Complex networks have become the main paradigm for modelling the dynamics of
interacting systems. However, networks are intrinsically limited to describing pairwise …

The collective activity of a population of neurons, beyond the properties of individual cells, is
crucial for many brain functions. A fundamental question is how activity correlations between …

Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our
ability to record large neural and behavioural data increases, there is growing interest in …

In this Review, we describe the singular success of attractor neural network models in
describing how the brain maintains persistent activity states for working memory, corrects …

Learning and interpreting the structure of the environment is an innate feature of biological
systems, and is integral to guiding flexible behaviors for evolutionary viability. The concept of …

Two different perspectives have informed efforts to explain the link between the brain and
behaviour. One approach seeks to identify neural circuit elements that carry out specific …

Neuroscientists today can measure activity from more neurons than ever before, and are
facing the challenge of connecting these brain-wide neural recordings to computation and …

The ongoing exponential rise in recording capacity calls for new approaches for analysing
and interpreting neural data. Effective dimensionality has emerged as an important property …

Computational models in neuroscience usually take the form of systems of differential
equations. The behaviour of such systems is the subject of dynamical systems theory …

Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance
based on deep learning. Unique to Neuroscience, deep learning models can be used not …