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How Does a Neural Network Mimic the Human Brain?



Does a neural network have to mimic the human brain in every way? Technically no, because the human brain has what is called a biological neural network as opposed to an artificial one. The real question we should be asking is, “How does an artificial neural network mimic the human brain?”

Biological Neural Networks and the Architecture of the Brain

The smallest unit of a human brain’s biological neural network is the biological neuron. A neuron is actually a type of cell found within the brain and the nervous system. Biological neurons act as neurotransmitters for our brain, nervous system, and motor system. The nervous system receives input from the outside world and sends it through the neurons to the brain. The brain sends commands, as output, through the neurons to the motor system to enact a response.

These neurons connect to each other through synapses to form large structures known as nodes and layers. These layers form the biological neural network that is used to form the nervous system and the brain. The activity in these layers and biological neural networks is used to perform all mechanisms of the human mind imaginable: image recognition, pattern recognition, object recognition, feature extraction, visualization, speech recognition, natural language, object recognition, cognition, prediction, etc.

Artificial Neural Networks and the Architecture of Artificial Intelligence

One could argue that the smallest unit of an artificial neural network (ANN) is not an artificial neuron but a data point. Picture a dataset filled with millions of features and pieces of information (data). The dataset, like the human body, also has cells. In each cell is a fact, and each fact is a data point.

While the artificial neuron may not be the smallest unit in the artificial neural network, it is the basic building block of that ANN and the basic building block of computer science in general. The artificial neuron is basically a conditional statement, the basis of binary code, and what is referred to in computer science as a switch. A switch, or artificial neuron, is an algorithm at its most primitive level, and algorithms are the foundation of any computer program.

Like biological neurons, these artificial neurons form nodes. These nodes form neural nets. Like biological neural networks, layers are formed. In ANNs, however, these layers come in many varieties: first layer, next layer, node layer, convolutional layer, recurrent layer, input layer, an output layer, hidden layer, deep layer, the final layer, etc. These layers are used to form neural networks, and these networks also come in varieties: deep neural networks, deep-learning networks, convolutional neural networks (CNN), recurrent neural networks (RNN), etc.

In order to improve our algorithms, data scientists have figured out how to create learning algorithms through training sets. This was the beginning of machine learning. Then these data scientists figured out how to train algorithms to train other algorithms, which was the beginning of unsupervised learning. These training sets were stored as layers so that the input of one layer could deliver output to the next layer. As the layer stacks got deeper and deeper, deep neural networks were developed. As data scientists trained their algorithms to form deep-learning networks, deep learning was born.

Similarities Between Neural Networks

The computational model for artificial neural networks is based on the biological neural networks, layers, nodes, and neurons of the human brain. Of course, the computational model was adjusted from time to time to meet the needs of computer scientists for different applications. Regardless, the basic elements are still there, and computer science owes a non-repayable debt to neuroscience and some unknown, long-deceased, individual neuron.

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