Artificial neural network vs Human brain | Verzeo

What is the difference between artificial neural network and the human brain?

Have you ever uploaded a photo on Facebook with your friends? You might have noticed how Facebook automatically highlights faces and prompts friends to tag in the photo. But how does Facebook know which of your friends is in this photo?

BY Saumya

31st August 2020

Difference between human brain vs artificial neural network - Verzeo

The answer is - Artificial intelligence. Facebook uses facial recognition powered by artificial neural networks to suggest to you whom you should tag in the post.

Artificial Neural Networks is the main tool used in Machine learning. It has been gaining popularity at a very fast pace with Deep Learning, Data Science and Machine Learning being around in the past few years.

Artificial Neural networks have taken over a lot of work that was considered manual effort which made us realize that Artificial neural networks are biologically inspired by the human brain and our nervous system.

So in this blog, we’ll discuss how does an artificial neural network model the brain and what is the difference between neural network and the human brain

What is Artificial Neural Networks (ANNs)

ANN’s had started with background work in the late nineteenth and early twentieth century. At that time there were no mathematical theories or algorithms about the neural networks. They had started with researching interdisciplinary work in psychology, physics, and neurophysiology. In the last two decades, ANN has been touching the roofs, it’s in every other field and a lot of research work has been done with new papers being published now and then.  Consider ANN as omnipresent as it is available in every field ranging from the environment to electronics.

What is Biological Neural Network (BNNs)?

In the Biological Neural network, neurons are working inside a human brain which is connected by synapses activated for the specific function they ought to carry out. Early studies of BNN’s have been done around the 1800s in terms of psychology but  The first rule of neuronal learning and what the BNN’s are was described by Hebb in 1949, in the Hebbian theory.

The connections between the neurons in the human brain are much more complicated than the artificial ones. There are two basic kinds of connections between neurons present in the biological brain called synapses, both electrical and chemical. Synapses help the connection of neurons in overlapping and interlinking the neural circuits. Consider the Biological Neural Network to be a connective bridge in the difference between a neural network and the human brain.


The brain and Artificial Neural Networks are two of the most controversial aspects of analysis in the field of Neural Network research. But there have been some postulations regarding working difference between a neural network and the human brain.

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  • An artificial neural network has 10-1000 neurons in them, whereas a human brain has around 86 billion neurons in it. Both networks have different types of working and structure. In a human brain, the single neuron can function both input and output information using it’s different ends whereas for Artificial Neurons there are different layers of neurons for input and output completely.

  • BNN’S don’t usually start or stop learning, and we still don’t know how it learns or recalls information. However, we are aware that Neuroplasticity allows new connections to be created and how synapses may strengthen or weaken based on the function’s importance.

    We also understand that we learn by repetition and while we are sleeping. Tasks which might have required pure focus can be done automatically once they are mastered by our human brain.

    ANN’s have a predefined model, only the weights of connections can change during training. The neurons can neither be added nor removed. For the training session of an ANN, it is gone through random data sets and not the same set of examples.

    A BNN will have a solution to every problem a human brain would face but this cannot be experienced with ANN.

  • The human brain’s neurons or the BNN have a very complicated topology. They have connected asynchronously. They can fire up in parallel and a serial way too. They can be present in small portions with high numbers of neurons or in a huge hub with less number of neurons. There is no fixed pattern for them to work.

    Whereas ANNs are mostly in a tree pattern having layers. Its layers are not connected to the parallel neighbouring layers, but it is possible to work as loops using some variant layering. All the layers are connected and compute one by one instead of just being connected asynchronously.

  • We are well aware of how much heat artificial machines generate while being used. An average Graphics processing unit consumes 250 watts and needs a power supply to be working all the time.

    Whereas the human brain consumes only 20% of the energy of our body to function. Despite the brain having a huge cut, it only needs 20 watts to operate, making it very efficient as more energy than this will be needed to light a bulb.

  • Signals in a human brain move at a speed dependent on the nerve impulse. It can vary from 0.61m/s to 119m/s. Some of the biological neurons can also fire up to 200 times a second on average. The signal’s speed varies from person to person.

    Signals in ANN carry continuously with floating-point numbers of the synaptic weights. An ANN can control that which function can be carried out at what speed exactly.

    Where a human brain might get tired of the information load one after the other that is not the case with Artificial brains.

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Artificial Intelligence’s Impact on Future

Now after going through this, You would’ve got a clear understanding of how the Artificial brain works, how does an artificial neural network model the brain, how it is spreading across every field possible, and the difference between a neural network and human brain.

Artificial Intelligence is like the driver for all the emerging industries we have today. It encapsulates all the prominent concepts like Big data, Deep Learning, Data Science and Machine Learning.

A recent study from Redwood Software and Sapio Research said they believe that 60 percent of businesses can be automated in the next five years. Gartner Analytics reported that AI will produce more jobs than it will displace.

Dennis Mortensen, CEO and founder of, maker of AI-based virtual assistants says “I look at our firm and two-thirds of the jobs here didn’t exist a few years ago”. For many reasons, the future with AI is the future most of the world sees and is working towards.

So you have finally made it to the end of this article which shows how much you want to make it big in AI. Why leave it here when you can easily learn how Artificial Intelligence works and become an expert in it?

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