| Paper: | PS-2A.20 | ||
| Session: | Poster Session 2A | ||
| Location: | H Lichthof | ||
| 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: | Bayesian Model for Multisensory Integration and Segregation | ||
| 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.2019.1230-0 | ||
| Authors: | Xiangyu Ma, He Wang, Min Yan, Michael K Y Wong, Hong Kong University of Science and Technology, China; Wenhao Zhang, University of Pittsburgh, United States | ||
| Abstract: | Multisensory integration and segregation are important for processing perceived information in animals. Experimental data indicate that the brain processes information in a Bayesian way. We consider a recently proposed model that is able to perform both multisensory integration and segregation concurrently using congruent and opposite groups of neurons in each sensory module. By incorporating output-dependence in the noise of the neural dynamics, we show that the model is able to yield estimates with excellent agreement with Bayesian inference in the weak stimulus limit, and fairly good agreement in stronger fields. When the prior consists of a correlated component and an independent component, we show that Bayesian inference can be achieved by incorporating an additional layer of neuron groups. | ||