Simon Musall — accepted AI-conference papers

2026

Coupled Transformer Autoencoder for Disentangling Multi-Region Neural Latent Dynamics

Ram Dyuthi Sristi, Sowmya Manojna Narasimha, Jingya Huang, Alice Despatin, Simon Musall, Vikash Gilja +1

ICLR 2026poster

Simultaneous recordings from thousands of neurons across multiple brain areas reveal rich mixtures of activity that are shared between regions and dynamics that are unique to each region. Existing alignment or multi-view methods neglect temporal structure, whereas dynamical latent-variable models ca…

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2024

A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings

Michael Scholkemper, Damin Kühn, Gerion Nabbefeld, Simon Musall, Björn Kampa, Michael T. Schaub

ICASSP 2024accepted

Distance measures between graphs are important primitives for a variety of learning tasks. In this work, we describe an unsupervised, optimal transport based approach to define a distance between graphs. Our idea is to derive representations of graphs as Gaussian mixture models, fitted to distributi…

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2019

BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos

Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall +7

NeurIPS 2019poster

A fundamental goal of systems neuroscience is to understand the relationship between neural activity and behavior. Behavior has traditionally been characterized by low-dimensional, task-related variables such as movement speed or response times. More recently, there has been a growing interest in au…

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