Artificial Neural Network Vs Human Brain: Understanding the critical Difference

Artificial Neural Network vs Human Brain

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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?

The answer is – Artificial intelligence. Facebook uses facial recognition powered by Artificial Neural Networks to suggest who you should tag in the post.

Artificial Neural Networks are 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 an Artificial Neural Network models the brain and the difference between an artificial neural network and the human brain.

Key Takeaways 

  • A brief explanation of Biological Neurons and their parts
  • Description of Artificial Neural Network and Biological Neural Network 
  • Difference between Artificial Neural Network and Biological Neural Network

Biological Neuron Explained

A neuron is the foundational building block that builds up the human nervous system.

The neurons are just like some other cells in the human body but what makes them different is their ability to transmit information throughout the body.

Basically, a biological neuron is divided into 3 main parts.

diagram of a biological neuron - artificial neural network vs human brain

  1. The Dendrites: The dendrites are the branch-like structures that receive signals from the other neurons. They process these signals and transfer the information to the core of the neuron.
  2. The Cell body: This cell body of a neuron comprises the nucleus (the part of the cell which contains its genetic material). In the cell body, neural proteins and membranes are synthesised and degraded.
  3. The Axon: The axon looks like a thin fibre that goes beyond a neuron. It transmits electric signals to carry out sensory perception and movement across long distances.

What is Artificial Neural Network (ANNs)?

ANNs started with background work in the late nineteenth and early twentieth century. At that time, there were no mathematical theories or algorithms about neural networks.

They started by researching interdisciplinary work in Psychology, Physics, and Neurophysiology.

In the last two decades, ANN has been touching the roofs. It is in every other field and a lot of research has been done with new papers being published now and then. 

Considering ANN as omnipresent, it is available in every field ranging from the environment to electronics.

What is Biological Neural Network (BNNs)?

In the Biological Neural network, neurons work inside a human brain and are connected by synapses activated for the specific function they ought to carry out.

Early studies of BNNs were done around the 1800s in terms of psychology but the first rule of neuronal learning and the meaning of BNN was coined 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.

Two basic kinds of connections between neurons are 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.

Now that we know what BNN and ANN are, let us now figure out the key differences when it comes to artificial neural network vs human brain.

Artificial Neural Network vs Biological Neural Network

The biological 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 the working difference between ANN and the human brain.

  • SIZE: In the human brain, there are 86 billion neurons and more than 100 trillion synapses to pass on electrical signals throughout a biological body. But on the other hand, the number of neurons in the artificial neural network is way lesser in numbers. One layer perceptron network consists of several perceptrons that are not connected to one another. They receive inputs on their “dendrites” and generate output on their “axon” branches.

  • SIGNALS: Based on the action potential, biological synapses either carry a signal or don’t. An artificial neuron accepts continuous values and applies a simple non-linear, easily differentiable function (an activation function) on the sum of its weighted inputs to restrict the outputs’ range of values.

  • TOPOLOGY: The artificial layers add up one by one instead of being a part of a network with nodes that don’t add up synchronously. On the other hand, in biological networks, neurons parallelly fire asynchronously with a small portion of highly connected neurons and a large amount of lesser connected ones.

  • POWER CONSUMPTION:  The biological brain consumes about 20% of the overall human body’s energy. An adult brain operates on about 20 watts, and in comparison to this, artificial builds can’t even match the efficiency level of a biological brain.

  • SPEED: In the biological neuron, electric signals travel at varying speeds depending on the type of nerve impulse. The speed usually ranges from 0.61 m/s to 119 m/s. On the other hand, an artificial neuron emits a signal by the continuous, floating-point number values of synaptic weights.
Artificial Neural Network vs Biological Neural Networks

Also read: 7 Applications Of Artificial Intelligence In The Healthcare Sector

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 the artificial neural network models the brain, how it is spreading across every field possible, and the difference between a neural network and the human brain.

Artificial Intelligence is 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 per cent 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 x.ai, a 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.

Also read: Artificial Intelligence And The Future Of Humans

Conclusion

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?

Learn all about AI and build your skills in AI by reading more blogs on skills required for AI, AI Influencers to follow in 2021, the Types of AI, and many more.

Get to know more about us by visiting us on www.verzeo.com and checking out all other courses to your liking in a wide array of over 25+ professional disciplines taught by industry professionals. Lay the foundation for a successful career and future now with Verzeo.

Frequently Asked Questions (FAQs)

1. What are the various parts of a Biological Neuron?

A biological neuron has mainly 3 parts, as mentioned below:

1. Dendrites
2. Cell Body 
3. Axon 

2. What is an Artificial Neural Network?

An artificial neural network is designed by computer programs to function like interconnected brain cells. It includes several elements that interpret the output based on the pre-defined input stored in it.

3. Where is an Artificial Neural Network used?

An artificial neural network is used in numerous applications, including machine translation, medical diagnosis, image, and speech recognition.

4. Who invented Artificial Neural Networks?

Artificial neural networks were invented by Frank Rosenblatt in 1958.

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