This review article gives a high-level overview of the approaches across different scales of
organization and levels of abstraction. The studies covered in this paper include …

Connectionist models can be characterized within the more general framework of
probabilistic graphical models, which allow to efficiently describe complex statistical …

Understanding the human brain is a “Grand Challenge” for 21st century research.
Computational approaches enable large and complex datasets to be addressed efficiently …

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes
over time due to specific causes, such as stimuli, events, or clinical interventions. Recent …

Artificial neural networks that can recover latent dynamics from recorded neural activity may
provide a powerful avenue for identifying and interpreting the dynamical motifs underlying …

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 …

The extreme complexity of the brain naturally requires mathematical modeling approaches
on a large variety of scales; the spectrum ranges from single neuron dynamics over the …

Many recent studies have employed task-based modeling with recurrent neural networks
(RNNs) to infer the computational function of different brain regions. These models are often …

Modelling the spatiotemporal dynamics in the activity of neural populations while also
enabling their flexible inference is hindered by the complexity and noisiness of neural …

In the past few decades, mathematical models based on dynamical systems theory have
provided new insight into diverse biological systems. In this review, we ask whether the …