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 …

Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

The anatomy of the brain necessarily constrains its function, but precisely how remains
unclear. The classical and dominant paradigm in neuroscience is that neuronal dynamics …

It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear
behaviours. Here we challenge this assumption by leveraging mathematical models derived …

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 recognition of spoken language has typically been studied by focusing on either words
or their constituent elements (for example, low-level features or phonemes). More recently …

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

The brain is composed of complex networks of interacting neurons that express
considerable heterogeneity in their physiology and spiking characteristics. How does this …

Brain function is a product of the balance between excitatory and inhibitory (E/I) brain
activity. Variation in the regulation of this activity is thought to give rise to normal variation in …

Over the past decade, deep neural networks (DNNs) have demonstrated remarkable
performance in a variety of applications. As we try to solve more advanced problems …