Artificial Neural Networks — Mapping the Human Brain
Understanding the Concept
The human brain consists of neurons or nerve cells which transmit and process the information received from our senses. Many such nerve cells are arranged together in our brain to form a network of nerves. These nerves pass electrical impulses i.e the excitation from one neuron to the other.
The dendrites receive the impulse from the terminal button or synapse of an adjoining neuron. Dendrites carry the impulse to the nucleus of the nerve cell which is also called as soma. Here , the electrical impulse is processed and then passed on to the axon. The axon is longer branch among the dendrites which carries the impulse from the soma to the synapse. The synapse then , passes the impulse to dendrites of the second neuron. Thus, a complex network of neurons is created in the human brain.
The same concept of the network of neurons is used in machine learning algorithms. In this case , the neurons are created artificially on a computer . Connecting many such artificial neurons creates an artificial neural network. The working of an artificial neuron is similar to that of a neuron present in our brain.
Construction of an Artificial Neuron
The data in the network flows through each neuron by a connection. Every connection has a specific weight by which the flow of data is regulated.
In the above diagram ,
x1 and x2 are the two inputs. They could be an integer , float etc. Here , for example , we assume them as 1 and 0 respectively.
When these inputs pass through the connections ( through W1 and W2 ) , they are adjusted depending on the connection weights.
Let us assume that W1 = 0.5 and W2 = 0.6 and W3 = 0.2 , then for adjusting the weights , we use ,
x1 * W1 + x2 * W2 = 1 * 0.5 + 0 * 0.6 = 0.5
Here we multiply the input with its weight and add them together. Hence , 0.5 is the value adjusted by the weights of the connection.The connections are assumed to be the dendrites in an artificial neuron.
Now , for processing the information we need an activation function ( here , it can be assumed as the soma ) . Here , I have used the simple sigmoid function .
f ( x ) = f ( 0.5 ) = 0.6224
The above value could be assumed as the output of the neuron ( axon ). This value needs to be multiplied by W3 .
0.6224 * W3 = 0.6224 * 0.2 = 0.12448
Now finally we apply the activation function to the above value ,
f ( x ) = f ( 0.12448 ) = 0.5310
Hence y ( final prediction) = 0.5310
In this example the weights were randomly generated. Artificial Neural Network is a supervised machine learning algorithm usually used for regression problems. In the next article , I will be also showing the back propagation method to adjust the weights in a systematic way.
