Biology subjects

Nieuwenhuis, J. S.

Publications and source records attributed to Nieuwenhuis, J. S..

2 recordsLinked to original sources

bioRxiv2026

A Unified Neurocomputational Framework for Closed-Loop Motor Control and Sense of Agency

Motor control relies on the closed-loop comparison of motor commands and sensory feedback to correct errors and adapt to perturbations. Relevant features must be selected from a rich stream of sensory inputs and bound to the appropriate motor commands. This process remains poorly understood. Closed-loop control is accompanied by the experience of causing the observed feedback, sense of agency (SoA). SoA grounds self-identification, and its impairment is associated with lower prosthesis acceptance and disorders like autism and schizophrenia. Like closed-loop motor control, it relies on comparing desired and observed action outcomes in fronto-parietal circuits. Yet, these two phenomena have been studied independently, leaving SoA without a functional meaning and disregarding subjective aspects in motor control models. We propose that SoA is the subjective correlate of selecting self-caused sensory features for closed-loop control. We tested this in a visuomotor task where SoA was manipulated through temporal delays and adaptation to spatial perturbations served as a proxy for closed-loop integration. Delays similarly modulated SoA and closed-loop integration, suggesting these may emerge from the same phenomenon. We modelled our results in a Bayesian framework in which self-causation probability is inferred from temporal congruence, jointly modulating SoA and the weight attributed to visual feedback.

neuroscience↗

bioRxiv2024

RATS: Unsupervised manifold learning using low-distortion alignment of tangent spaces

With the ubiquity of high-dimensional datasets in various biological fields, identifying low-dimensional topological manifolds within such datasets may reveal principles connecting latent variables to measurable instances in the world. The reliable discovery of such manifold structure in high-dimensional datasets can prove challenging, however, largely due to the introduction of distortion by leading manifold learning methods. The problem is further exacerbated by the lack of consensus on how to evaluate the quality of the recovered manifolds. Here, we present a novel measure of distortion to evaluate low-dimensional representations obtained using different techniques. We additionally develop a novel bottom-up manifold learning technique called Riemannian Alignment of Tangent Spaces (RATS) that aims to recover low-distortion embeddings of data, including the ability to embed closed manifolds into their intrinsic dimension using a unique tearing process. Compared to previous methods, we show that RATS provides low-distortion embeddings that excel in the visualization and deciphering of latent variables across a range of idealized, biological, and surrogate datasets that mimic real-world data. One-sentence summaryWe introduce a novel dimensionality reduction technique that generates low-dimensional embeddings while preserving the global structure within the data for a variety of biological and non-biological datasets.

neuroscience↗