An invite-only workshop to be held at the santa fe institute, july 10-14, 2023
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.