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Natural vs Artificial Neural Networks
For the past few years, deep learning and Artificial Neural Networks (ANNs) gained a lot of popularity as a machine learning algorithm in a wide variety of fields. These include computer vision, natural language processing / machine translation, speech processing and generation, robotics and self-driving cars. Many tasks which were previously reserved exclusively for humans slowly become automated with ANNs, often with equal or even better performance.
Looking at the many successes of ANNs and deep learning, one may be inclined to believe that they are to some degree able to emulate how humans think. After all, the idea of neural networks evolved from observing biological nervous systems such as the human brain. In this blog post, I would therefore like to highlight some very important differences between ANNs and nervous systems in vertebrates such as humans. I will explain how both of these work and learn. Furthermore, I will outline some current developments in neuromorphic computing. Neuromorphic computing is a novel computing domain trying to more closely emulate biological nervous systems.
Artifical Neural Networks (ANNs)
As already mentioned, ANNs were developed as very crude approximations of nervous systems found in biological organisms. The idea of an Artificial Neural Network is to transport information along a predefined path between “neurons”. Neurons have the ability to add up information from multiple sources and they generally apply a non-linear transformation to this information in order to allow for more complex interactions.
Contrary to some popular beliefs, the idea of ANNs is already very old. One of the first neural networks to be invented was the perceptron. The perceptron was a very simple neural network with only one neuron and the Heaviside function as a non-linearity. In other words, the perceptron implements the following decision function:
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