Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios
conference contributionposted on 2015-03-18, 13:20 authored by Andrea SoltoggioAndrea Soltoggio, John A. Bullinaria, Claudio Mattiussi, Peter Durr, Dario Floreano
Neuromodulation is considered a key factor for learning and memory in biological neural networks. Similarly, artificial neural networks could benefit from modulatory dynamics when facing certain types of learning problem. Here we test this hypothesis by introducing modulatory neurons to enhance or dampen neural plasticity at target neural nodes. Simulated evolution is employed to design neural control networks for T-maze learning problems, using both standard and modulatory neurons. The results show that experiments where modulatory neurons are enabled achieve better learning in comparison to those where modulatory neurons are disabled. We conclude that modulatory neurons evolve autonomously in the proposed learning tasks, allowing for increased learning and memory capabilities.
- Computer Science
Published inArtificial Life XI: Proceedings of the 11th International Conference on the Simulation and Synthesis of Living Systems, ALIFE 2008
Pages569 - 576
CitationSOLTOGGIO, A. ... et al., 2008. Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios. IN: Artificial Life XI: Proceedings of the 11th International Conference on the Simulation and Synthesis of Living Systems (ALIFE 2008). MIT Press. pp. 569 - 576
- AM (Accepted Manuscript)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
NotesThis is a conference paper.