View Sensitive Cells as a Neural Basis for the Representation of Others in a Self-Centered Frame of Reference

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This work proposes a transfer learning-based encoder-decoder framework to predict the relationship between loading conditions and residual stiffness in carbon fiber reinforced composites and adhesives. The encoder, built from a Convolutional Neural Network ...

Blind use patterns significantly impact visual comfort and energy efficiency in office buildings. To quantify this impact on building performance, blind control occupant behaviour models (BC-OBMs) are typically integrated into building performance simulati ...

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 ...

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 ...

This article summarizes the recent advancements in the design, fabrication, and control of microrobotic devices for the diagnosis and treatment of brain disorders. With a focus on diverse actuation methods, we discuss how advancements in materials science ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

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 ...

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulatio ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

Functional connectivity (FC) between brain regions as manifested via fMRI entails signatures that can be used to differentiate individuals and decode cognitive tasks. In this work, we use methods from graph structure inference to estimate FC, which is in c ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

View Sensitive Cells as a Neural Basis for the Representation of Others in a Self-Centered Frame of Reference

Chattez avec Graph Search

Transfer learning-based approach for evaluating residual stiffness in carbon fiber reinforced composites and adhesives

Development of a blind control occupant behaviour model (BC-OBM) based on contextual and time-related factors through supervised machine learning approaches

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

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

Small-scale robotic devices for medical interventions in the brain

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Investigating neural resource allocation in the sensorimotor control of extra limbs

Fast adaptation to rule switching using neuronal surprise

Driving and suppressing the human language network using large language models

Generalization of Scaled Deep ResNets in the Mean-Field Regime

The neural correlates of topographical disorientation-a lesion analysis study

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Safe Deep Neural Networks

Quantifying the biomimicry gap in biohybrid robot-fish pairs

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Joint Subject-Identification and Task-Decoding From Inferred Functional Brain Graphs Via a Multi-Task Neural Network

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination