Movement Curvature Planning through Force Field Internal Models

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In this thesis, we concentrate on advancing high-level behavioral control policies for robotic systems within the framework of Dynamical Systems (DS). Throughout the course of this research, a unifying thread weaving through diverse fields emerges, and tha ...

An important function of the brain is to interpret incoming sensory information from the outside world to guide adaptive behavior. Understanding how and where sensory information is transformed into motor commands in a context- and learning-dependent manne ...

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

The transition from aquatic to terrestrial environments represents a significant event in the history of evolution. For this transition to occur, animals had to adapt their morphology, physiology and locomotory skills to handle the challenges and interacti ...

An important initial step in fault detection for complex industrial systems is gaining an understanding of their health condition. Subsequently, continuous monitoring of this health condition becomes crucial to observe its evolution, track changes over tim ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical r ...

The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges i ...

ABS T R A C T BACKGROUND: Upper limb (UL) motor impairment following stroke is a leading cause of functional limitations in activities of daily living. Robot-assisted therapy supports rehabilitation, but how its efficacy and the underlying neural mechanism ...

Subcortical brain structures such as the basal ganglia or the thalamus are involved in regulating motor and cognitive behavior. However, their contribution to perceptual consciousness is still unclear, due to the inherent difficulties of recording subcorti ...

Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Dynamic modeling of folding joints is critical for predicting dynamic behavior, optimizing design parameters, and developing control strategies for origami robots and machines. Although kinematics of the folded joints exists, little research describes thei ...

Purpose The aim of this study was to develop a novel approach for predicting the expression status of Epidermal Growth Factor Receptor (EGFR) and its subtypes in patients with Non-Small Cell Lung Cancer (NSCLC) using a Three-Dimensional Convolutional Neura ...

Animal behaviour alternates between stochastic exploration and goal-directed actions, which are generated by the underlying neural dynamics. Previously, we demonstrated that the compositional Restricted Boltzmann Machine (cRBM) can decompose whole-brain ac ...

Over the past decade, there has been a growing interest in the development of intelligent neural interface systems-on-chip (SoCs) for a range of neurological disorders and emerging brain-machine interface (BMI) applications. The shift toward creating intel ...

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

Movement Curvature Planning through Force Field Internal Models

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Geometric Learning: Leveraging differential geometry for learning and control

Neuronal correlates of goal-directed sensorimotor transformation in sensory, motor and higher-order cortical areas

On the number of regions of piecewise linear neural networks

Neuromechanical Simulations of Animats with Decentralised Control

A Comparison of Residual-based Methods on Fault Detection

Optimizing Dynamic Aperture Studies with Active Learning

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Investigating neural resource allocation in the sensorimotor control of extra limbs

Neurophysiological underpinnings of an intensive protocol for upper limb motor recovery in subacute and chronic stroke patients

Subcortical correlates of consciousness with human single neuron recordings

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Dynamic Modeling of an Origami Prismatic Joint

Advancing EGFR mutation subtypes prediction in NSCLC by combining 3D pretrained ConvNeXt, radiomics, and clinical features

The Recurrent Temporal Restricted Boltzmann Machine Captures Neural Assembly Dynamics in Whole-brain Activity

Intelligent Neural Interfaces: An Emerging Era in Neurotechnology

Unveiling the complexity of learning and decision-making