Hormonal computing: a conceptual approach

Abstract

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.

Keywords: bioinspiration; hormonal computing; hormones; programming; sensors; signaling.

FIGURE 1

Hormonal loop schematic (Elmenreich et al., 2021), redesigned by the authors, where the notation is H ^iy hormone for task T _i executed on OPC _y and H _iy hormone from task T _i executed on OPC _y . Latin letters represent task indices, and Greek letters represent processing element indices.

FIGURE 2

Levels of action in humans according to the hormonal mechanisms. According to Volzhenin et al. (2022), hormonal mechanisms in humans operate at different levels of action. These levels include the hypothalamic–pituitary axis, which regulates the release of hormones from the brain’s hypothalamus and pituitary gland, and the endocrine glands located throughout the body. Hormones released by these glands travel through the bloodstream, reaching various target cells and tissues. At the cellular level, hormones interact with specific receptors on target cells, initiating intracellular signaling pathways that lead to various physiological responses. These responses include changes in gene expression, alterations in cellular metabolism, or modifications in the function of specific organs or systems. Understanding the levels of action in hormonal mechanisms is crucial for comprehending how hormones influence different aspects of human physiology and behavior. By studying these mechanisms, researchers can gain insights into the regulation of bodily functions, the maintenance of homeostasis, and the coordination of various physiological processes (Volzhenin et al., 2022).

FIGURE 3

DAG diagram of type 2 diabetes: total serum testosterone at baseline; outcome: incident type 2 diabetes for follow-up; covariates: age, smoking status, waist circumference, and physical activity and sex hormone-binding globulin (SHBG) as an unmeasured variable. Colliders framed: waist measurement and SHBG. Adapted by the authors from Schipf (2011)

FIGURE 4

High-level activity diagram of the main messaging and signaling workflow. (1) DAG network definition: (1a) definition of network nodes; (1b) network axes; (1c) axes’ weights. (2) The life cycle contains the following stages: (2a) propagation of the neuronal signals or spikes; (2b) triggering and activation of nuclei via signals; (2c) propagation of hormonal messaging via bloodstream; (2d) activation of hormonal glands.

FIGURE 5

High-level representation of the proposed neuro-hormonal messaging architecture. (1) Neurons of the nucleus spiking form the inbound signal of the neuro-hormonal gateway; (2) later, the gateway propagates the hormonal message to the gland; (3) the gland, in turn, sends feedback to the gateway; and (4) the gateway sends feedback to the nucleus. Messaging is performed using the fibers.

FIGURE 6

Examples of spike shapes: (A) example of simulated extracellular spike shape; (B) example of sum of simulated extracellular voltages.

FIGURE 7

Hypothalamus–pituitary–thyroid axis (Sharlin, 2015) signaling/messaging and auto-regulation system, where hypothalamus, pituitary, and thyroid glands are groups of nodes and TRH, TSH, and negative feedback are hormonal messaging channels.

FIGURE 8

Example of CPG bus implementation. (A) Invasive implementation of the CPG neuro-hormonal message bus. (B) Hybrid architecture of the CPG neuro-hormonal message bus for the SC neurorehabilitation, where nanocapsules, EES, and the CPG bus are implemented invasively, while limb stimulators are connected to the bus via the transcutaneous port.

FIGURE 9

Complete SCI. High-level design of the hormonal orchestration architecture with neuromorphic neuroprosthesis with CPG bus infrastructure. Teal—CPG model bases computing machines and prostheses. Green—stimulators. Orange—muscles. Blue—input controllers, pressure sensors, and fleximeters.

FIGURE 10

Bluetooth flat antenna of the transcutaneous connections. (A) Typical Bluetooth flat antenna with UMCC (Ultra-Miniature Coax Connector). (B) Hybrid connection schematic where the prosthesis with five OMs manages microstimulators implanted in the muscle tissue via the Bluetooth transmitting antenna under the skin and the receiving antenna is attached to the skin, which is connected to an amplifier and later to a transmitting antenna near the prosthesis receiving antenna.

FIGURE 11

Schematic illustration of defined-shape microcapsule fabrication. (A) Traditional pelmeni production process; (B) PEM microcapsule harvesting in toluene; and (C) PLA microcapsule harvesting in water (Kudryavtseva et al., 2021)