On irregular behavior of neuron spike trains

A. Yu Shahverdian*, A. V. Apkarian

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


The computational analysis of neuron spike trains shows that the changes in monotony of interspike interval values can be described by a special type of real numbers. As a result of such an arithmetical approach, we establish the presence of chaos in neuron spike trains and arrive at the conclusion that in stationary conditions, brain activity is found asymptotically close to a multidimensional Cantor space with zero Lebesgue measure, which can be understood as the brain activity attractor. The self-affinity, power law dependence, and computational complexity of neuron spike trains are also briefly examined and discussed.

Original languageEnglish
Pages (from-to)93-103
Number of pages11
Issue number1
StatePublished - Mar 1999

ASJC Scopus subject areas

  • Modeling and Simulation
  • Geometry and Topology
  • Applied Mathematics

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