Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception

Open access
Date
2017-08-18Type
- Journal Article
Citations
Cited 17 times in
Web of Science
Cited 18 times in
Scopus
ETH Bibliography
yes
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Abstract
The robust estimation of dynamical hidden features, such as the position of prey, based on sensory inputs is one of the hallmarks of perception. This dynamical estimation can be rigorously formulated by nonlinear Bayesian filtering theory. Recent experimental and behavioral studies have shown that animals’ performance in many tasks is consistent with such a Bayesian statistical interpretation. However, it is presently unclear how a nonlinear Bayesian filter can be efficiently implemented in a network of neurons that satisfies some minimum constraints of biological plausibility. Here, we propose the Neural Particle Filter (NPF), a sampling-based nonlinear Bayesian filter, which does not rely on importance weights. We show that this filter can be interpreted as the neuronal dynamics of a recurrently connected rate-based neural network receiving feed-forward input from sensory neurons. Further, it captures properties of temporal and multi-sensory integration that are crucial for perception, and it allows for online parameter learning with a maximum likelihood approach. The NPF holds the promise to avoid the ‘curse of dimensionality’, and we demonstrate numerically its capability to outperform weighted particle filters in higher dimensions and when the number of particles is limited. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000191256Publication status
publishedExternal links
Journal / series
Scientific ReportsVolume
Pages / Article No.
Publisher
Nature Publishing GroupOrganisational unit
02533 - Institut für Neuroinformatik / Institute of Neuroinformatics
Related publications and datasets
Is referenced by: https://doi.org/10.1038/s41598-017-17246-9
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Citations
Cited 17 times in
Web of Science
Cited 18 times in
Scopus
ETH Bibliography
yes
Altmetrics