A unified, scalable framework for neural population decoding
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 …
from greater scale, in terms of both the model size and the datasets. However, the …
Neural data transformer 2: multi-context pretraining for neural spiking activity
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 …
(iBCIs) contain rich structure. Current models of such spiking activity are largely prepared for …
Extraction and recovery of spatio-temporal structure in latent dynamics alignment with diffusion model
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 …
against the drastic domain shift among them. A foundational framework within neuroscience …
Learning time-invariant representations for individual neurons from population dynamics
Neurons can display highly variable dynamics. While such variability presumably supports
the wide range of behaviors generated by the organism, their gene expressions are …
the wide range of behaviors generated by the organism, their gene expressions are …
AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity
Abstract Latent Variable Models (LVMs) propose to model the dynamics of neural
populations by capturing low-dimensional structures that represent features involved in …
populations by capturing low-dimensional structures that represent features involved in …
Frequency-aware masked autoencoders for multimodal pretraining on biosignals
Leveraging multimodal information from biosignals is vital for building a comprehensive
representation of people's physical and mental states. However, multimodal biosignals often …
representation of people's physical and mental states. However, multimodal biosignals often …
Sibblings: Similarity-driven building-block inference using graphs across states
Interpretable methods for extracting meaningful building blocks (BBs) underlying multi-
dimensional time series are vital for discovering valuable insights in complex systems …
dimensional time series are vital for discovering valuable insights in complex systems …
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
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 …
practice, there are often large gaps in their performance. This issue of class bias is widely …
[HTML][HTML] Population Transformer: Learning Population-level Representations of Intracranial Activity
We present a self-supervised framework that learns population-level codes for intracranial
neural recordings at scale, unlocking the benefits of representation learning for a key …
neural recordings at scale, unlocking the benefits of representation learning for a key …
[Re] The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Non-Gaussian Observation Models
Kalman filters provide a straightforward and interpretable means to estimate hidden or latent
variables, and have found numerous applications in control, robotics, signal processing, and …
variables, and have found numerous applications in control, robotics, signal processing, and …