Adaptive actions on shading devices in response to local visual stimuli

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In the last few years, stroke ranked as the second most common cause of death and is the third most significant condition affecting disability-adjusted life years (DALYs) worldwide. Being the most prevalent and quality of life impacting post-stroke symptom ...

Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. ...

Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

Decisions about a current visual stimulus are systematically biased by recently encountered stimuli, a phenomenon known as serial dependence. In human vision, for instance, we tend to report the features of current images as more similar â i.e., an attra ...

This repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by ...

To fully comprehend visual perception, we need to necessarily understand its temporal dimension. Our visual environment is highly dynamic, requiring the processing and integration of temporal signals in order to make sense of it. Many processes, such as th ...

Introduction: Sensory processing is profoundly regulated by brain neuromodulatory systems. One of the main neuromodulators is serotonin which influences higher cognitive functions, such as different aspects of perceptual processing. Accordingly, malfunctio ...

Objective. Artificial vision has been and still is the subject of intense research. The ultimate goal is to help blind people in their daily life. Approaches to artificial vision, including visual prostheses and optogenetics, have strongly focused on resto ...

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, i ...

CEBRA is a machine-learning method that can be used to compress time series in a way that reveals otherwise hidden structures in the variability of the data. It excels at processing behavioural and neural data recorded simultaneously, and it can decode act ...

In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscilla-tion amplitudes or large momentum error are scrape ...

We study socio-political systems in representative democracies. Motivated by problems that affect the proper functioning of the system, we build computational methods to answer research questions regarding the phenomena occurring in them. For each phenomen ...

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 ...

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

Measuring bathymetry has always been a major scientific and technological challenge. In this work, we used a deep learning technique for inferring bathymetry from the depth-averaged velocity field. The training of the neural network is based on 5742 labora ...

The acquisition of survey responses is a crucial component in conducting research aimed at comprehending public opinion. However, survey data collection can be arduous, time-consuming, and expensive, with no assurance of an adequate response rate. In this ...

Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

Introduction Recent work has shown an association between cognitive and visual impairments and two main theories were advanced, namely the sensory deprivation and the common cause theories. Most studies considered only basic visual functions such as visual ...

This paper presents our approach to predicting future error-related events in a robot-mediated gamified phys- ical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable fe ...

Adaptive actions on shading devices in response to local visual stimuli

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Probing and modulating inter-areal coupling in the cortical visual motion processing pathway with non-invasive brain stimulation

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Serial Dependence in Human Visual Perception and Decision-Making

Data and scripts for the RaFSIP scheme

Temporal aspects of feature integration in vision

Investigating the Serotonergic Modulation of Orientation Tuning of Neurons in Primary Visual Cortex of Anesthetized Mice

The role of the visual field size in artificial vision

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Community-based reconstruction and simulation of a full-scale model of region CA1 of rat hippocampus

Brain dynamics uncovered using a machine-learning algorithm

Data-driven modeling of beam loss in the LHC

How Words Move Hearts: Interpretable Machine Learning Models of Bias, Engagement, and Influence in Socio-Political Systems

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

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Bed Topography Inference from Velocity Field Using Deep Learning

Predicting Survey Response with Quotation-based Modeling: A Case Study on Favorability towards the United States

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

No Common Factor Underlying Decline of Visual Abilities in Mild Cognitive Impairment

Predictive Analysis of Errors During Robot-Mediated Gamified Training