a per-trial likelihood based on neural networks. Our method applies to discrete and continuous, as well as static and time-series data. We demonstrate it on recordings from two experimental visual paradigms in Macaque V1 and V2. | ||