Multisensory integration using dynamical Bayesian networks.

Mohsen Falahi,Fatemeh Bakouie,Shahriar Gharibzadeh,Taher Abbas Shangari

doi:10.3389/fncom.2015.00058

Abstract

Multisensory integration using dynamical Bayesian networks.

Highlights

Multisensory Integration (MSI) is the study of how information coming from different sensory modalities, such as vision, audition and etc. are being integrated by the nervous system (Stein et al, 2009) as a complex system
According to Kalman Filter (KF), it is provable that data fusion of two different kinds of data for one variable measurement leads to more accurate results (Kalman, 1960)
As different formats are used by each sensory modality to encode the same properties of the environment or body, MSI cannot be as simple as an averaging between sensory inputs (Deneve and Pouget, 2004)

Summary

Introduction

Multisensory Integration (MSI) is the study of how information coming from different sensory modalities, such as vision, audition and etc. are being integrated by the nervous system (Stein et al, 2009) as a complex system. Multisensory Integration (MSI) is the study of how information coming from different sensory modalities, such as vision, audition and etc. Computational methods, such as Kalman Filter (KF) and Bayesian Networks (BN) are used widely to model probabilistic functions of the nervous system including MSI (Van Der Kooij et al, 1999; Kording and Wolpert, 2004).

Results

Conclusion