5 papers
Deep probabilistic model synthesis enables unified modeling of whole-brain neural activity across individual subjects
William E. Bishop, Luuk W. Hesselink, Bernhard Englitz +2
Many disciplines need quantitative models that synthesize experimental data across multiple instances of the same general system. For example, neuroscientists must combine data fro…
Behavior-dLDS: A decomposed linear dynamical systems model for neural activity partially constrained by behavior
Eva Yezerets, En Yang, Misha B. Ahrens +1
Brain-wide recordings of large-scale networks of neurons now provide an unprecedented view into how the brain drives behavior. However, brain activity contains both information dir…
POCO: Scalable Neural Forecasting through Population Conditioning
Yu Duan, Hamza Tahir Chaudhry, Misha B. Ahrens +4
Predicting future neural activity is a core challenge in modeling brain dynamics, with applications ranging from scientific investigation to closed-loop neurotechnology. While rece…
ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
Jan-Matthis Lueckmann, Alexander Immer, Alex Bo-Yuan Chen +15
Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we introduce the Zebrafish Activity Predi…
Forecasting Whole-Brain Neuronal Activity from Volumetric Video
Alexander Immer, Jan-Matthis Lueckmann, Alex Bo-Yuan Chen +14
Large-scale neuronal activity recordings with fluorescent calcium indicators are increasingly common, yielding high-resolution 2D or 3D videos. Traditional analysis pipelines reduc…