15 papers
Learning Biophysical Models of Large-Scale Multineuronal Data to Enable Precise Neurostimulation
Amrith Lotlikar, Ian Christopher Tanoh, Praful Vasireddy +9
Multi-compartment Hodgkin-Huxley (HH) models provide a principled framework for predicting neural dynamics and responses to electrical stimulation. However, fitting HH biophysical…
Closing the Approximation Gap in Simulation-free Latent SDEs
Henry D. Smith, Brian L. Trippe, Scott W. Linderman
Recovering dynamical systems from noisy observations is a recurring challenge across scientific domains, including neuroscience and physics. Latent stochastic differential equation…
A cross-species neural foundation model for end-to-end speech decoding
Yizi Zhang, Linyang He, Chaofei Fan +9
Speech brain-computer interfaces (BCIs) aim to restore communication for people with paralysis by translating neural activity into text. Most systems use cascaded frameworks that d…
A Unifying Framework for Parallelizing Sequential Models with Linear Dynamical Systems
Xavier Gonzalez, E. Kelly Buchanan, Hyun Dong Lee +6
Harnessing parallelism in seemingly sequential models is a central challenge for modern machine learning. Several approaches have been proposed for evaluating sequential processes…
Predictability Enables Parallelization of Nonlinear State Space Models
Xavier Gonzalez, Leo Kozachkov, David M. Zoltowski +2
The rise of parallel computing hardware has made it increasingly important to understand which nonlinear state space models can be efficiently parallelized. Recent advances like DE…
An Information Theoretic Perspective on Agentic System Design
Shizhe He, Avanika Narayan, Ishan S. Khare +3
Agentic language model (LM) systems power modern applications like "Deep Research" and "Claude Code," and leverage multi-LM architectures to overcome context limitations. Beneath t…