Human Brain Vs Artificial Neural Network – ANN | How far an ANN is from the Human Brain?

The Artificial Neural Network – ANN is one of the most powerful AI techniques through which we are trying to make computers as powerful and capable as the human brain.

With the development of powerful processors and advancement in parallel computation techniques, science is bridging the gap between the human brain and ANN.

Let’s compare the ANN with the human brain for several parameters considering the most powerful parallel computer available today.

Human Brain Vs Artificial Neural Network - ANN


Computers have been evolving for a few decades whereas the human brain has been evolving for tens of millions of years.


Each biological neuron consists of four parts. First is dendrites which collect the input. Second is soma which process the input and generate pulse. Third is axon which transfer the pulse to the end terminals connected to the other neuron. It connected through the fourth part synapsis and electrochemical connection.

Likewise today’s artificial neural network structure also comprises numbers of neurons. Each neuron consists of four parts: inputs, process part, activation function and outputs.

Even though the model of the artificial neuron is far away from the biological neuron, combining the latest training algorithms and parallel processing power, it is capable of performing incredible tasks.

Interconnection of Neurons:

Each biological neuron is connected with thousands of other neurons similar to powerful parallel computers.

That means today’s powerful computers are capable of having similar numbers of interconnections with other neuron models to perform complex tasks.

Processing Power

There are nearly 10 billion neurons in the human cortex compared to thousands of processors in most powerful parallel computers.


The typical operating speeds of biological neurons is measured in milliseconds (10^-3 s), while a silicon chip can operate in nanoseconds (10^-9 s).


The human brain is extremely energy efficient, using approximately 10^-16 joules per operation per second, whereas the best computers today use around 10^-6 joules per operation per second.

Thus efficiency is the most challenging task for parallel computers than the processing speed today.


I am leaving it unto reads. Write in comment about what you think of the future of an artificial neural network?

To get the exact idea about how an ANN works checkout the course Fundamentals of Artificial Neural Network with MATLAB. Starting from the first model of the artificial neuron to it covers implementation with MATLAB.

– Thank you

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By Dr. Jignesh Makwana

Dr. Jignesh Makwana, Ph.D., is an Electrical Engineering expert with over 15 years of teaching experience in subjects such as power electronics, electric drives, and control systems. Formerly an associate professor and head of the Electrical Engineering Department at Marwadi University, he now serves as a product design and development consultant for firms specializing in electric drives and power electronics.