title: Recurrent network for multisensory integration-identification of common sources of audiovisual stimuli.
abstract: We perceive our surrounding environment by using different sense organs. However, it is not clear how the brain estimates information from our surroundings from the multisensory stimuli it receives. While Bayesian inference provides a normative account of the computational principle at work in the brain, it does not provide information on how the nervous system actually implements the computation. To provide an insight into how the neural dynamics are related to multisensory integration, we constructed a recurrent network model that can implement computations related to multisensory integration. Our model not only extracts information from noisy neural activity patterns, it also estimates a causal structure; i.e., it can infer whether the different stimuli came from the same source or different sources. We show that our model can reproduce the results of psychophysical experiments on spatial unity and localization bias which indicate that a shift occurs in the perceived position of a stimulus through the effect of another simultaneous stimulus. The experimental data have been reproduced in previous studies using Bayesian models. By comparing the Bayesian model and our neural network model, we investigated how the Bayesian prior is represented in neural circuits.
authors.affiliation: Graduate School of Frontier Sciences, The University of Tokyo Kashiwa, Chiba, Japan.
authors.forename: Itsuki
authors.initials: I
authors.lastname: Yamashita
authors.forename: Kentaro
authors.initials: K
authors.lastname: Katahira
authors.forename: Yasuhiko
authors.initials: Y
authors.lastname: Igarashi
authors.forename: Kazuo
authors.initials: K
authors.lastname: Okanoya
authors.forename: Masato
authors.initials: M
authors.lastname: Okada
keywords: Mexican-hat type interaction; causality inference; multisensory integration; recurrent neural network; spatial orientation
references.citation: Curr Opin Neurobiol. 1994 Aug;4(4):569-79
references.pmid: 7812147
references.citation: Nat Neurosci. 1999 Aug;2(8):740-5
references.pmid: 10412064
references.citation: Biol Cybern. 1977 Aug 3;27(2):77-87
references.pmid: 911931
references.citation: Exp Brain Res. 2004 Sep;158(2):252-8
references.pmid: 15112119
references.citation: PLoS One. 2007 Sep 26;2(9):e943
references.pmid: 17895984
references.citation: J Physiol. 1962 Jan;160:106-54
references.pmid: 14449617
references.citation: J Cogn Neurosci. 2003 Jan 1;15(1):20-9
references.pmid: 12590840
references.citation: Neural Comput. 2013 Jan;25(1):1-45
references.pmid: 23020108
references.citation: Curr Biol. 2004 Feb 3;14(3):257-62
references.pmid: 14761661
references.citation: Neural Comput. 1998 Feb 15;10(2):373-401
references.pmid: 9472487
references.citation: Nat Neurosci. 2006 Nov;9(11):1432-8
references.pmid: 17057707
references.citation: J Neurosci. 1997 Nov 1;17(21):8621-44
references.pmid: 9334433
references.citation: J Neurosci. 1996 Feb 15;16(4):1486-510
references.pmid: 8778300
references.citation: Psychol Sci. 2000 Sep;11(5):353-9
references.pmid: 11228904
references.citation: Neural Comput. 2007 Dec;19(12):3335-55
references.pmid: 17970656
references.citation: Behav Brain Res. 2001 Jun;121(1-2):69-79
references.pmid: 11275285
references.citation: J Physiol Paris. 2003 Jul-Nov;97(4-6):683-94
references.pmid: 15242674