Abstract
The connection patterns of neural circuits in the brain form a complex network. Collective signalling within the network manifests as patterned neural activity and is thought to support human cognition and adaptive behaviour. Recent technological advances permit macroscale reconstructions of biological brain networks. These maps, termed connectomes, display multiple non-random architectural features, including heavy-tailed degree distributions, segregated communities and a densely interconnected core. Yet, how computation and functional specialization emerge from network architecture remains unknown. Here we reconstruct human brain connectomes using in vivo diffusion-weighted imaging and use reservoir computing to implement connectomes as artificial neural networks. We then train these neuromorphic networks to learn a memory-encoding task. We show that biologically realistic neural architectures perform best when they display critical dynamics. We find that performance is driven by network topology and that the modular organization of intrinsic networks is computationally relevant. We observe a prominent interaction between network structure and dynamics throughout, such that the same underlying architecture can support a wide range of memory capacity values as well as different functions (encoding or decoding), depending on the dynamical regime the network is in. This work opens new opportunities to discover how the network organization of the brain optimizes cognitive capacity.
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Data availability
The source dataset from University of Lausanne is available at https://doi.org/10.5281/zenodo.2872624. To facilitate the reproducibility of this work, the data ready to run the code is publicly available on Zenodo (https://doi.org/10.5281/zenodo.4776453).
Code availability
All code used for data processing, simulation, analysis, and figure generation is publicly available on GitHub and Zenodo (https://github.com/netneurolab/suarez_neuromorphicnetworks; https://doi.org/10.5281/zenodo.4776829)172, and is built on top of the following open-source Python packages: reservoir (https://github.com/estefanysuarez/reservoir; https://doi.org/10.5281/zenodo.4913398), Netneurotools (https://github.com/netneurolab/netneurotools), Numpy173,174,175, Scipy176, Pandas177, Scikit-learn178, bctpy (https://github.com/aestrivex/bctpy)89, NetworkX 179, Matplotlib180 and Seaborn181.
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Acknowledgements
We thank R. Markello, B. Vazquez-Rodriguez, G. Shafiei, V. Bazinet, J. Hansen and Z.-Q. Liu for insightful comments on the manuscript. B.M. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN no. 017-04265), from the Canada Research Chairs Program, from the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative, and from the Brain Canada Future Leaders Fund. G.L. acknowledges support from NSERC (Discovery Grant RGPIN-2018-04821), FRQS (Research Scholar Award Junior 1 LAJGU0401-253188), and CIFAR (Canada CIFAR AI Chair). B.A.R. acknowledges support from NSERC (NSERC Discovery Grant RGPIN 2020-05105), Healthy Brains, Healthy Lives (New Investigator Start-up, 2b-NISU-8), and funding from CIFAR (Learning in Machines and Brains Program, Canada CIFAR AI Chair). E.S. acknowledges support from the Fonds de Recherche du Québec—Nature et Technologies (FRQNT) Strategic Clusters Program (2020-RS4-265502—Centre UNIQUE—Union Neurosciences and Artificial Intelligence—Quebec) and the Fonds de Recherche du Québec—Nature et Technologies (FRQNT).
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L.E.S., G.L. and B.M. conceptualized the work. L.E.S. performed the methodology and the formal analysis. L.E.S. and B.M. wrote the original draft, whereas B.A.R. and G.L. reviewed and edited the manuscript. B.M. acquired funding.
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Suárez, L.E., Richards, B.A., Lajoie, G. et al. Learning function from structure in neuromorphic networks. Nat Mach Intell 3, 771–786 (2021). https://doi.org/10.1038/s42256-021-00376-1
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DOI: https://doi.org/10.1038/s42256-021-00376-1
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