  Guillain-Barre syndrome is a rare neurological condition in which the human
immune system attacks the peripheral nervous system. A peripheral nervous
system appears as a diffusively connected system of mathematical models of
neuron models, and the system's period becomes shorter than the periods of each
neural circuit. The stimuli in the conduction path that will address the myelin
sheath that has lost its function are received by the axons and are conveyed
externally to the target organ, aiming to solve the problem of decreased nerve
conduction. In the NEURON simulation environment, one can create a neuron model
and define biophysical events that take place within the system for study. In
this environment, signal transmission between cells and dendrites is obtained
graphically. The simulated potassium and sodium conductance are replicated
adequately, and the electronic action potentials are quite comparable to those
measured experimentally. In this work, we propose an analog and digital coupled
neuron model comprising individual excitatory and inhibitory neural circuit
blocks for a low-cost and energy-efficient system. Compared to digital design,
our analog design performs in lower frequency but gives a 32.3\% decreased
energy efficiency. Thus, the resulting coupled analog hardware neuron model can
be a proposed model for the simulation of reduced nerve conduction. As a
result, the analog coupled neuron, (even with its greater design complexity)
serious contender for the future development of a wearable sensor device that
could help with Guillain-Barre syndrome and other neurologic diseases.
