Fully implicit parallel simulation of single neurons

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Gels made of telechelic polymers connected by reversible cross-linkers are a versatile design platform for biocompatible viscoelastic materials. Their linear response to a step strain displays a fast, near-exponential relaxation when using low-valence cros ...

Many empirical studies have provided evidence for the emergence of algorithmic mechanisms (abilities) in the learning of language models, that lead to qualitative improvements in the model capabilities. Yet, a theoretical characterization of how such mecha ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model and its variants have been widely used to do so. The historical model conside ...

Sign Language conveys information through multiple channels composed of manual (handshape, hand movement) and non-manual (facial expression, mouthing, body posture) components. Sign language assessment involves giving granular feedback to a learner, in ter ...

Electron cloud continues to be one of the main limiting factors of the Large Hadron Collider (LHC), the biggest accelerator at CERN. These clouds form in the beam chamber when positively charged particles are passing through and cause unwanted effects in b ...

The distribution of mass in galaxy-scale strong gravitational lenses is often modelled as an elliptical power-law plus 'external shear', which notionally accounts for neighbouring galaxies and cosmic shear along our line of sight. A small amount of externa ...

In less than a century, electronic computers have become essential to everyday life and have transformed scientific research. Their use in solving scientific problems has integrated theoretical and experimental approaches, often blurring the lines between ...

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hard ...

Today's continued increase in demand for processing power, despite the slowdown of Moore's law, has led to an increase in processor count, which has resulted in energy consumption and distribution problems. To address this, there is a growing trend toward ...

Fairness of decision-making algorithms is an increasingly important issue. In this paper, we focus on spectral clustering with group fairness constraints, where every demographic group is represented in each cluster proportionally as in the general populat ...

This paper introduces MNE-RT, a Python package designed for real-time neural feature extraction from magne-toencephalography (MEG) and electroencephalography (EEG) signals in Brain-Computer Interface (BCI) systems. The package incorporates efficient algori ...

Synaptic plasticity underlies our ability to learn and adapt to the constantly changing environment. The phenomenon of synapses changing their efficacy in an activity-dependent manner is often studied in small groups of neurons in vitro or indirectly throu ...

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

Interactions are ubiquitous in our world, spanning from social interactions between human individuals to physical interactions between robots and objects to mechanistic interactions among different components of an intelligent system. Despite their prevale ...

Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

A key component in developing atrial digital twins (ADT) - virtual representations of patients’ atria — is the accurate prescription of myocardial fibers which are essential for the tissue characterization. Due to the difficulty of reconstructing atrial fi ...

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possi ...

The outstanding information processing capacity of the brain relies on numerous molecular mechanisms. It is an extremely energy-expensive function, which involves specialization and collaboration of different cell types. A common framework to study the com ...

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learnin ...

Fully implicit parallel simulation of single neurons

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Valence can control the nonexponential viscoelastic relaxation of multivalent reversible gels

A phase transition between positional and semantic learning in a solvable model of dot-product attention

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment

Characterization of Transverse Instabilities Driven by Electron Cloud

Strong gravitational lensing's 'external shear' is not shear

Simulating Interacting Quantum Systems with Near-Term Quantum Computers

Training hybrid neural networks with multimode optical nonlinearities using digital twins

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Accelerating Spectral Clustering under Fairness Constraints

Advances on Real Time M/EEG Neural Feature Extraction

Principles of Network Plasticity in Neocortical Microcircuits

Domain shift, domain adaptation, and generalization: A focus on MRI

Learning Robust and Adaptive Representations: from Interactions, for Interactions

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Defining myocardial fiber bundle architecture in atrial digital twins

Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub

Molecular reconstruction and simulation of the Neuron-Glia-Vasculature system

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics