Impact of higher order network structure on emergent cortical activity

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While convolution and self-attention mechanisms have dominated architectural design in deep learning, this survey examines a fundamental yet understudied primitive: the Hadamard product. Despite its widespread implementation across various applications, th ...

The uncanny ability of over-parameterised neural networks to generalise well has been explained using various ‘simplicity biases’. These theories postulate that neural networks avoid overfitting by first fitting simple, linear classifiers before learning m ...

The design of wings is crucial for tailless Flapping-wing robots (FWRs), as these robots rely exclusively on their wings for lift generation and body control. However, the current methodology for wing design primarily depends on the experience and heuristi ...

Despite widespread helmet usage in high-speed sports, the incidence of brain injuries continues to rise, indicating some deficiencies in current helmet technologies. Existing helmet safety standards only address linear impacts, failing to account adequatel ...

A potential framework to estimate the volume of water stored in a porous storage reservoir from seismic data is neural networks. In this study, the man-made groundwater reservoir is modeled as a coupled poroviscoelastic–viscoelastic medium, and the underly ...

Long-range axonal projections of diverse classes of neocortical excitatory neurons likely contribute to brain-wide interactions processing sensory, cognitive and motor signals. Here, we performed light-sheet imaging of fluorescently labeled axons from gene ...

Metal additive manufacturing is a recent breakthrough technology that promises automated production of complex geometric shapes at low operating costs. However, its potential is not yet fully exploited due to the low reproducibility of quality in mass prod ...

BACKGROUND: Cortical excitation/inhibition dynamics have been suggested as a key mechanism occurring after stroke. Their supportive or maladaptive role in the course of recovery is still not completely understood. Here, we used transcranial m ...

IntroductionTranscranial magnetic stimulation (TMS) mapping has become a critical tool for exploratory studies of the human corticomotor (M1) organization. Here, we propose to gather existing cutting-edge TMS-EMG and TMS-EEG approaches into a combined mult ...

Cells in the mammalian cerebral cortex exhibit layer-dependent patterns in their distribution. Classical methods of determining cell type distributions typically employ a painstaking process of large-scale sampling and characterization of cellular composit ...

The body temperature of mice is higher at night than during the day. We show here that global deletion of acid-sensing ion channel 1a (ASIC1a) results in lower body temperature during a part of the night. ASICs are pH sensors that modulate neuronal activit ...

Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to heat and particle flux constraints on the divertor target. Simulation is an important tool to understand and control these plasmas, however, for real-time applications ...

Majority of the past research on application of machine learning (ML) in earthquake engineering focused on contrasting the predictive performance of different ML algorithms. In contrast, the emphasis of this paper is on the use of data to boost the predict ...

The question of whether attention-related disorders such as attention-deficit/hyperactivity disorder (ADHD) are best understood as clinical categories or as extreme ends of a spectrum is an ongoing debate. Assessing individuals with varying degrees of atte ...

In this thesis, we advocate that Computer-Aided Engineering could benefit from a Geometric Deep Learning revolution, similarly to the way that Deep Learning revolutionized Computer Vision. To do so, we consider a variety of Computer-Aided Engineering pr ...

Impact of higher order network structure on emergent cortical activity

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Hadamard product in deep learning: Introduction, Advances and Challenges

Neural networks trained with SGD learn distributions of increasing complexity

Enhancing Lift and Dynamic Stability Through Morphological Optimization of Wings for Tailless Flapping-Wing Robots

Inverse material design to tailor helmet protection against brain injury

Monitoring of water volume in a porous reservoir using seismic data: A 3D simulation study

Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices

Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance

Stroke Recovery–Related Changes in Cortical Reactivity Based on Modulation of Intracortical Inhibition

Multi-scale and cross-dimensional TMS mapping: A proof of principle in patients with Parkinson's disease and deep brain stimulation

Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization

ASIC1a affects hypothalamic signaling and regulates the daily rhythm of body temperature in mice

Fast dynamic 1D simulation of divertor plasmas with neural PDE surrogates

The automated collapse data constructor technique and the data-driven methodology for seismic collapse risk assessment

Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation

The continuum of attention dysfunction: Evidence from dynamic functional network connectivity analysis in neurotypical adolescents

Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering