Self-Motivated Growing Neural Network for Adaptive Architecture via Local Structural Plasticity

Abstract:Control policies are often implemented with fixed-capacity multilayer perceptrons trained by backpropagation, which require architecture selection in advance and cannot adapt their capacity during learning. This paper introduces the Self-Motivated Growing Neural Network (SMGrNN), a gradient-trained controller whose topology evolves online through a local Structural Plasticity Module (SPM). The SPM monitors edge-wise weight update statistics over short temporal windows and uses these local signals to trigger neuron insertion and pruning, while synaptic weights are optimized by a standard gradient-based optimizer. This allows network capacity to be adjusted during learning without manual architectural tuning. SMGrNN is evaluated on control benchmarks via policy distillation. Compared with multilayer perceptron baselines, it achieves similar or higher returns, lower variance, and task-appropriate network sizes. Ablation studies with growth disabled and growth-only variants isolate the role of structural plasticity, showing that adaptive growth improves reward stability while pruning prevents uncontrolled expansion and supports compact network formation. These results establish the independent value of local structural plasticity within gradient-trained networks and motivate future investigation of whether similar structural rules can be extended to more local or spike-based learning settings.

Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)

arXiv:2512.12713 [cs.NE]

(or arXiv:2512.12713v2 [cs.NE] for this version)

https://doi.org/10.48550/arXiv.2512.12713 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.