no code implementations • 1 Mar 2024 • Eric Maris
All motor tasks with a mechanical system (a human body, a rider on a bicycle) that is approximately linear in the part of the state space where it stays most of the time (e. g., upright balance control) have the following property: actionable sensory feedback allows for optimal control actions that are a simple linear combination of the sensory feedback.
no code implementations • 23 Feb 2022 • Eric Maris
This paper presents a computational model of this neurobiological component, based on the theory of stochastic optimal feedback control (OFC).
no code implementations • NeurIPS 2018 • Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk, Max Hinne, Eric Maris, Marcel A. J. van Gerven
This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory.
no code implementations • 29 May 2018 • Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva W. P. van den Borne, Yağmur Güçlütürk, Max Hinne, Eric Maris, Marcel A. J. van Gerven
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss.
1 code implementation • 19 May 2017 • Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven, Eric Maris
In the Bayesian filtering example, we show that the method can be used to filter complex nonlinear and non-Gaussian signals defined on manifolds.
no code implementations • NeurIPS 2017 • Luca Ambrogioni, Max Hinne, Marcel van Gerven, Eric Maris
Here we propose to model this causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connectivity.
no code implementations • 10 Apr 2017 • Luca Ambrogioni, Eric Maris
This is possible because the posterior expectation of Gaussian process regression maps a finite set of samples to a function defined on the whole real line, expressed as a linear combination of covariance functions.
no code implementations • 17 Feb 2017 • Luca Ambrogioni, Umut Güçlü, Eric Maris, Marcel van Gerven
Estimating the state of a dynamical system from a series of noise-corrupted observations is fundamental in many areas of science and engineering.
no code implementations • 30 Nov 2016 • Luca Ambrogioni, Eric Maris
Furthermore, the complex-valued Gaussian process regression allows to incorporate prior information about the structure in signal and noise and thereby to tailor the analysis to the features of the signal.
no code implementations • 31 Oct 2016 • Luca Ambrogioni, Eric Maris
In this paper, we introduce a new framework for analyzing nonstationary time series using locally stationary Gaussian process analysis with parameters that are coupled through a hidden Markov model.
no code implementations • 9 May 2016 • Luca Ambrogioni, Marcel A. J. van Gerven, Eric Maris
Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks.