A neural network model for the evolution of learning in changing environments.

ABSTRACT: Learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex neural network models often make biologically unrealistic assumptions and rarely consider evolutionary questions. Here, we present a novel way of modelling learning using small neural networks and a simple, biology-inspired learning algorithm. Learning affects only part of the network, and it is governed by the difference between expectations and reality. We use this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging).

SUBMITTER: Kozielska M 

PROVIDER: S-EPMC10857588 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature