Comparative study of Artificial and Biological Neural Networks with respect to structure, functionalities, learning methods and information transmission method across fundamental units. Also includes mathematical modelling of an artificial neuron.

1. ANALOGY BETWEEN ANs AND BNs
It is known very well that the study of ANNs is inspired from the working of
human brain. In this context, it is quite necessary to draw an analogy between
ANN and BNN as the former closely resembles the latter and provides a better
insight for the improvement so as to establish a closer correlation between the
two.
BIOLOGICAL NEURON
STRUCTURE
Biological Neurons have the following parts :
 Soma
 Dendrites
 Synapse
 Axon
Biological neurons have three main parts: a
central cell body, called the soma, and two
different types of branched, tree like
structures that extend from the soma, called
dendrites and axons.
Information from other neurons, in the form
of electrical impulses, enters the dendrites at
connection points called synapses. The
information flows from the dendrites to the
soma where it is processed. Theoutput
signal, a train of impulses, is then sent down
the axon to the synapse of other neurons.
ARTIFICIAL NEURON
Artificial neurons also have the following
components that are equivalent to biological
neurons :
 Node
 Input
 Weight
 Output
The main bodyof an artificial neuron is called
a node or unit. They are physically connected
to one another by wires that resemble the
connections between biological neurons.
The arrangement and connections of the
neurons which make up the network has three
layers :
 The inputlayer, which is the only layer
exposed to external signals.
 The input layer transmits signals to the
neurons in the next layer, which is called
a hidden layer. The hidden layer extracts
relevant features or patterns from the
received signals.
 Thosefeatures or patterns that are
considered important in the hidden layer
stage are then directed to the output
layer, which is the final layer of the
network.

2. FUNCTIONS
Dendrites provide input signals to the cells.
A synapse is able to increase or decrease the
strength of the connection. This is where
information is stored.

The axon sends output signal to another cell.
The axon terminals merge with the dendrites
of the other cells.
The brain is made up of a great number of
neurons (about 1011
), each of which is
connected to many other components (about
104
) and performs some relatively simple
computation, whose nature is unclear, in slow
fashion connections.
The artificial neuron receives inputs in its input
nodes analogous to the electrochemical
impulses the dendrites of biological neurons
receive from another neuron.

The artificial signals can be changed by
altering weights in a manner similar to the
physical changes that occur in the synapses.

The output nodes of the artificial neuron
correspond to output signals sent out from
biological neuron over its axon.
Artificial neurons are connected randomly or
uniformly, and all neurons perform the same
computation. Each connection has a numerical
weight associated with it. Each neuron’s output
is a single numerical activity which is
computed as a monotonic function of the sum
of the products ofthe activity of the input
neurons with their correspondingconnection
weights.
LEARNING

They learn from past experience to improve
their own performance levels.


Learning in biological systems involves
adjustments to the synaptic connections that
exist between the neurons.
They also learn from past experience to
improve their own performance levels.
This is true of artificial neurons as well.

3. INFORMATION
TRANSMISSION
Information transmission from one biological
neuron to another involves in the form of
electrical signals.
In artificial neurons, electrical signals are also
used in information transmission.
MATHEMATICAL MODELLING OF A NEURON:
As is evident from the above comparative study of biological and artificial
neuron chiefly in regard to structural and functional perspectives, in a nutshell,
we can draw an analogous mapping between biological neurons and their
artificial counterparts, as summarised in the following table:
BIOLOGICAL NEURON ARTIFICIAL NEURON
Neuron Processing Element
Dendrites Combining Function
Cell Body Transfer Function
Axons Element Output
Synapses Weights
In case of biological neuron information comes into the neuron via dendrite,
soma processes the information and passes it on via axon. In case of artificial
neuron the information comes into the bodyof an artificial neuron via inputs
that are weighted (each input can be individually multiplied with a weight). The

4. bodyof an artificial neuron then sums the weighted inputs, bias and “processes”
the sum with a transfer function. At the end an artificial neuron passes the
processed information via output(s). Benefit of artificial neuron model
simplicity can be seen in its mathematical description below:
..(1)
Where:
• xi(k) is input value in discrete time k where i goes from 0 to m,
• wi(k) is weight value in discrete time k where i goes from 0 to m,
• b is bias,
• F is a transfer function,
• yi(k) is output value in discrete time k.
As seen from a model of an artificial neuron and its equation (1) the major
unknown variable of our model is its transfer function. Transfer function defines
the properties of artificial neuron and can be any mathematical function. We
chooseit on the basis of problem that artificial neuron (artificial neural network)
needs to solve.
y(k) = F[∑ {𝒙𝒊 ( 𝒌). 𝒘𝒊 ( 𝒌)} + 𝒃]𝒎
𝒊=𝟎