Our ability to use deep learning approaches to decipher neural activity would likely benefit
from greater scale, in terms of both the model size and the datasets. However, the …

The neural population spiking activity recorded by intracortical brain-computer interfaces
(iBCIs) contain rich structure. Current models of such spiking activity are largely prepared for …

In the field of behavior-related brain computation, it is necessary to align raw neural signals
against the drastic domain shift among them. A foundational framework within neuroscience …

Neurons can display highly variable dynamics. While such variability presumably supports
the wide range of behaviors generated by the organism, their gene expressions are …

Understanding the neural basis of behavior is a fundamental goal in neuroscience. Current
research in large-scale neuro-behavioral data analysis often relies on decoding models …

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

Leveraging multimodal information from biosignals is vital for building a comprehensive
representation of people's physical and mental states. However, multimodal biosignals often …

Neuroscience research has made immense progress over the last decade, but our
understanding of the brain remains fragmented and piecemeal: the dream of probing an …

Interpretable methods for extracting meaningful building blocks (BBs) underlying multi-
dimensional time series are vital for discovering valuable insights in complex systems …

Classification models are expected to perform equally well for different classes, yet in
practice, there are often large gaps in their performance. This issue of class bias is widely …