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  Homotopy theoretic and categorical models of neural information networks

Manin, Y., & Marcolli, M. (2024). Homotopy theoretic and categorical models of neural information networks. Compositionality, 6: 4. doi:10.46298/compositionality-6-4.

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The articles are distributed under a Creative Commons CC-BY 4.0 license: users are free to copy, distribute, transmit. Authors grant the journal non-exclusive publication rights and retain their own rights without restriction.

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 Creators:
Manin, Yuri1, Author           
Marcolli, Matilde, Author
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1Max Planck Institute for Mathematics, Max Planck Society, ou_3029201              

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Free keywords: Computer Science, Logic in Computer Science, Information Theory, Mathematics
 Abstract: In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure in the form of assignments of resources, either computational or metabolic or informational. The starting point for this construction is the notion of summing functors and of Segal's Gamma-spaces in homotopy theory. The main results in this paper include functorial assignments of concurrent/distributed computing architectures and associated binary codes to networks and their subsystems, a categorical form of the Hopfield network dynamics, which recovers the usual Hopfield equations when applied to a suitable category of weighted codes, a functorial assignment to networks of corresponding information structures and information cohomology, and a cohomological version of integrated information.

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Language(s): eng - English
 Dates: 2024
 Publication Status: Issued
 Pages: 86
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: arXiv: 2006.15136
DOI: 10.46298/compositionality-6-4
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Title: Compositionality
Source Genre: Journal
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Publ. Info: Compositionality Charitable Incorporated Organisation
Pages: - Volume / Issue: 6 Sequence Number: 4 Start / End Page: - Identifier: ISSN: 2631-4444