Chapter

Artificial Intelligence and Soft Computing

Volume 7267 of the series Lecture Notes in Computer Science pp 143-151

Spectra of the Spike-Flow Graphs in Geometrically Embedded Neural Networks

  • Jarosław PiersaAffiliated withFaculty of Mathematics and Computer Science, Nicolaus Copernicus University
  • , Tomasz SchreiberAffiliated withFaculty of Mathematics and Computer Science, Nicolaus Copernicus University

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Abstract

In this work we study a simplified model of a neural activity flow in networks, whose connectivity is based on geometrical embedding, rather than being lattices or fully connected graphs. We present numerical results showing that as the spectrum (set of eigenvalues of adjacency matrix) of the resulting activity-based network develops a scale-free dependency. Moreover it strengthens and becomes valid for a wider segment along with the simulation progress, which implies a highly organised structure of the analysed graph.

Keywords

geometric neural networks graph spectrum scale-freeness