Involve: A Journal of Mathematics
- Volume 12, Number 5 (2019), 737-754.
Sparse neural codes and convexity
Determining how the brain stores information is one of the most pressing problems in neuroscience. In many instances, the collection of stimuli for a given neuron can be modeled by a convex set in . Combinatorial objects known as neural codes can then be used to extract features of the space covered by these convex regions. We apply results from convex geometry to determine which neural codes can be realized by arrangements of open convex sets. We restrict our attention primarily to sparse codes in low dimensions. We find that intersection-completeness characterizes realizable 2-sparse codes, and show that any realizable 2-sparse code has embedding dimension at most . Furthermore, we prove that in and , realizations of 2-sparse codes using closed sets are equivalent to those with open sets, and this allows us to provide some preliminary results on distinguishing which 2-sparse codes have embedding dimension at most .
Involve, Volume 12, Number 5 (2019), 737-754.
Received: 26 October 2017
Revised: 24 October 2018
Accepted: 5 December 2018
First available in Project Euclid: 29 May 2019
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Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 05C62: Graph representations (geometric and intersection representations, etc.) For graph drawing, see also 68R10 52A10: Convex sets in 2 dimensions (including convex curves) [See also 53A04] 92B20: Neural networks, artificial life and related topics [See also 68T05, 82C32, 94Cxx]
Jeffs, R. Amzi; Omar, Mohamed; Suaysom, Natchanon; Wachtel, Aleina; Youngs, Nora. Sparse neural codes and convexity. Involve 12 (2019), no. 5, 737--754. doi:10.2140/involve.2019.12.737. https://projecteuclid.org/euclid.involve/1559095402