Topic: Latent variable models

Workshop bringing together researchers working on AI for learning dynamics from data, with a special focus on information-theoretic methods. Topics include predictive information, the information bottleneck, latent-variable models, entropy and mutual-information estimation, world models of biological systems, and connections to dynamical systems and control theory. The workshop is part of the six-week NITMB program “Image-Based Scientific Machine Learning for Theories of Biological Dynamics Across Scales” (February 1–March 12, 2027). Program: AI in Biological Dynamics Scientific Focus

A one-year machine-learning research internship with the Allen Institute Neural Dynamics accelerator. The intern will analyze recordings from two genetically distinct neural populations in the striatum alongside motor output, muscle activity and behavioral video. The project asks how shared and population-specific neural activity encodes movement, using latent-variable models, regression, classification and cross-validation. Work includes reproducible Python analysis and communicating results through figures and methods that may contribute to a poster or publication. No previous neuroscience b

Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While latent variable models can successfully extract low-dimensional embeddings from such recordings, using them to generate realistic spiking data, especially in a behavior-dependent manner, still poses a challenge. Here, we present Latent Diffusion for Neural Spiking data (LDNS), a diffusion-based generative model with a low-dimensional latent space: LDNS employs an autoencoder with structured state-space (S4) layers to pro