Humans have made several attempts to mimic the biological systems, and one of them is artificial neural networks inspired by the biological neural networks in living organisms. However, they are very much different in several ways. For example, the birds had inspired humans to create airplanes, and the four-legged animals inspired us to develop cars.
The artificial counterparts are definitely more powerful and make our life better. The perceptrons, who are the predecessors of artificial neurons, were created to mimic certain parts of a biological neuron such as dendrite, axon, and cell body using mathematical models, electronics, and whatever limited information we have of biological neural networks.
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Components and Working of Biological Neural Networks
Image caption: Parts of a biological neural network
In living organisms, the brain is the control unit of the neural network, and it has different subunits that take care of vision, senses, movement, and hearing. The brain is connected with a dense network of nerves to the rest of the body’s sensors and actors. There are approximately 10¹¹ neurons in the brain, and these are the building blocks of the complete central nervous system of the living body.
The neuron is the fundamental building block of neural networks. In the biological systems, a neuron is a cell just like any other cell of the body, which has a DNA code and is generated in the same way as the other cells. Though it might have different DNA, the function is similar in all the organisms. A neuron comprises three major parts: the cell body (also called Soma), the dendrites, and the axon. The dendrites are like fibers branched in different directions and are connected to many cells in that cluster.
Dendrites receive the signals from surrounding neurons, and the axon transmits the signal to the other neurons. At the ending terminal of the axon, the contact with the dendrite is made through a synapse. Axon is a long fiber that transports the output signal as electric impulses along its length. Each neuron has one axon. Axons pass impulses from one neuron to another like a domino effect.
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Why Understand Biological Neural Networks?
For creating mathematical models for artificial neural networks, theoretical analysis of biological neural networks is essential as they have a very close relationship. And this understanding of the brain’s neural networks has opened horizons for the development of artificial neural network systems and adaptive systems designed to learn and adapt to the situations and inputs.
Image caption: An artificial neuron
Biological Neural Networks vs Artificial Neural Networks
The human brain consists of about 86 billion neurons and more than 100 trillion synapses. In artificial neural networks, the number of neurons is about 10 to 1000. But we cannot compare biological and artificial neural networks’ capabilities based on just the number of neurons. There are other factors also that need to be considered. There are many layers in artificial neural networks, and they are interconnected to solve classification problems.
Biological neural networks tolerate a great deal of ambiguity in data. However, artificial neural networks require somewhat precise, structured, and formatted data to tolerate ambiguity. Biological neural networks are fault-tolerant to a certain level, and the minor failures will not always result in memory loss.
The brain can recover and heal to an extent. But the artificial neural networks are not designed for fault tolerance or self-regeneration. We can still sometimes recover by saving the model’s current weight values and continuing the training from the saved state.
Talking about power consumption, the brain requires about 20% of all the human body’s energy, equivalent to about 20 watts, which is exceptionally efficient. But computers need an enormous amount of computational power to solve the same problem, and they also generate a lot of heat during computation.
Artificial neural networks were inspired by the biological neural networks of the human body. The modeling of biological neural networks was a crucial step in the development of artificial neural networks. Many scientists attempted to understand the working of the brain. Artificial neural networks today are being used for various applications, some are biologically related, and most of them are engineering related.
Even though biological neural networks and artificial neural networks are similar in function, they still have many differences. Many attempts have been made to understand the complex mechanism of biological neural networks. Yet, they still hold many secrets to unlock and inspire the future of artificial intelligence.
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What is the need of biological neural network?
Neural network, a network of simple processors (neurons) is found everywhere in the organism: in human brain, in every animal brain and in our heart, pancreas or lungs. It's a very efficient mechanism, whose functioning principle is based on the learning process, that makes these systems very adaptive. The study of biological neural networks is important to understand and simulate the functioning of our own brain, the best known and most complex biological neural network in the world. This can lead to the development of bio-inspired artificial neural networks.
What are the characteristics of a biological neural network?
A biological neural network is a network of neurons that are connected together by axons and dendrites. The connections between neurons are made by synapses. The axons transport chemicals that cause neurotransmitters to be released onto dendrites, where the neurotransmitters are then able to excite or inhibit an adjacent neuron. The neural network is able to learn and remember information, allowing it to solve problems or make decisions.
What are the limitations of deep learning?
The limitations of deep learning are similar to the limitations of all machine learning techniques. The common problem for all techniques is that they only give you the answers to the questions you ask them. They can't answer questions that you didn't think of before. Deep learning is heavily dependent on the data you give them. If your data isn't complete, there are gaps in it, or the data itself is suspect then your deep learning model won't be very good. As a result you will get poor performance.