How ecosystems and gene regulatory networks can learn?
Invited
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.
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.
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.
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Presenters
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Eörs Szathmáry
Centre for Ecological Research, Hungary
Authors
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Eörs Szathmáry
Centre for Ecological Research, Hungary