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 …

Latent variable models are ubiquitous in the exploratory analysis of neural population
recordings, where they allow researchers to summarize the activity of large populations of …

Abstract Latent Variable Models (LVMs) propose to model the dynamics of neural
populations by capturing low-dimensional structures that represent features involved in …

A central objective in neuroscience is to understand how the brain orchestrates movement.
Recent advances in automated tracking technologies have made it possible to document …

The complex activity of neural populations in the Prefrontal Cortex (PFC) is a hallmark of
high-order cognitive processes. How these rich cortical dynamics emerge and give rise to …

The advent of large-scale neural recordings has enabled new approaches that aim to
discover the computational mechanisms of neural circuits by understanding the rules that …

In this paper, the authors propose a neural network architecture designed specifically for a
class of Birkhoffian systems—The Newtonian system. The proposed model utilizes recurrent …

Latent factor analysis via dynamical systems (LFADS) is an RNN-based variational
sequential autoencoder that achieves state-of-the-art performance in denoising high …

During delayed ballistic reaches, motor areas consistently display movement-specific activity
patterns prior to movement onset. It is unclear why these patterns arise: while they have …