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07-06-2025
Connectomes inform function: from time-varying dynamics to animal behaviour
Authors: Jacob Morra, Kaitlyn Fouke, Eva A. Naumann, Mark Daley
Published in: Natural Computing
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Abstract
The article explores the complex interplay between brain structure and function, focusing on how connectomes—detailed maps of neural connections—can inform our understanding of behavior and cognitive processes. It highlights the use of connectome-constrained artificial neural networks (ANNs) to capture the nuances of this relationship, leveraging biologically-derived, network-constrained models to enhance machine learning practices and neuroscientific research. The studies presented involve a range of animal models, including adult fruit flies and larval zebrafish, and demonstrate the superior performance of connectome-constrained models in tasks such as chaotic time-series prediction, multifunctionality, and behavior tracking. Key findings include the non-trivial advantages of explicit connectome topology, the importance of global clustering coefficients, and the robustness of connectome-constrained networks to parameter changes. The article also discusses the impact of rewiring and sparsity on network performance, providing a detailed analysis of how specific topological features contribute to the observed advantages. Overall, the research underscores the potential of connectome-constrained ANNs in both biological and computational contexts, offering a deeper understanding of the intrinsic features and limitations of neural networks.
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Abstract
Structure guides computation in biological and artificial neural networks. However, the nature of the relationship between structure and function, in this context, is unclear. For example, there is still debate on whether constraining a network with biological detail confers a non-trivial functional advantage over a network without such constraints. To shine light on this topic, we highlight five experiments which employ biological constraints onto artificial neural networks using empirically-guided wiring diagrams, or connectomes, from an adult fruit fly, a larval zebrafish, and from the Mammalian MRI (MaMI) dataset, and impose these onto reservoir-based recurrent neural networks, while studying changes in performance and prediction dynamics on synthetic and naturalistic time series data. We observe that fly-constrained networks are better at making predictions from chaotic input data, and in executing multiple mutually exclusive tasks simultaneously, all with a robustness to hyperparameter variations, some of which may lead to chaos. Separately, we find that the global clustering coefficient of the fly network improves performance and variance on time-varying predictions. We also report that an empirical functional connectome from the optomotor response circuitry of a larval zebrafish validates its own behaviour, and that this is interrupted by rewiring. Finally, using the MaMI dataset, we determine that rewiring degrades multifunctional capacity, and that more multifunctional networks have a higher mean degree centrality. Collectively, these findings suggest that biological topology constraints confer distinct advantages to arbitrarily-weighted networks.
