# A Coupled Neural Circuit Design for Guillain-Barre Syndrome

Oguzhan Derebasi<EMAIL_ADDRESS>Turkish-German
UniversityIstanbulTurkey34000 ,  Murat Isik<EMAIL_ADDRESS>Drexel
UniversityPhiladelphiaUSA19104 ,  Oguzhan Demirag<EMAIL_ADDRESS>Pamukkale UniversityDenizliTurkey20010 ,  Dilek Göksel Duru
<EMAIL_ADDRESS>Turkish-German UniversityIstanbulTurkey34000 and  Anup
Das<EMAIL_ADDRESS>Drexel UniversityPhiladelphiaUSA19104

###### Abstract.

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
conveying 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 takes place within
the system for study. In this environment, signal transmission between cells
and dendrites are 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 of 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
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.

Guillain-Barre, Simulation, Neuron Modelling, Analog Design, Digital Design

††conference: X; X; ††ccs: Hardware Neural systems

##  1\. Introduction

Nervous system cells, which are the basic cells of the central nervous system,
are called neurons. The human brain consists of about 86 billion neurons,
whose primary role is to regulate all functions necessary for life using
chemical and electrical signals (Azevedo et al., 2009). Each neuron is
connected densely to 50,000-250,000 other neurons, which are consisting of
three types of neurons namely the afferent neuron, the efferent neuron and the
interneuron. The electrical stimulation of the neurons occurs through the
firing of the action potential, which is a rapid rise and fall of membrane
potential. The action potential is generated when the membrane potential
exceeds a given threshold value. The number of Na+ and Cl- ions is higher in
the extracellular environment than in the intracellular environment, where the
situation is the opposite for K+ ions.

Myelin, which was coined by Rudolf Virchow in 1854, is the sheath that
surrounds and protects the axons; 40% of its mass consists of water, and the
remainder is made up of 75% lipid and 25% protein. Along with its protective
function, it also accelerates the transmission of and prevents the loss of
impulses. It consists of the membrane of Schwann cells in the peripheral
nervous system and the membrane of oligodendrocyte cells in the central
nervous system. In cases, where myelin is damaged, a demyelinating disease is
observed [27, Figure 1]. Depending on the severity of demyelination,
conduction may slow down or stop completely (Ironside, 1997; Halliday and
McDonald, 1977). Guillain-Barre Syndrome is an autoimmune neurological
condition that causes temporary paralysis by damaging the peripheral nervous
system. Muscle weakening leads to issues such as numbness and lack of
reflexes. The immune system’s antibodies assault the myelin coating in the
nerve cell, causing it to rupture. It causes nerve conduction to slow or
cease.

The rest of the paper is organized as follows: Section II presents the 3D
Modelling of the neuron structure, the majority of neuron design choices, and
the execution of each part of the neuron. Section III introduces Neuron
Models. Hardware Implementation and its experimental results and analysis are
reported in Section IV. Section V concludes the contents of this paper and
Section VI give future aspects of this paper.

Figure 1. Demyelination

##  2\. 3D Modelling

The fact that neuron structures can be modeled mathematically has paved the
way for simulating these models in a computer environment. The NEURON
simulation program is at the forefront of modeling neuron models to the
simulation environment. As of February 2021, more than 2400 scientific
articles and book studies have been published using the NEURON simulation
program (Carnevale, ). In its most general definition, NEURON is a simulation
environment for developing and applying models of neurons and neuronal
networks (Carnevale and Hines, 2006). It is suitable for describing and
solving problems involving cable properties, near-membrane extracellular
potential, and complex cell membrane properties that play an important role in
describing neuron morphology. In addition, biophysical events, such as
determining the intracellular and extracellular potassium, sodium, and calcium
concentrations of the generated neurons, to determine the cell membrane
potential can be defined and signal outputs can be obtained (Carnevale and
Hines, 2006). In terms of programming, as well as the _High Order Calculator_
(HOC) interpreter, being programmable in the Python environment brings many
conveniences and advantages. Python has powerful data structures, such as
lists and dictionaries, built in to the language, and it has a large standard
library, extensive functionality for data processing, database access, network
programming, etc. (Hines et al., 2009).

An example of neuron morphology can be created and visualized in the NEURON
program. After the biophysical properties of this model are entered, the
output of the signals formed in the cells can be observed. An example neuron
morphology with five cells is represented in Figure 2.

Figure 2. 5 cells around 50 micron radius

In Figure 2, there are ball and stick cells arranged in a ring. While
performing the biophysical modeling of the cell, parameters, such as the
diameter and length of the soma cell and the cell membrane element
permeability, are defined. Finally, an axon from cell ”n” connects to a
synapse in the middle of the dendrite in cell ”n+1”. For this model, the
specific dynamics of axons do not need to be explicitly modeled. When the soma
fires an action potential, the spike is assumed to travel along the axon, and
with some delay, induces a synaptic event in the dendrite of the target cell
(Davison et al., 2009).

Figure 3. Signal transmission for a cell

Multiple spikes of the signal in the cell in Figure 3 indicate that the spikes
are transmitted throughout the network. The graph showing that all soma cells
are fired in the sequence is illustrated in Figure 4.

Figure 4. Signal sequence graph for all 5 cells

In the NEURON program, the user can create his own cell morphology and
topology, as well as imported pre-built models. In a study at the Allen
Institute, a Gad2-IRES-Cre neuron from layer 5 of the mouse primary visual
cortex was modeled with the NEURON program within the Allen Database of Cell
Types. The model was based on a traced morphology after filling the cell with
biocytin and optimized using experimental electrophysiology data recorded from
the same cell. The electrophysiology data was collected in a highly
standardized way to facilitate comparison across all cells in the database.
The model was optimized by a genetic algorithm that adjusted the densities of
conductance placed at the soma to match experimentally-measured features of
action potential firing (for Brain Science, 2004). [10, Figure 5] and Figure 6
show the real image of the neuron and its model created in the NEURON
environment.

Figure 5. Real image of Gad2-IRES-Cre neuron from layer 5 from the Allen Mouse
Brain Atlas Figure 6. Gad2-IRES-Cre neuron from layer 5 in NEURON

In the image in Figure 6, the tracts of the neuron are divided into different
segments (soma cell, dendrite, and axon). Then, using the positioning
function, the color change was assigned according to the distance of the
appendage in each section to the soma cell. Accordingly, the closest position
to the soma cell is represented by light blue, and the farthest position is
represented by dark purple Figure 6. The exact location information is hidden
inside the graph. This makes it easy to set values for all states at a
particular location.

The simulation results obtained in the study are presented in [10, Figure 7],
[10, Figure 8], and [10, Figure 9]. The summary of electrophysiology is given
in Figure 7, where the values of neuron-specific morphological features are
included. The first of the graphics in the image is the potential difference
in the neuron depending on the pulse amplitude. The second graphic shows the
spike flight time that changes depending on the current amplitude.

Figure 7. Electrophysiology summary

It is aimed to observe the behavior of the neuron cell applied with different
pulse amplitudes. In this direction, it is observed that a total of 5 spikes
occur when a pulse amplitude of 70pA is applied in Figure 8, and a total of 8
spikes are formed when a pulse amplitude of 90pA is applied in Figure 9. As
the pulse amplitude is increased, the observed spike also increases.

Figure 8. The output for 70pA stimulus amplitude Figure 9. The output for 90pA
stimulus amplitude

##  3\. Neuron Models

###  3.1. Hodgkin-Huxley Neuron Model

The _Hodgkin-Huxley_ (HH) neuron model is the study that explains the ionic
mechanism that provides the generation and transmission of action potentials
on the squid giant axon (Hodgkin and Huxley, 1952). The HH Model forms the
basis of many existing neuron models. This model is based on the assumption
that the ion channels in the membrane work independently of each other and
that each channel passes a certain ion. Leakage channels with no specific
selectivity are assumed to carry different ion types. The equivalent schematic
of the HH Model is shown in Figure 10.

Figure 10. Hodgkin-Huxley (HH) Model Equivalent Circuit

For channels, the ion permeability of the membrane is found according to Ohm’s
law using the following formulas:

(1) $G_{Na}=\frac{I_{Na}}{V_{m}-V_{Na}}$ (2) $G_{K}=\frac{I_{K}}{V_{m}-V_{K}}$
(3) $G_{L}=\frac{I_{L}}{V_{m}-V_{L}}$

The sum of the membrane current is represented by Kirchhoff’s current law as
follows:

(4)
$I_{m}=C_{m}\frac{dV_{m}}{dt}+(V_{m}{-}V_{N}{}_{a})G_{N}{}_{a}{+}(V_{m}{-}V_{k})G_{K}+(V_{m}{-}V_{L})G_{L}$

###  3.2. Izhikevich Neuron Model

This model has emerged by reducing the HH Neuron model, which includes 4
varying differential equations, and is valuable in terms of its computational
ease and rich neuron behavior (Izhikevich, 2003). Differential equations
defining the Izhikevich neuron model are given in (5), (6) and (7):

(5) $v=0.04^{2}+5v+140-u+I$

(6) $u=a(bv-u)$

(7) $v=>30mV\implies\binom{v\leftarrow c}{u\leftarrow u+d}$

Different neuron dynamics can be exhibited when fixed parameter values are
properly adjusted. Therefore, it is a suitable model for the study of neuron
behavior.

###  3.3. FitzHugh-Nagumo Neuron Model

This neuron model has simplified the HH neuron model actually, where the
voltage gate variables’ dependency on each other is found. As a result of
these studies, he observed that the voltage gate variables were related to
each other. HH made changes to the ”Van Der Pol Oscillator Model in order to
simplify the Neuron Model. The _FitzHugh-Namugo_ (FHN) neuron model formed as
a result of the studies is explained with the Equations (8) and (9) below
(FitzHugh, 1961):

(8) $\frac{dv}{dt}=c(v-u+I-\frac{v^{3}}{3})$

(9) $\frac{du}{dt}=\frac{(v-bu+a)}{c}$

FHN Neuron Model performs only spike behavior among neuron dynamics. The
frequency change that occurs with the change of the “I” value in the HH Neuron
Model is absent in this model.

###  3.4. Hindmarsh-Rose Neuron Model

The FHN model is a simple version of the HH model and cannot exhibit all
neuron dynamics. The _Hindmarsh-Rose_ (HR) neuron model was obtained by adding
another state variable to the bivariate equation of the FHN Neuron Model. It
is a simple model but can exhibit most of the neuron dynamics. It is defined
by the following equations (10), (11) and (12):

(10) $v^{\prime}=u-v^{3}+bx^{2}+I-w$

(11) $u=1-5v^{2}-u$

(12) $w=\mu(s(v-v_{rest})-w)$

###  3.5. Morris-Lecar Neuron Model

_Morris-Lecar_ (M-L) neuron model is a model using calcium, potassium, and
ohmic load currents and expresses muscle fibers mathematically. It is
expressed by the differential equations (13) and (14):

(13) $C\frac{dV}{dt}=I-g_{L}(V-V_{L})-g_{Ca}M_{ss}(V-V_{Ca})-g_{K}N(V-V_{K})$

(14) $\frac{dN}{dt}=\frac{N_{SS}-N}{\tau_{N}}$

###  3.6. Coupled Neuron Models

Neurons communicate and coordinate through synapses. This coupling between
neurons creates scenarios where rapid action, (such as a working reflex).
Coupling neurons connect with each other using electrical and chemical
pathways. Since the electrical coupling is fast in nature, electrical
conduction is used to replicate this effect such as reflex. A single
neuromorphic hardware can implement only a limited number of neurons and
synapses (Işık et al., 2022). For this work, M-L, FHN, and HR Neuron Models
(which are described below) were used to study the behavior of neuron
coupling.

  * •

The M-L model, which was put forward as the mathematical modeling of muscle
fibers, was arranged as in equation (15), (16), (17), (18), (19) in a research
paper to coupling. In that study, synaptic (chemical) coupling between 2
neurons whose neuron model was implemented with hardware was created and
examined (Xue et al., 2008).

(15)
$\begin{split}C\frac{dV_{1}}{dt}=-g_{ca}M_{\infty}(V_{1})-(V_{1}-V_{ca})-g_{K}M_{\infty}(V_{1})-(V_{1}-V_{ca})\\\
-g_{L}M_{\infty}(V_{1})-(V_{1}-V_{L})+1\end{split}$

(16) $\frac{dN_{1}}{dt}\lambda(-N_{1}+(G(V_{1}))$

(17) $\tau\frac{dZ}{dt}=\alpha F(V_{1})-Z$

(18)
$\begin{split}C\frac{dV_{2}}{dt}=-g_{ca}M_{\infty}(V_{2})-(V_{2}-V_{ca})-g_{K}M_{\infty}(V_{2})-(V_{2}-V_{ca})\\\
-g_{L}M_{\infty}(V_{2})-(V_{2}-V_{L})+1-Z(V_{2}-\gamma)\end{split}$

(19) $\frac{dN_{2}}{dt}\lambda(-N_{2}+(G(V_{2}))$

  * •

The mathematical representation of the FHN model was modified to study the
behavior of synchronized neurons. Modified mathematical are represented in
equations (20), (21), (22), (23).

(20)
$\frac{dV_{1}}{d{\tau}}=\begin{bmatrix}V_{1}-\left(\frac{V_{1}^{3}}{3}\right)\end{bmatrix}-{W_{1}}$

(21)
$\frac{dW_{1}}{d{\tau}}=\epsilon_{1}\begin{bmatrix}g(V_{1})-W_{1}-\eta_{1}\end{bmatrix}$

(22)
$\frac{dV_{2}}{d{\tau}}=\begin{bmatrix}V_{2}-\left(\frac{V_{2}^{3}}{3}\right)\end{bmatrix}-{W_{2}}+DV_{1}$

(23)
$\frac{dW_{2}}{d{\tau}}=\epsilon_{2}\begin{bmatrix}g(V_{2})-W_{2}-\eta_{2}\end{bmatrix}$

  * •

One of the reported models suitable for studying the behavior of synchronized
neurons is the Hindmarsh-Rose neuron model. In the coupled HR neuron model,
the interaction between ”i” and ”j” neurons are expressed using the following
equations (24), (25), (26).

(24)
$v^{\prime}=u{-}v^{3}-bv{+}I{-}w{-}g_{s}\sigma(v_{i})\displaystyle\sum\limits_{j{=}1}^{N}{C_{ij}}\gamma(V_{i}{,}V_{j})$
(25) $u^{\prime}=1-5v^{2}-u$

(26) $w^{\prime}=\mu(s(v-v_{rest})-w)$

##  4\. Hardware Implementation

Proposed circuit model is aimed to represent the stimuli that cannot be
transmitted or incompletely transmitted due to the damaged sheath and to
ensure that they are transmitted to the target neurons intact. Since the model
is designed to be implemented through assistive devices, the advantage of
implementation has been taken into account. The coupled _Hardware Neuron_ (HN)
model is preferred, which is suitable for generating an artificial axon. The
HN model provides an advantage in terms of implementation. Protection of
stimulus is ensured by acting as the myelinated axon body. Our circuit’s
fundamental concept can be applied to many different types of membranes and
can generate a wide range of electrical responses. There are a number of
benefits to studying HN models rather than merely mathematical formulas. Real-
time processing is possible with an HN model and brings for instance energy
efficiency, more accurate and multi-tasking. As a result, when several HN
models are coupled to each other, it would be suitable for researching large-
scale neural network dynamics. Each parameter of a model has a clear physical
interpretation, and in certain circumstances, it can be directly correlated
with electrophysiological values of membranes. Circuit tests and mathematical
analysis to gain a better understanding of the dynamics. These dynamics
separate parts of the design process such that they can be independently
optimized for different metrics such as performance, power, cost, and
reliability (Huynh et al., 2022).

###  4.1. Analog Design

Two neuromorphic circuits are compared in this study, and these circuits are
performing for synchronization HN models. An acceleration phenomenon appears
in a diffusively connected system of mathematical models of the axon, with the
system’s period becoming shorter than the periods of each isolated circuit
(Maeda et al., 2005). Axons self-organize in such a way that they accelerate
through axon and body cell coupling while consuming the least amount of
energy. Our target is used for inhibitory synapse and excitatory synapse in
analog design. In our circuit design, an excitatory synapse which is shown in
Figure 11 is formed by modifying the depolarizing process channel to be
dependent on presynaptic voltage (and altering the reversal potential). The
top trace represents a presynaptic cell. The diode in the synapse symbolizes
the presynaptic side’s isolation as a result of transmitter release. An
inhibitory synapse is shown in Figure 12 is formed by modifying the
repolarizing process channel to be dependent on presynaptic voltage. Shunting
and hyperpolarization are observed at this inhibitory synapse. By connecting
the two cells using a diode and resistors, an electrotonic link is formed,
with a stronger connection from left to right. The traces demonstrate a highly
complicated relationship between the two cells.

Figure 11. Excitatory Circuit

The rectified electrotonic synapse can be rectified in either direction or not
at all. _Negative-Positive-Negative_ (NPN) and _Positive-Negative-Positive_
(PNP) transistors occupy an important place. Our selection are 2N3904 type of
transistor which is a very common NPN transistor that can take 200 mA
(absolute maximum) and frequencies as high as 100 MHz when used as an
amplifier and 2N3906 type of transistor which is a typical PNP bipolar
junction transistor used for general-purpose, low-power amplification or
switching. It is intended for modest electric current and power, medium
voltage, and relatively high speeds. The dynamics of our model are based on
hardware experimentation and mathematical studies. A generic type of neural
circuit have been proposed in this study:

Typical dynamics in the HN model of Maeda (Tsai et al., 2018). The parameters
are C=0.5 mF, $C_{n}$=1.0 mF, $R_{L}=100k$\mathrm{\SIUnitSymbolOhm}$$,
$R_{1}=200k$\mathrm{\SIUnitSymbolOhm}$$,
$R_{2}=2k$\mathrm{\SIUnitSymbolOhm}$$,
$R_{3}=100k$\mathrm{\SIUnitSymbolOhm}$$, $E_{N}a$= 5 V, $E_{K}$= -0.4 V,
$E_{L}$= 0 V, $T_{1}$=$T_{3}$: NPN-type 2N3904, T2: PNP-type 2N3906, and
$I_{e}xt$=0.025 mA. The ordinate is the membrane potential V. In the
literature, one may conduct circuit experiments as well as quantitative
analysis to gain a better knowledge of the dynamics. Several HN model circuits
have been built with motives comparable to recent models (Gulrajani et al.,
1977)(Sekine, 1999).

Figure 12. Inhibitory Circuit

Excitation and inhibition have been widely investigated in terms of proper
neuronal function and stability, and disruption of excitation and inhibition
has been linked to a variety of neurological diseases. When two neuron
activation function circuits are manually linked into the excitation and
inhibition module circuit, hardware experiments for the adapting synapse-based
neuron model can be performed. We built excitatory-inhibitory circuits by
adding excitatory nodes and edges to simulate synaptic diversity. This
diversity attaches additional connection circuit edges in combination with the
addition of inhibitory + excitatory nodes and expands the encoding capacity of
our network. It is shown in LABEL:

Figure 13. Simulation Results of Proposed Circuit

The output impedance of the inhibitory circuit, the $R_{c}$ resistor, and a
1uF capacitor are added to the input impedance of the excitatory circuit. The
expectation here was, from the roles of each parameter, that the former would
be associated with the modification of firing patterns and the latter with the
generation of the potential voltage. In this way, it is aimed that the
circuits work in accordance with the coupled HN model. The secondary circuit
resembled the synaptic voltage value that appeared at the conclusion of the
first circuit at 2.10 Watt values which are shown in Figure 14, and it was
demonstrated that the circuits could operate in a suitable manner.

Figure 14. Simulation Results of Proposed Circuit

This is significant because of the measurement, and hence the definition of
excitatory and inhibitory, may be related to its function. Excitatory and
inhibitory roles have been hypothesized in a variety of ways. It is stated
that the temporally tightly matched E and I circuits described in sensory
cortices can only be measured by sensory-evoked synaptic responses and may be
essential for efficient sensory information coding, whereas the balance of E
and I at the level of single neurons and circuits, in general, may safeguard
circuit stability in previous works (O’Donnell et al., 2017). It demonstrated
two ways for building a varied excitatory-inhibitory neural network, starting
with network topology and then introducing different types of inhibitory
interneurons and circuit variability to the simulated network. They discovered
that inhibitory interneurons improve encoding capacity by shielding the
network from extremely brief activation periods when network wiring complexity
is high. Furthermore, various kinds of interneurons have varying impacts on
encoding capacity and reliability (Denève and Machens, 2016). Our proposed
circuit measured area with die shrink methodology which is the act of an
identical circuit, using a more advanced manufacturing process.

###  4.2. Digital Design

To demonstrate excellent design principles, the hardware neural system was
implemented on Xilinx Kria KV260 FPGA. The mentioned system produces a
hardware platform comprising a high-performance XCK26-SFVC784-2LV FPGA chip.
The number of hardware neurons specified in this study is realized in a single
core of the KV260 development board. This board represented the latest product
of _System on Module_ (SoM). Because of its reconfigurability, developers may
create apps with little or no external memory accesses, which not only reduces
the total power consumption of the program but also improves responsiveness
with lower end-to-end latency (Vision, 2021). The use of artificial neural
networks is critical in order to analyze massive amounts of data collected
from millions of electronic devices all around the world. This sector is under
constant improvement since it is critical to increase speed and performance in
order to match the defined restrictions and objectives.

Figure 15. Structure of Hardware Neuron Model

Capacitor weights are small; however, they have leakage issues and are
substantial to prevent weight decay. Capacitor weights with refreshment can
help with leakage issues, but they need off-chip memory. Digital multipliers
use 130 nm technology and have a larger area than analog multipliers. It
necessitates a huge number of multiplications, which can be multiplexed onto a
small number of physical-digital multipliers. The total of the results of
multiplications yields a weighted input signal. The transfer function is
performing on/off the chip, and it controls and evaluates the weighted sum of
the neural inputs. The sigma activation block, which is constantly on the
neuro-chip, analyzes the neuron state block of the transfer function. The
fastest way to implement the sigmoid function was using non-volatile, read-
only memories. The sigmoid function is one of the most often utilized
activation functions since it fluctuates between 0 and 1, as are
probabilities, which are the outputs of neurons. An activation function is
applied to a weighted sum of inputs, and the outcome is used as an input to
the following layer. The output of this unit will be a nonlinear function of
the weighted input sum since the sigmoid is a nonlinear function. A neuron
that uses a sigmoid function as an activation function is called a sigmoid
unit. Digital design results are shown in Table 1.

Table 1. Utilization Summary Logic Utilization |  | Used
---
| Available
---
| Device
---
LUTs | 2067 | 11720 | XCK26-SFVC784-2LV
Registers | 112 | 2344240 | XCK26-SFVC784-2LV
DSPs | 5 | 1248 | XCK26-SFVC784-2LV
GCLKs | 1 | 352 | XCK26-SFVC784-2LV
Power | 2.780W | - | XCK26-SFVC784-2LV

###  4.3. Overall Performance

We have demonstrated that analog model consumes lower power estimation is that
computed to the digital model. The literature (Vittoz, 1994) clearly states
that the minimal power consumption in analog circuitry is entirely dictated by
the acquired precision and operating frequency. It should be noted that the
circuitry used to program the floating gate charge consumes a significant
amount of chip space (on-chip charge-pumps or high-voltage selection
transistors). The accuracy of an analog circuit is typically described in
terms of _Signal-to-Noise-Ratio_ (SNR), which is the ratio of signal power to
noise power. In real circuits, the SNR inaccuracy is the ratio of signal to
total of all noise, mismatch, drifts, and so on. We have presented only power
consumption and synchronization in our work.

Table 2. Comparison of the proposed implementations with the related works for small moderate scale models Work | Technology | Design Methodology | Power | Operation Frequency
---|---|---|---|---
Grassia et al. (Grassia et al., 2016) | $65\text{\,}\mathrm{nm}$ | Digital design |  $\approx$ $4.560\text{\,}\mathrm{W}$ | 100 MHz
Joubert et al. (Joubert et al., 2012) | $65\text{\,}\mathrm{nm}$ | Analog design | $78.160\text{\,}\mathrm{nW}$ | 256 MHz
Cosp et al. (Cosp et al., 2014) | $0.35\text{\,}\mathrm{\SIUnitSymbolMicro m}$ | Analog design | $40.95\text{\,}\mathrm{nW}$ | 100 MHz
Khanday et al. (Khanday et al., 2018) | $130\text{\,}\mathrm{nm}$ | Analog design | $22.8\text{\,}\mathrm{\SIUnitSymbolMicro W}$ | 8.98 MHz
Our Work | $16\text{\,}\mathrm{nm}$ | Digital design | $2.780\text{\,}\mathrm{W}$ | 100 MHz
| $2\text{\,}\mathrm{\SIUnitSymbolMicro m}$ | Analog design | $2.100\text{\,}\mathrm{W}$ | 1 MHz

Table 2 shows the overall performance of various design approaches. All
studies are evaluated on small moderate scale design instead of neuromorphic
chips (e.g., SpiNNaker (Furber et al., 2014) , TrueNorth (Merolla et al.,
2014) , Loihi (Davies et al., 2018) , BrainScaleS-1 (Meier, 2015), NeuroGrid
(Benjamin et al., 2014) , DYNAP (Moradi et al., 2017) , ODIN (Frenkel et al.,
2018) ) . Thus, in spite of its limited application, it is possible to build
coupled circuits for this disease in a low-power and low-price manner.
Furthermore, for a variety of classification applications, an analog feed-
forward neural network (rather than digital neural nets) is chosen for low
power consumption. Analog neural networks consume less power. As a result, our
proposed circuit lowered 32,3% of the power usage and used the lowest-price
transistors in the market. One of the reasons for the acceleration is that the
connected system’s duty cycle is as low as possible. We built hardware
membrane models that simulated the action potential of neurons in order to
investigate synchronization in a diffusively coupled system. When two circuits
with the same working principle (algorithm) but differing progress lengths
were connected together, the resulting system gave rise to the acceleration HN
model concept.

##  5\. Conclusions

Contrary to our assumptions, we discovered that coupling circuits might
minimize power usage. In other words, the Coupled Neuron Model efficiently
drove the power usage described by the proposed neural circuit. The impulse
produced by the neuron cell was successfully copied and the output was taken
from the analog circuit, which gave the most efficient result. This output can
be correctly transmitted to the target organ with the application. The
generated impulses are coupled and shown in the simulation environment.
Accompanied by coupled HN model, it is modeled to increase the functionality
of the patient by providing the transmission of balance and coordination
reflexes that require speed transmission.

##  6\. FUTURE ASPECTS

Our experience is this proposed HN model based on both analog and digital
designs. These designs are getting mixed-signal design options to show that
simulation works. This research opens the possibility of creating a wearable
sensor device that could help on Guillain-Barre syndrome and other
neurological diseases. We would like to expand our model to a wearable sensor
level. It has been observed, that a response can occur in body cells that are
not innervated as a result of Guillain-Barre syndrome. In addition, it is
planned to test the proposed circuit to transfer from the sensory area to the
brain together with the reverse transformation methodology, and it is planned
as future studies that the sensory information in the receptor cells can be
transferred to the central nervous system. Future studies will cover that our
model will be produced and tested for progress in the lab environment.

##  7\. ACKNOWLEDGEMENT

We thank Lakshmi Mirtinti for her technical assistance and for her valuable
contributions.

## References

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