Hormonal computing : a conceptual approach
Vallverdú, Jordi 1973-
![Identificador ORCID](/img/uab/orcid.ico)
(Universitat Autònoma de Barcelona)
Talanov, Max ![Identificador ORCID](/img/uab/orcid.ico)
(Institute for Artificial Intelligence R&D. Kazan Volga Region Federal University)
Leukhin, Alexey (Kazan Volga Region Federal University. B-Rain Labs LLC)
Fatykhova, Elsa (Children's Republican Clinical Hospital)
Erokhin, Victor (Istituto dei Materiali per l'Elettronica ed il Magnetismo, National Research Council (Itàlia))
Data: |
2023 |
Resum: |
This paper provides a conceptual roadmap for the use of hormonal bioinspired models in a broad range of AI, neuroengineering, or computational systems. The functional signaling nature of hormones provides an example of a reliable multidimensional information management system that can solve parallel multitasks. Two existing examples of hormonal computing bioinspired possibilities are shortly reviewed, and two novel approaches are introduced, with a special emphasis on what researchers propose as hormonal computing for neurorehabilitation in patients with complete spinal cord injuries. They extend the use of epidural electrical stimulation (EES) by applying sequential stimulations to limbs through prostheses. The prostheses include various limb models and are connected to a neurostimulation bus called the central pattern generator (CPG). The CPG bus utilizes hormonal computing principles to coordinate the stimulation of the spinal cord and muscles. |
Drets: |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. ![Creative Commons](/img/licenses/by.ico) |
Llengua: |
Anglès |
Document: |
Article ; recerca ; Versió publicada |
Matèria: |
Bioinspiration ;
Hormonal computing ;
Hormones ;
Programming ;
Sensors ;
Signaling |
Publicat a: |
Frontiers in chemistry, Vol. 11 (2023) , art. 1232949, ISSN 2296-2646 |
DOI: 10.3389/fchem.2023.1232949
PMID: 37663143
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