TY - JOUR
T1 - On irregular behavior of neuron spike trains
AU - Shahverdian, A. Yu
AU - Apkarian, A. V.
N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 1999/3
Y1 - 1999/3
N2 - 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.
AB - 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.
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U2 - 10.1142/S0218348X99000116
DO - 10.1142/S0218348X99000116
M3 - Article
AN - SCOPUS:0347669606
VL - 7
SP - 93
EP - 103
JO - Fractals
JF - Fractals
SN - 0218-348X
IS - 1
ER -