Neural networks underpin both biological intelligence and modern Al systems, yet there is
relatively little theory for how the observed behavior of these networks arises. Even the …

Neuroscientists apply a range of common analysis tools to recorded neural activity in order
to glean insights into how neural circuits implement computations. Despite the fact that these …

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

As one of the most important paradigms of recurrent neural networks, the echo state network
(ESN) has been applied to a wide range of fields, from robotics to medicine, finance, and …

Neural Networks is an important subject of both biological and artificial intelligence, which
we have not yet fully understand. Recently the field of neural networks has been popularized …

Neuroscience research is undergoing a minor revolution. Recent advances in machine
learning and artificial intelligence research have opened up new ways of thinking about …

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural
networks (ANNs) have exploited biological properties to solve complex problems. However …

Deep neural networks exhibit a fascinating spectrum of phenomena ranging from
predictable scaling laws to the unpredictable emergence of new capabilities as a function of …

Deep neural networks follow a pattern of connectivity that was loosely inspired by
neurobiology. The existence of a layered architecture, with deeper neurons representing …

Network neuroscience has catalysed crucial insights into the systems-level organisation of
the brain, however the lack of a 'ground truth'inherently limits direct interpretation. In parallel …