Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3

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Peripersonal space (PPS) is the region of space near the body, the multisensory interface where interactions with the environment predominantly occur. This space is represented by a specialized neural system that integrates tactile and external stimuli as ...

Machine learning for protein modeling faces significant challenges due to proteins' inherently dynamic nature, yet most graph-based machine learning methods rely solely on static structural information. Recently, the growing availability of molecular dynam ...

Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic fun ...

In a matter of years, single-cell omics has matured from a pioneering technique employed by just a handful of specialized laboratories to become a ubiquitous feature of biological research and a key driver of scientific discovery. The widespread adoption a ...

The simplification of neural network models is described. For example, a method for simplifying a neural network model includes providing the neural network model to be simplified, defining a first temporal filter for the conveyance of input from a neuron ...

Mechano-electric regulations (MER) play an important role in the maintenance of cardiac performance. Mechano-calcium and mechano-electric feedback (MCF and MEF) pathways adjust the cardiomyocyte contractile force according to mechanical perturbations and a ...

Optical neural networks are expected to be hybrid systems for the foreseeable future combining optics and electronics. The linear portion of the system, consisting of the weighted interconnections between the neurons, can be realized optically. The nonline ...

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

Memory formation and storage rely on multiple interconnected brain areas, the contribution of which varies during memory consolidation. The medial prefrontal cortex, in particular the prelimbic cortex (PL), was traditionally found to be involved in remote ...

Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3

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Computational models of peripersonal space representation

Boosting Protein Graph Representations through Static-Dynamic Fusion

Characterization of Machine Learning-Based Surrogate Models of Neural Activation Under Electrical Stimulation

A clinical road map for single-cell omics

Simplification of spiking neural network models

A detailed mathematical model of the human atrial cardiomyocyte: integration of electrophysiology and cardiomechanics

On the implementation of nonlinearities in optical neural networks: opinion

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

On double-descent in uncertainty quantification in overparametrized models

Brain-wide screen of prelimbic cortex inputs reveals a functional shift during early fear memory consolidation