Engineers try to predict future input from past input; this can take the form of prediction of natural video, natural audio, or text, which has famously led to such products as Generative Pre-trained Transformer 3 (GTP3) and proprietary algorithms for stock market prediction. Organisms and parts of organisms may have evolved to efficiently predict their input as well, and the hypothesis that they do are cornerstones of theoretical neuroscience and theoretical biology. How one can design systems to predict input is still a matter of debate, especially when one has continuous-time input—input that has a state at every point in time, not just at specially sampled points. We aim to bring together researchers that approach the question of how to design systems to predict input through the lens of biology with machine learning, information theory, and dynamical systems. This knowledge will help establish a foundation of theoretical neuroscience and theoretical biology, to enable the scientific community to better calibrate and understand prediction products.
 
We hope that this workshop will cover a wide range of topics.  Some participants will talk about examine evolved systems, including the study of neurons in the retina, hippocampus, and the visual cortex. Some participants will discuss engineering systems to better predict input through reservoir computing and training recurrent neural networks, in which reservoir weights are trained as well.

An agenda is here.

This workshop is graciously sponsored by the National Science Foundation, the Santa Fe Institute, the W. M. Keck Science Department, and Awecom.

A list of confirmed participants:

• Sarah Marzen, W. M. Keck Science Department, Pitzer, Scripps, and Claremont McKenna College, and co-organizer
• Jim Crutchfield, U. C. Davis and co-organizer
• David Pfau, Google DeepMind
• Nicolas Brodu, INRIA
• Adam Rupe, Los Alamos National Laboratory
• Alexandra Jurgens, INRIA
• Marc Howard, Boston University
• Steve Presse, Arizona State University
• Nicol Harper, University of Oxford
• Chris Hillar, Awecom, Inc.
• Bryan Daniels, Arizona State University
• Erik Bollt, Clarkson
• Antonio Carlos Costa, Ecole Normale Supérieure
• Guillaume Pourcel, University of Groningen
• Manuel Beiran, Columbia University
• Jared Salisbury, University of Chicago
• Andre Bastos, Vanderbilt
• Bruno Olshausen, University of California, Berkeley
• Kelly Finn, Dartmouth
• Alex Beltsen, University of California, Berkeley

​Please submit to the Special Issue on Sensory Prediction in Computational Brain and Behavior! You can submit even if you didn't attend this workshop. More details coming soon.