# Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection

arXiv: https://arxiv.org/abs/2311.05862 · HTML: https://mlsanity.com/paper/2311.05862/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2023.

## Built on by (most cited first, 6 of 6)

- [IP2-RSNN: Bi-level Intrinsic Plasticity Enables Learning-to-learn in Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2501.14539/) (2025)
- [Association-sensory spatiotemporal hierarchy and functional gradient-regularised recurrent neural network with implications for schizophrenia](https://mlsanity.com/paper/2511.02722/) (2025)
- [Modular connectivity in neural networks emerges from Poisson noise-motivated regularisation, and promotes robustness and compositional generalisation](https://mlsanity.com/paper/2512.13707/) (2025)
- [When Does Structure Matter in Continual Learning? Dimensionality Controls When Modularity Shapes Representational Geometry](https://mlsanity.com/paper/2604.27656/) (2026)
- [Unifying Dynamical Systems and Graph Theory to Mechanistically Understand Computation in Neural Networks](https://mlsanity.com/paper/2605.03598/) (2026)
- [Dimensionality Controls When Modularity Helps in Continual Learning](https://mlsanity.com/paper/2606.17889/) (2026)

## Abstract (arXiv)

While advances in artificial intelligence and neuroscience have enabled the emergence of neural networks capable of learning a wide variety of tasks, our understanding of the temporal dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian Feedforward (HebbFF) neural networks in tasks of continual familiarity detection. Drawing inspiration from the field of network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. In particular, we find that the emergence of network modularity is a salient predictor of performance, and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological realms.