Predictive information

…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

…projections, which poses a problem for information transfer: how is it possible to preserve information through a synaptic layer that has significantly more inputs than outputs? There are an infinite number of ways to perform compression, and lossy compression suggests efficient yet computation-agnostic methods for reading out input stimuli or activity patterns. However, downstream neurons must perform essential computations on their inputs for the organism’s survival, rather than simply recapitulate input activity. In t

A fundamental property of neurons in the primary visual cortex is their preference for orientation of edges. Recent studies further consolidated the finding that the spatial arrangement of orientation selective neurons in orientation domains and pinwheels is invariant across primates, carnivores and potentially even in some marsupials. No functional advantage favoring this universal common design is currently know. Here we address the question of whether the universal common design can provide a functional advantage over alternative designs. We formulate a mathematical theory formalizing the h

We build upon and expand the efficient coding and predictive information models of neurons, presenting a novel perspective that neurons not only predict but also actively influence their future inputs through their outputs. We introduce the concept of neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing a novel data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers.

…et al., 2022) that seamlessly combines predictive information from shorter and longer contexts of