ArticleNetwork neuroscience (Cambridge, Mass.)2022
An application of neighbourhoods in digraphs to the classification of binary dynamics.
Article in Network neuroscience (Cambridge, Mass.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes.iScience · 2025Article
- Neighbourhood topology unveils pathological hubs in the brain networks of epilepsy-surgery patients.Brain communications · 2025Article
- Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies.PloS one · 2022Article
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6 authors.
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Abstract
A binary state on a graph means an assignment of binary values to its vertices. A time-dependent sequence of binary states is referred to as binary dynamics. We describe a method for the classification of binary dynamics of digraphs, using particular choices of closed neighbourhoods. Our motivation and application comes from neuroscience, where a directed graph is an abstraction of neurons and their connections, and where the simplification of large amounts of data is key to any computation. We present a topological/graph theoretic method for extracting information out of binary dynamics on a graph, based on a selection of a relatively small number of vertices and their neighbourhoods. We consider existing and introduce new real-valued functions on closed neighbourhoods, comparing them by their ability to accurately classify different binary dynamics. We describe a classification algorithm that uses two parameters and sets up a machine learning pipeline. We demonstrate the effectiveness of the method on simulated activity on a digital reconstruction of cortical tissue of a rat, and on a nonbiological random graph with similar density.
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