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Sci Rep. 2017 Aug 18;7(1):8722. doi: 10.1038/s41598-017-06519-y.

Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception.

Author information

1
Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich, Switzerland. annak@ini.uzh.ch.
2
Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland. annak@ini.uzh.ch.
3
Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich, Switzerland.
4
Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland.
5
Department for Electrical Engineering & Computer Science, Technische Universität Berlin, Berlin, Germany.
6
Bernstein Center for Computational Neuroscience, Berlin, Germany.

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.

PMID:
28821729
PMCID:
PMC5562918
DOI:
10.1038/s41598-017-06519-y
[Indexed for MEDLINE]
Free PMC Article

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