Podcast with Randall Beer on dynamical systems and information theory

Is the brain a dynamical system, an information processor, or a prediction machine , and does it even matter which label we choose? Computational scientist Randall Beer argues that these are not competing theories but complementary mathematical lenses, and that real progress requires building theory around carefully analyzed toy models rather than debating metaphors. Subscribe for more from the Convergent Science Network podcast series. Randall Beer joins Paul Verschure and Tony Prescott at the BCBT summer school to present his approach to understanding brain, body, and environment as coupled dynamical systems. Beer makes a sharp epistemological argument: statements like "the brain is a dynamical system" or "the brain is an information processor" are not testable theories but pre-theoretical intuitions, each backed by a body of mathematics that serves as a lens for examining neural systems. No experiment could definitively prove or disprove any of them. What matters is the utility of each lens for generating insight, and Beer advocates maintaining a toolkit of multiple mathematical languages rather than committing to any single framework. The discussion centers on Beer's detailed analysis of a minimal agent performing relational categorization , distinguishing the relative size of two falling objects. Using both dynamical systems theory and information theory applied to the same evolved neural controller, Beer demonstrates that each lens reveals complementary features invisible to the other. Dynamical analysis highlights bifurcations, transient manifolds, and the role of sensor discontinuities, while information-theoretic analysis reveals which combinations of system elements carry the most relevant information at each moment. The invariant pattern across many evolved solutions is a transient manifold that gets spread into a sheet and then sliced by a bifurcation into a decision. Key topics include why brain-body-environment should be the unit of analysis rather than the brain alone, how toy models in the tradition of Galileo's frictionless planes can build fundamental theory, what the difference is between ontological and epistemological claims about neural computation, why dynamical systems theory and information theory are complementary rather than competing, and how Beer plans to extend these analytical tools to the biological nervous system of C. elegans. Part of the Convergent Science Network podcast series from the BCBT Summer School.