During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses
are embedded within multilayered networks, making it difficult to determine the effect of an …

As the research on artificial intelligence booms, there is broad interest in brain‐inspired
computing using novel neuromorphic devices. The potential of various emerging materials …

There are two general approaches to develo** artificial general intelligence (AGI):
computer-science-oriented and neuroscience-oriented. Because of the fundamental …

Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long
been considered an important component of learning and memory. Computational and …

Abstract Systems neuroscience seeks explanations for how the brain implements a wide
variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to …

This perspective describes predictive processing as a computational framework for
understanding cortical function in the context of emerging evidence, with a focus on sensory …

Recurrently connected networks of spiking neurons underlie the astounding information
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …

This review article summarises recently proposed theories on how neural circuits in the
brain could approximate the error back-propagation algorithm used by artificial neural …

Artificial neural networks (ANNs) are essential tools in machine learning that have drawn
increasing attention in neuroscience. Besides offering powerful techniques for data analysis …

Deep learning has seen remarkable developments over the last years, many of them
inspired by neuroscience. However, the main learning mechanism behind these advances …