This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control. Bayesian inference is widely regarded as a core computational principle of brain function, providing a normative framework for perception, decision-making, and learning under uncertai...
Sepideh Adamiat, Hongye Wang, Wouter M. Kouw et al.· 0 citations
An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of greatest value, and paying for every meter of travel and the cost of every measurement it takes. Classica...
A. Meera, Pablo Lanillos, Wouter M. Kouw· 0 citations
This work introduces a curvature-aware update law for Expected Free Energy and demonstrates it on a system identification problem in a Van der Pol oscillator and proves that Expected Free Energy has unbiased convergence guarantees for concave functions.
A. Meera, Wouter M. Kouw· IEEE Control Systems Letters· 1 citation
This work expresses multi-output Gaussian process regression as a Forney-style factor graph in which a nearest-neighbor chain orders a fixed candidate set of inputs into a one-dimensional sequence, and compares the factor-graph formulation against an exact kernel-matrix baseline, a sparse-variational inducing-point bas...
Wouter W. L. Nuijten, Esther G. van Pelt, Albert Podusenko et al.· 0 citations
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