This article highlights specific features of biological neurons and their dendritic trees, whose
adoption may help advance artificial neural networks used in various machine learning …

Numerous neurochemicals have been implicated in the modulation of brain function, making
them appealing analytes for sensors and diagnostics. However, it is a grand challenge to …

Deep neural networks (DNNs) trained on object recognition provide the best current models
of high-level visual cortex. What remains unclear is how strongly experimental choices, such …

Understanding of the evolved biological function of sleep has advanced considerably in the
past decade. However, no equivalent understanding of dreams has emerged. Contemporary …

Despite continual debate for the past 30 years about the function of anterior cingulate cortex
(ACC), its key contribution to neurocognition remains unknown. However, recent …

The utility of machine learning in understanding the motor system is promising a revolution
in how to collect, measure, and analyze data. The field of movement science already …

Recurrent neural networks (RNNs) trained with machine learning techniques on cognitive
tasks have become a widely accepted tool for neuroscientists. In this short opinion piece, we …

Biological neuronal networks (BNNs) are a source of inspiration and analogy making for
researchers that focus on artificial neuronal networks (ANNs). Moreover, neuroscientists …

Digital pathology is gaining prominence among the researchers with developments in
advanced imaging modalities and new technologies. Generative adversarial networks …

Hierarchy is a major organizational principle of the cortex and underscores modern
computational theories of cortical function. The local microcircuit amplifies long-distance …