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Exploring Brain Information Storage/Reading for Neuronal Connectivity Using Macromolecular Electrochemical Sensing Motors
Author(s) -
Otero Toribio Fernández
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202100058
Subject(s) - computer science , macromolecule , electrochemistry , nanotechnology , ionic bonding , decipher , channel (broadcasting) , molecular machine , biological system , ion , materials science , chemistry , electrode , bioinformatics , biology , telecommunications , biochemistry , organic chemistry
Dense electroactive gels of conducting polymers, carbon nanotubes, or graphenes, constituted by multisensing macromolecular electrochemical motors, are presented herein as model materials to get a physicochemical characterization of the opening/closing cycles of ion protein channels in neurons. The conformational contracted energetic states of these macromolecular motors store both long‐term and short‐term information packages. Under a potential cycle, the energy of each ionic pulse generated during the channel opening reads the stored information, which is restored during the channel conformational closing. Simultaneously the motor actuation senses the working physical and chemical energetic conditions, transferring to the ionic pulse energy those sensing information packages. A quantitative description of the stored and read information packages is attained from basic electrochemical, mechanical, and polymeric concepts. Translated to ion channel proteins in brain dendrites, it can describe brain information storage mechanisms and the energetic information packages read and carried through each ionic pulse to the action potential. An unexplored way is opened to try to understand and describe how the different brain parts can use this information to originate brain functions. In parallel, the construction of new electrochemical devices and computers replicating biological functions will provide feedback quantitative information to decipher brain functions.

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