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During the transmission, the nerve impulse travels in the form of electrical signals from the axon of one neuron
to another neuron through a joint called synapse. The dendrites of another neuron receives an impulse which
travels through the cell body and performs an activation function on the impulse received and then gives it to
the output axon which passes the same to the next neuron in the system.
Dendrites
Soma
Nucleus
Axon hillock
Myelin sheath
Schwann cell
Node of ranvier
Terminal
button
A nerve cell (neuron)
Relation between the Neural Network and Nervous System
Just like the human brain where all neurons are interconnected to one another, artificial neural networks also
have a large number of artificial neurons(nodes) that are interconnected to one another in a sequence of layers
of the networks.
Dendrites in the human brain receive the impulse and pass on to the cell body of the neuron. From the cell body
the impulse travels to the axon and passes to another neuron attached through a joint called synapses. This
process goes on through a complex network of neurons to get the desired stimulus of the impulse.
Similarly, the artificial neurons(nodes) take input data and perform simple operations on the data. The result of
these operations is passed to other artificial neurons(nodes) which are arranged in a sequence of layers.
Working of Neural Networks
Neural networks are made up of layers of neurons, just like the human brain that consists of millions of neurons.
These neurons are the core processing units of the network.
A neural network is divided into multiple layers and each layer is further divided into several blocks called nodes.
Each node is responsible to do its task and pass on it to the next layer. First, we have the input layer which
receives the input in several different formats provided by the programmer and feeds it to the neural network.
No processing occurs in the input layer. The output layer predicts our final output. The output at each node is
called its activation or node value.
The layer present in-between input and output layers is called the hidden layers which perform most of the
computations required by our network. These layers are not visible to the user. Each node of the hidden layer
has its own machine learning algorithm which it executes on the data received by the input layer. The processed
Neural Networks 249

