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Topological Adventures in Neuroscience

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Topological Data Analysis

Part of the book series: Abel Symposia ((ABEL,volume 15))

Abstract

This survey consists of a brief overview of recent work at the interface of topology and neuroscience carried out primarily by a collaboration among the Blue Brain Project, the Laboratory for Topology and Neuroscience, and the Laboratory of Neural Microcircuity at the EPFL. The articles surveyed concern the algebraic topology of brain structure and function, and the topological charaterization and classification of neuron morphologies.

The author is grateful to the Blue Brain Project [20] for allowing her team to use their computational resources, which are supported by funding from the ETH Domain and hosted at the Swiss National Supercomputing Center (CSCS).

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Acknowledgements

I would like to thank the local organizers of the 2018 Abel Symposium for a truly marvelous experience: exciting science in one of the most exquisitely beautiful places on Earth.

Without the support of a large team of enthusiastic, creative, hardworking collaborators, with a wide range of skills and knowledge, none of the research presented here could have been realized. I express my great appreciation to all of those who collaborated on the articles surveyed below: Nicolas Antille, Jean-Baptiste Bardin, Giuseppe Chindemi, Paweł Dłotko, Lida Kanari, Ran Levi, Julie Meystre, Sébastien Morand, Max Nolte, Rodrigo Perin, Michael Reimann, Martina Scolamiero, Julian Shillcock, Gard Spreemann, and Kate Turner. I thank as well Daniel Lütgehetmann, whose creation of Flagser enabled us to do the computations that were out of reach when we wrote [27].

Finally, I would like to express my deep gratitude to Henry Markram, for believing that topology could play an important in neuroscience and for providing us with the means necessary to realize our dreams.

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Correspondence to Kathryn Hess .

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Hess, K. (2020). Topological Adventures in Neuroscience. In: Baas, N., Carlsson, G., Quick, G., Szymik, M., Thaule, M. (eds) Topological Data Analysis. Abel Symposia, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-43408-3_11

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