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This article is cited in 10 scientific papers (total in 10 papers)
Cognitive modeling
Asynchronous discrete model of chemical interactions in simple neuronal systems
O. P. Kuznetsova, N. Bazenkova, B. A. Boldysheva, L. Yu. Zhilyakovaa, S. G. Kulivetsa, I. A. Chistopolskyb a V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, Russia
b Koltzov Institute of Developmental Biology, Russian Academy of Sciences, Moscow, Russia
Abstract:
An asynchronous discrete model of nonsynaptic chemical interactions between neurons is proposed. The model significantly extends the previous work [1, 2] by novel concepts that make it more biologically plausible. In the model, neurons interact by emitting neurotransmitters to the shared extracellular space (ECS). We introduce dynamics of membrane potentials that comprises two factors: the endogenous rates of change depending on the neuron’s firing type and the exogenous rate of change, depending on the concentrations of neurotransmitters that the neuron is sensitive to. The neuron’s firing type is determined by the individual composition of endogenous rates. We consider three basic firing types: oscillatory, tonic and reactive. Each of them is essential for modeling central pattern generators – neural ensembles generating rhythmic activity in the absence of external stimuli. Differences in endogenous rates of different neurons leads to asynchronous neural interactions and significant variability of phase durations in the activity patterns present in simple neural systems. The algorithm computing the behavior of the proposed model is provided.
Keywords:
asynchronous discrete model, heterogeneous neuronal system, extracellular space, nonsynaptic interactions, neurotransmitters.
Citation:
O. P. Kuznetsov, N. Bazenkov, B. A. Boldyshev, L. Yu. Zhilyakova, S. G. Kulivets, I. A. Chistopolsky, “Asynchronous discrete model of chemical interactions in simple neuronal systems”, Artificial Intelligence and Decision Making, 2018, no. 2, 3–20; Scientific and Technical Information Processing, 45:6 (2018), 375–389
Linking options:
https://www.mathnet.ru/eng/iipr203 https://www.mathnet.ru/eng/iipr/y2018/i2/p3
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