Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming
This work addresses the problem of automatically discovering biologically plausible synaptic plasticity rules for researchers in computational neuroscience and machine learning, offering an incremental approach to rule generation.
This paper formulates the search for synaptic plasticity models as an optimization problem, using Cartesian genetic programming to evolve human-interpretable plasticity rules. These rules enable a network to solve tasks, such as efficiently determining the first principal component of its input distribution, performing competitively with hand-designed solutions.
We formulate the search for phenomenological models of synaptic plasticity as an optimization problem. We employ Cartesian genetic programming to evolve biologically plausible human-interpretable plasticity rules that allow a given network to successfully solve tasks from specific task families. While our evolving-to-learn approach can be applied to various learning paradigms, here we illustrate its power by evolving plasticity rules that allow a network to efficiently determine the first principal component of its input distribution. We demonstrate that the evolved rules perform competitively with known hand-designed solutions. We explore how the statistical properties of the datasets used during the evolutionary search influences the form of the plasticity rules and discover new rules which are adapted to the structure of the corresponding datasets.