Modeling the implications of nitric oxide dynamics on information transmission: An automata networks approach

Author:

Fernández-López Pablo1,Báez Patricio García2,Cabrera-León Ylermi1,Procházka Aleš3,Suárez-Araujo Carmen Paz1

Affiliation:

1. Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, Las Palmas de Gran Canaria, 35017, Spain

2. Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Camino San Francisco de Paula, 19, San Cristóbal de La Laguna, 38200, Spain

3. Department of Computing and Control Engineering, Univ. of Chemistry and Technology at Prague & Czech Inst. Inf., Rob. Cyber, Czech Tech Univ. of Prague, 16000 Prague, Czech Republic

Abstract

<abstract><p>Nitric oxide (NO) is already recognized as an important signaling molecule in the brain. It diffuses easily and the nervous cell's membrane is permeable to NO. The information transmission is three-dimensional, which is different from synaptic transmission. NO operates in two different ways: Close and specific at the synapses of neurons, and as a volumetric transmitter sending signals to various targets, regardless of their anatomy, connectivity or function, when multiple nearby sources act simultaneously. These modes of operation seem to be the basis by which NO is involved in many central mechanisms of the brain, such as learning, memory formation, brain development and synaptogenesis. This work focuses on the effect of NO dynamics on the environment through which it diffuses, using automata networks. We study their implications in the formation of complex functional structures in the volume transmission (VT), which are necessary for the synchronous functional recruitment of neuronal populations. We qualitatively and quantitatively analyze the proposed model regarding these characteristics through the concepts of entropy and mutual information. The proposed deterministic model allows the incorporation of fuzzy dynamics. With that, a generalized model based on fuzzy automata networks can be provided. This allows the generation and diffusion processes of NO to be arbitrarily produced and maintained over time. This model can accommodate arbitrary processes in decision-making mechanisms and can be part of a complete formal VT framework in the brain and artificial neural networks.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

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