Time-Modulated Hebbian Learning for Classic Control Tasks
Year: 2025
Type: conference-paper
Abstract: Artificial neural networks (ANNs) commonly rely on fixed-weight architectures, limiting their adaptability in dynamic and uncertain environments. In contrast, biological neural systems exhibit plasticity, enabling continuous adaptation to changing conditions. Hebbian learning provides a mechanism for incorporating plasticity into ANNs, offering the potential for improved flexibility in real-world control tasks. However, Hebbian-based models often suffer from instability, making their practical application challenging. This work explores the trade-off between adaptability and stability in Hebbian learning-driven ANNs by introducing a neuro-modulatory term into the standard Hebbian learning formulation, enabling a gradual transition from high plasticity to fixed weights over time. The proposed model, named time-modulated Hebbian learning (tm-HL), was tested on three classical control tasks from the Gymnasium environment, and it was compared to a standard Hebbian learning model and a fixed-weight ANN. An evolutionary algorithm was employed to optimise the Hebbian parameters for the former and the weights of the latter. The proposed approach showed a promising performance, showing higher adaptability over the model with fixed weights in certain scenarios. Moreover, it consistently performed better than the standard Hebbian learning model in all the testing environments.
Institution: National University of Ireland, Maynooth
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413209838