Decoding of Neural Data Using Cohomological Feature Extraction

DOI10.1162/neco_a_01150zbMath1471.92034arXiv1711.07205OpenAlexW2891486278WikidataQ93271625 ScholiaQ93271625MaRDI QIDQ3379578

Erik Rybakken, Benjamin Dunn, Nils A. Baas

Publication date: 27 September 2021

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.07205

zbMATH Keywords

cohomological feature extractiondecode features in neural codelarge population neural recordings

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (5)

Networks beyond pairwise interactions: structure and dynamics ⋮ Higher-Order Description of Brain Function ⋮ Sparse Circular Coordinates via Principal $$\mathbb {Z}$$-Bundles ⋮ Computing the interleaving distance is NP-hard ⋮ Persistent Cohomology for Data With Multicomponent Heterogeneous Information

Uses Software

• SciPy
• GitHub
• Ripser
• CliqueTop

Cites Work

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• Persistent cohomology and circular coordinates
• Cohomological learning of periodic motion
• Clique topology reveals intrinsic geometric structure in neural correlations
• The Structure and Stability of Persistence Modules
• Topology and data
• Approximate Methods for State-Space Models
• Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains
• Barcodes: The persistent topology of data

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