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 …

A body of recent work in modeling neural activity focuses on recovering low-dimensional
latent features that capture the statistical structure of large-scale neural populations. Most …

Latent dynamics models have emerged as powerful tools for modeling and interpreting
neural population activity. Recently, there has been a focus on incorporating simultaneously …

Understanding the dynamical transformation of neural activity to behavior requires modeling
this transformation while both dissecting its potential nonlinearities and dissociating and …

Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or
inputs from other brain regions. To avoid misinterpreting temporally structured inputs as …

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 …

An important problem in systems neuroscience is to identify the latent dynamics underlying
neural population activity. Here we address this problem by introducing a low-dimensional …

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 …

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 …

An influential framework within systems neuroscience posits that neural computations can
be understood in terms of low-dimensional dynamics in recurrent circuits. A number of …