Abstract
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.
Original language | English |
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Title of host publication | Artificial Life XI: |
Subtitle of host publication | Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems |
Editors | Seth Bullock, Jason Noble, Watson Richard, Mark A. Bedau |
Publisher | MIT Press |
Pages | 569-576 |
Number of pages | 8 |
ISBN (Print) | 978-0-262-28719-7 |
Publication status | Published - 1 Jan 2008 |
Event | Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems - Duration: 1 Jun 2008 → … |
Conference
Conference | Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems |
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Period | 1/06/08 → … |
Keywords
- Evolutionary Robotics