On the implementation of nonlinearities in optical neural networks: opinion

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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 ...

Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in ...

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 ...

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 ...

The identification and segmentation of histological regions of interest can provide significant support to pathologists in their diagnostic tasks. However, segmentation methods are constrained by the difficulty in obtaining pixel-level annotations, which a ...

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 ...

Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated ('replayed'), either in the same or reversed order, during bursts of activity (sharp wave-ripples [SWRs]) that occur ...

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and emplo ...

In systems having an anisotropic electronic structure, such as the layered materials graphite, graphene, and cuprates, impulsive light excitation can coherently stimulate specific bosonic modes, with exotic consequences for the emergent electronic properti ...

The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission i ...

Neuromorphic computing takes inspiration from the brain to build highly parallel, energy- and area-efficient architectures. Recently, hardware realizations of neurons and synapses using memristive devices were proposed and applied for the task of correlati ...

On the implementation of nonlinearities in optical neural networks: opinion

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

Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models

A clinical road map for single-cell omics

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

Weakly supervised joint whole-slide segmentation and classification in prostate cancer

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

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

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Real-Time Observation of Phonon-Mediated σ−π Interband Scattering in MgB2

Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function

Neuromorphic Architecture with 1M Memristive Synapses for Detection of Weakly Correlated Inputs