Publications related to Risk prediction error signaling: A two-component response?

Unveiling the complexity of learning and decision-making

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

EPFL2024

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

Maria del Carmen Sandi Perez, Elias Georges Gebara, Ana del Rocio Conde Moro

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 ...

Springer2024

White adipose tissue distribution and amount are associated with increased white matter connectivity

Jean-Philippe Thiran, Elda Fischi Gomez, Gabriel Girard, Philipp Johannes Koch, Liana Okudzhava

Obesity represents a significant public health concern and is linked to various comorbidities and cognitive impairments. Previous research indicates that elevated body mass index (BMI) is associated with structural changes in white matter (WM). However, a ...

Wiley2024

Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills

Elena Beanato, Esra Neufeld, Friedhelm Christoph Hummel, Takuya Morishita, Maximilian Jonas Wessel, Traian Popa, Pierre Theopistos Vassiliadis, Julie Duqué, Fabienne Windel

Reinforcement feedback can improve motor learning, but the underlying brain mechanisms remain underexplored. In particular, the causal contribution of specific patterns of oscillatory activity within the human striatum is unknown. To address this question, ...

Nature Portfolio2024

Thalamic contributions to psychosis susceptibility: Evidence from co-activation patterns accounting for intra-seed spatial variability (μCAPs)

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton, Nada Kojovic, Farnaz Delavari

The temporal variability of the thalamus in functional networks may provide valuable insights into the pathophysiology of schizophrenia. To address the complexity of the role of the thalamic nuclei in psychosis, we introduced micro-co-activation patterns ( ...

Wiley2024

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

Axel Bisi, Alberto Silvio Chiappa, Alexander Mathis, Alessandro Marin Vargas

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 ...

Zenodo2024

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

Emilie Cornelia Maria Revol

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 ...

EPFL2024

Fast adaptation to rule switching using neuronal surprise

Wulfram Gerstner, Martin Louis Lucien Rémy Barry

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 ...

2024

An immersive virtual reality tool for assessing left and right unilateral spatial neglect

Olaf Blanke, Andrea Serino, Roberta Ronchi

The reported rate of the occurrence of unilateral spatial neglect (USN) is highly variable likely due to the lack of validity and low sensitivity of classical tools used to assess it. Virtual reality (VR) assessments try to overcome these limitations by pr ...

Wiley2024

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

EPFL2024

On the Generalization of Stochastic Gradient Descent with Momentum

Volkan Cevher, Kimon Antonakopoulos

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

Microtome Publishing2024

Robust machine learning for neuroscientific inference

Steffen Schneider

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 ...

EPFL2024

Small-scale robotic devices for medical interventions in the brain

Lorenzo Francesco John Noseda, Mahmut Selman Sakar

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 ...

2024

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

David Atienza Alonso, Giovanni Ansaloni, Flavio Ponzina, Amirhossein Shahbazinia, José Angel Miranda Calero, Jonathan Dan

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

IEEE2024

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

Michele Bevilacqua

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 ...

EPFL2024

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Arnout Jan J Devos

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

EPFL2024

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

Silvestro Micera, Daniela De Luca

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, ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

Silvestro Micera, Laura Toni, Fiorenzo Artoni, Simone Romeni

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. ...

Aip Publishing2024

Striatal Dopamine Signals and Reward Learning

Carl Petersen, Sylvain Crochet, Mauro Pulin, Yanqi Liu, Christos Sourmpis, Parviz Ghaderi, Pol Bech Vilaseca, Meriam Malekzadeh, Anthony Pierre Robert Renard, Robin François Virginien Dard

We are constantly bombarded by sensory information and constantly making decisions on how to act. In order to optimally adapt behavior, we must judge which sequences of sensory inputs and actions lead to successful outcomes in specific circumstances. Neuro ...

Oxford Univ Press2023

A striatal circuit balances learned fear in the presence and absence of sensory cues

Ralf Schneggenburger, Olexiy Kochubey, Michael Kintscher

During fear learning, defensive behaviors like freezing need to be finely balanced in the presence or absence of threat-predicting cues (conditioned stimulus, CS). Nevertheless, the circuits underlying such balancing are largely unknown. Here, we investiga ...

eLIFE SCIENCES PUBL LTD2023