Paul F. M. J. Verschure

This paper introduces a biomimetic agent that autonomously regulates internal needs through a neural mass model, outperforming control agents in efficiency and stability.

This paper introduces MoHA, a model that integrates motivational and visual inputs to generate cognitive maps, enabling adaptive navigation in reinforcement learning agents.

This paper investigates how excitatory-inhibitory homeostasis shapes edge-of-bifurcation dynamics and gamma oscillations in cortical networks using the Wilson-Cowan model.

This study shows that multiple excitatory-inhibitory homeostasis mechanisms contribute to metastable dynamics in cortical networks, with local gamma oscillations playing a key role.

The paper develops a theoretical framework showing theta-band oscillations optimize the speed-precision trade-off in phase coding neurons, maximizing information rate within the theta band.

This study uses a computational model to show that excitatory-inhibitory homeostasis drives global functional connectivity recovery after cortical lesions and relates to post-stroke symptoms.

The paper compares two models for simulating brain activity across modalities, showing they can replicate functional connectivity and dynamics but with varying performance across modalities.

This paper investigates how hippocampal theta oscillations normalize episodic memory information content relative to sensory input density during virtual navigation.

This study investigates how theta oscillations in the supplementary motor areas (SMAs) support deliberate action switching during a motor sequence task in humans.

The paper examines how theta and gamma oscillations in the basal forebrain change during exploration of novel environments and finds that theta-gamma coupling increases with learning.

This paper analyzes the degree distribution of human brain functional connectivity networks across resolutions and finds they follow a short-tailed generalized Pareto distribution rather than heavy-tailed power laws.

This study investigates how purely distal environmental cues influence body ownership by manipulating sensory predictability in an embodied task.

This study shows that distal cues affecting task predictions influence body ownership and performance through forward models.

This paper models grid-cell scale and oscillatory frequency gradients in the MEC using spiking neurons and finds intrinsic cellular properties, not external oscillations, drive these patterns.

This study uses a meta-analysis to show that VR-based rehabilitation for upper extremities in stroke patients remains effective beyond the traditional 3-6 month critical recovery window, extending over a year.

The paper proposes and validates an alternative hypothesis for anticipatory control through sensory predictions rather than motor error-based inverse models in a simulated task.