Connecting the dots in ethology: applying network theory to understand neural and animal collectives

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We investigate reduced order models for acoustic and electromagnetic wave problems in parametrically defined domains. The parameter-to-solution maps are approximated following the so-called Galerkin POD-NN method, which combines the construction of a reduc ...

Rock cuttings from destructive boreholes are a common and cheaper source of drilling materials that can be used to determine underground geology compared to rock core samples. Classifying manually the series of cuttings can be a long and tedious process an ...

Wearable Sensor (WS)-based monitoring systems detect minute patient movements/ demands and abnormalities through periodic sensing and imaging. Sensor data observed over different intervals is not constant or available based on operating sequences. Due to v ...

Human movements emerge from complex interactions between the musculoskeletal system, the neural system and the external environment. Their control is realised through a hierarchical neural system composed of the brain, the spinal cord (SC), and remaining n ...

A computer-implemented method of training a neural network for generating upper or lower limb motion control signals to operate a virtual, robotic or prosthetic upper or lower limb, comprising providing or receiving, at a neural network, electromyographic ...

Self-regulated learning (SRL) has been extensively studied using self-reported measures, such as surveys, and more recently, behavioral measures, such as trace data. While both modalities offer insights into SRL, their relationship remains ambiguous. Altho ...

Notions of surprise and novelty have been used in various experimental and theoretical studies across multiple brain areas and species. However, 'surprise' and 'novelty' refer to different quantities in different studies, which raises concerns about whethe ...

Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...

Planning and execution of voluntary movement depend on the contribution of distinct classes of neurons in primary motor and premotor areas. However, timing and pattern of activation of GABAergic cells during specific motor behaviors remain only partly unde ...

Objective. Translational studies on motor control and neurological disorders require detailed monitoring of sensorimotor components of natural limb movements in relevant animal models. However, available experimental tools do not provide a sufficiently ric ...

The Neurorobotics Platform of the Human Brain Project hosts many different large-scale models that can easily be connected with each other. Here, we linked a deep neural network for saliency computation to a spiking cortical model for visual segmentation ( ...

The estimation of the apparent axon diameter (AAD) via diffusion MRI is affected by the incoherent alignment of single axons around its axon bundle direction, also known as orientational dispersion. The simultaneous estimation of AAD and dispersion is chal ...

Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned in the process of learning. Neurons act simultaneousl ...

Certain brain disorders, resulting from brainstem infarcts, traumatic brain injury, stroke and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders wou ...

Recent advances in traffic flow theory at the network level, namely the Macroscopic Fundamental Diagram (MFD), reveals the existence of well-defined laws of congestion dynamics at aggregated levels. The same knowledge for multimodal networks however is lim ...

What is the self? This major philosophical question has been considered too complex to be studied in neuroscience. However, during the last 20 years, researchers from different disciplines within the cognitive neurosciences have started investigating the n ...

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervise ...

Connecting the dots in ethology: applying network theory to understand neural and animal collectives

Chat with Graph Search

Galerkin neural network-POD for acoustic and electromagnetic wave propagation in parametric domains

X-ray Micro-CT based characterization of rock cuttings with deep learning

Enhancing wearable sensor data analysis for patient health monitoring using allied data disparity technique and multi instance ensemble perceptron learning

Investigation of the role of spinal circuits in human motor control, from healthy to rehabilitation conditions

Wearable electromyographic device and system and methods for motion decoding of body parts

Navigating Self-regulated Learning Dimensions: Exploring Interactions Across Modalities

Surprise and novelty in the brain

The inductive bias of deep learning: Connecting weights and functions

Fast-Spiking Interneurons of the Premotor Cortex Contribute to Initiation and Execution of Spontaneous Actions

A versatile robotic platform for the design of natural, three-dimensional reaching and grasping tasks in monkeys

Using the Neurorobotics platform to explain global processing in visual crowding

Orientation-Dispersed Apparent Axon Diameter via Multi-Stage Spherical Mean Optimization

Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

Understanding and Decoding Imagined Speech using Electrocorticographic Recordings in Humans

A Dynamic Network Approach for Multimodal Urban Mobility : Modeling, Pricing and Control

Turning Body and Self Inside Out : Extero- and Interoceptive Signal Integration in Temporo-parietal and Insular Cortex

Perceptual learning, roving and the unsupervised bias