KLI Colloquia are invited research talks of about an hour followed by 30 min discussion. The talks are held in English, open to the public, and offered in hybrid format.
Fall-Winter 2025-2026 KLI Colloquium Series
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https://us02web.zoom.us/j/5881861923?omn=85945744831
Meeting ID: 588 186 1923
25 Sept 2025 (Thurs) 3-4:30 PM CET
A Dynamic Canvas Model of Butterfly and Moth Color Patterns
Richard Gawne (Nevada State Museum)
14 Oct 2025 (Tues) 3-4:30 PM CET
Vienna, the Laboratory of Modernity
Richard Cockett (The Economist)
23 Oct 2025 (Thurs) 3-4:30 PM CET
How Darwinian is Darwinian Enough? The Case of Evolution and the Origins of Life
Ludo Schoenmakers (KLI)
6 Nov (Thurs) 3-4:30 PM CET
Common Knowledge Considered as Cause and Effect of Behavioral Modernity
Ronald Planer (University of Wollongong)
20 Nov (Thurs) 3-4:30 PM CET
Rates of Evolution, Time Scaling, and the Decoupling of Micro- and Macroevolution
Thomas Hansen (University of Oslo)
4 Dec (Thurs) 3-4:30 PM CET
Chance, Necessity, and the Evolution of Evolvability
Cristina Villegas (KLI)
8 Jan 2026 (Thurs) 3-4:30 PM CET
Embodied Rationality: Normative and Evolutionary Foundations
Enrico Petracca (KLI)
15 Jan 2026 (Thurs) 3-4:30 PM CET
On Experimental Models of Developmental Plasticity and Evolutionary Novelty
Patricia Beldade (Lisbon University)
29 Jan 2026 (Thurs) 3-4:30 PM CET
Jan Baedke (Ruhr University Bochum)
Event Details

Topic description / abstract:
Both evolution and learning are known to produce (sometimes spectacular) adaptive solutions. One can rightfully ask whether these processes might share some common features, and whether they can help each other, possibly in the form of one being a "subroutine" in the other and vice versa.
Learning in evolution: Recent models inform us that ecosystem evolution and evolution of genetic regulatory networks (so important in development) can partly be best understood as learning processes. Features like Hebbian change in coupling terms, memory capacity, forgetting and graceful degradation all come into play. These investigations are complemented by the proposals that the Bayesian update rule is analogous to the discrete-time replicator equation and that evolving replicator populations can learn about grammatical classes. I shall give examples of these processes.
Evolution in learning: This is the flip side of the coin. The idea that something like evolution by natural selection might go on in the brain is not new. Neurobiology saw some eminent attempts to validate this claim, but previous proposals are merely selectionist rather than truly evolutionary. In order to advance in the latter direction one must demonstrate some form of replication, even though neurons do not reproduce. I shall discuss how this might work.
Biographical note:
Eörs Szathmáry (1959) is a Hungarian theoretical and evolutionary biologist, best known for his continued work on the comparative and theoretical aspects of the major evolutionary transitions. The theme was set by a book that he published together with the late John Maynard Smith in 1995. This monograph and the subsequent popular book have were published in a dozen countries. Google finds about 150 thousand hits for the “major transition” AND “evolution”. In addition, Szathmáry studies replicator theory, the relationship between learning and evolution, the question of minimal life and the conditions for open-ended evolution. He is a member of the Hungarian Academy of Sciences, EMBO, the Norwegian Academy of Sciences and Letters as well as Academia Europaea.