Highlights•Artificial and biological neural networks can be analyzed using similar
methods.•Neural analysis has revealed similarities between the representations in artificial …

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

This timely overview and synthesis of recent work in both artificial neural networks and
neurobiology seeks to examine neurobiological data from a network perspective and to …

Conclusion Neural networks are experiencing a revival that not only transforms AI but also
provides new insights about neural computation in biological systems. The contributions in …

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

The goal of computational neuroscience is to find mechanistic explanations of how the
nervous system processes information to support cognitive function and behaviour. At the …

Highlights•Deep neural networks (DNNs) now reach human-level performance on some
perceptual tasks.•They show human-like error patterns and predict sensory cortical …

Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as
a modeling framework for neural computations in the primate brain. Just like individual …

The goal of cognitive neuroscience is to understand how mental operations are performed
by the brain. Given the complexity of the brain, this is a challenging endeavor that requires …

Artificial deep neural networks (DNNs) initially inspired by the brain enable computers to
solve cognitive tasks at which humans excel. In the absence of explanations for such …