What can topology tell us about the neural code?
Not on the map. Why?
Abstract (arXiv)
Neuroscience is undergoing a period of rapid experimental progress and expansion. New mathematical tools, previously unknown in the neuroscience community, are now being used to tackle fundamental questions and analyze emerging data sets. Consistent with this trend, the last decade has seen an uptick in the use of topological ideas and methods in neuroscience. In this talk I will survey recent applications of topology in neuroscience, and explain why topology is an especially natural tool for understanding neural codes. Note: This is a write-up of my talk for the Current Events Bulletin, held at the 2016 Joint Math Meetings in Seattle, WA.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 May 2016.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 2.
6 papers.
- 2022Learnable latent embeddings for joint behavioral and neural analysiscited by 113
- 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
- 2021Topological Measurement of Deep Neural Networks Using Persistent Homologycited by 9
- 2021Topological Deep Learning: Classification Neural Networkscited by 5
- 2022Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensemblescited by 3
- 2024Dirac-Equation Signal Processing: Physics Boosts Topological Machine Learningcited by 1
- 2024Dirac-Equation Signal Processing: Physics Boosts Topological Machine Learning6 Dec 2024
- 2024Position: Topological Deep Learning is the New Frontier for Relational Learning14 Feb 2024
- 2022Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles6 Oct 2022
- 2022Learnable latent embeddings for joint behavioral and neural analysis1 Apr 2022
- 2021Topological Measurement of Deep Neural Networks Using Persistent Homology6 Jun 2021
- 2021Topological Deep Learning: Classification Neural Networks16 Feb 2021