Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

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Social behaviors such as cooperation are crucial for mammals. A deeper knowledge of the neuronal mechanisms underlying cooperation can be beneficial for people suffering from pathologies with impaired social behavior. Our aim was to study the brain activit ...

Animals have to navigate complex environments and perform intricate swimming maneuvers in the real world. To conquer these challenges, animals evolved a variety of motion control strategies. While it is known that many factors contribute to motion control, ...

This document contains all the data and the details of the analysis used in the manuscript titled " Control of Ge island coalescence for the formation of nanowires on silicon." https://doi.org/10.1039/D3NH00573A ...

Dataset corresponding to the following manuscript: Slagter, A., Setyadji, J.A., Vogt, E.L. et al. Nanoindentation Hardness and Modulus of Al2O3–SiO2–CaO and MnO–SiO2–FeO Inclusions in Iron. Metall Mater Trans A (2024). https://doi.org/10.1007/s11661-024-0 ...

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional methodologies predominantly utilize central ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Derived from the Karush–Kuhn–Tucker (KKT) optimality conditions of utility maximization, the multiple discrete–continuous extreme value (MDCEV) models are extensively used in the field of time use. This is because they are grounded in a framework that maxi ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

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 ...

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 ...

Neuroprostheses have been used clinically for decades, to help restore or preserve brain functions, when pharmaceutical treatments are inefficient. Although great progress in the field has been made over the years to interface with the nervous system, surf ...

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 ...

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 ...

This thesis explores the how computational methods can be used to enrich the history of architecture. Using Venice as a case study, it establishes a rich and multifaceted information system, made up of temporal, stylistic, photographic, architectural, and ...

Numerous conductor designs for pulsed magnets based on High Temperature Superconductors (HTS), featuring stacks of tapes are currently being proposed. A major contribution to the AC losses is expected to be given by hysteresis losses. Several numerical mod ...

Multi-frequency and Doppler spectral radars are valuable tools for retrieving information on the microphysics of precipitation, particularly snowfall. However, estimating microphysical properties from radar measurements is an ill-posed problem, i.e., diffe ...

Implanted medical devices (IMDs) have been widely developed to support the monitoring and recording of biological data inside the body or brain. Wirelessly powered IMDs, a subset of implantable electronics, have been proposed to eliminate the limitations r ...

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 ...

This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “3D printing of customizable transient bioelectronics and sensors”. This work aims to study and demonstrate ...

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Chat with Graph Search

Involvement of prelimbic cortex neurons and related circuits in the acquisition of a cooperative learning by pairs of rats

Using deep reinforcement learning to investigate stretch feedback during swimming of the lamprey

Data set for control of Ge island coalescence for the formation of nanowires on silicon

Nanoindentation hardness and modulus of Al2O3-SiO2-CaO and MnO-SiO2-FeO inclusions in iron

Improving the accuracy and interpretability of neural networks for wind power forecasting

On-device diagnostic recommendation with heterogeneous federated BlockNets

Robust machine learning for neuroscientific inference

A new flexible multiple discrete–continuous extreme value (MDCEV) choice model: exploring non-monotonic preference in time use decisions

Reliable data-driven decision-making through optimal transport

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

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

Unraveling behavior and cortical signals to guide the development of soft neuroprostheses for auditory restoration and spreading depolarization

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

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

The Skin of Venice: A Computational Approach to the City's Urban Structure

Validation and Application of Hysteresis Loss Model for HTS Stacks and Conductors for Fusion Applications

What can radars tell us about snowfall microphysics? Insights from a Markov chain Monte-Carlo approach

Integrated Wireless Power, Data Communication, and Thermal Sensing System for Autonomous Multisite Brain Implants

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

Dataset for '3D printing of customizable transient bioelectronics and sensors'