Theoretical Mechanics of Biological Neural Networks

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However, ANNs can also learn based on a pre-existing representation. This process is called fine-tuning and consists of adjusting the weights from a pre-trained network topology at a relatively slow learning rate to perform well on newly supplied input training data. We can also effortlessly replicate ANNs, but we have a while to go before we can do that for a human brain.

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Whether training from scratch or fine-tuning, the weight update process begins by passing data through the neural network, measuring the outcome, and modifying the weights accordingly. Weights are gradually pushed in the directions that most increase performance of the desired task, e. After failed attempts and feedback on the accuracy of the answer, the child tries again in a different direction to achieve the correct response.

An ANN performs the same task when learning. It is fed stimuli that have known responses and a learning regime adjusts weights so as to maximize the number of accurate responses that result from feeding the ANN new stimuli.

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Once this learning process is complete, both the child and the ANN can use their previous representations of the problems to craft responses to new stimuli that they have not previously been exposed to in the learning process. The child learns best via exposure to as many similar problems as possible. An ANN is similar in that with more exposure to the wide distribution of possible stimuli for the task in question, the better the ANN can learn to respond to new stimuli from the same distribution that it was not previously exposed to.

We have long known in humans that the more children are exposed to the world the better they learn, even if this learning is sometimes painful. In fact,when learning is painful the pain serves as a sharp feedback mechanism. Similarly, exposing ANNs to a wide variety of stimuli in a particular domain is extremely important to train or fine-tune a neural network, of any kind, and can ensure you are not over-fitting a model to one kind of stimulus. With additional representations of a particular class of stimuli, the better a network can classify new stimuli, or generalize a concept.

This holds for both biological neural networks and artificial neural networks, although biological neural networks do a much better job of generalizing. This is true in part because they are exposed to vastly more types and modalities of data in part because of more advanced biological topologies and learning algorithms, and in great part because of Darwinism.

The terminology comes from a common expression in 16th century London stating that all swans are white because there has been no documented occurrence of any other colored swan. Therefore, to them a swan must be white to be classified as a swan. The idea here is that if one has grown up seeing only white swans, i. The fact that not so many neuronal connections require re-wiring is one reason why, on average, avid skiers are far-quicker to pick up snowboarding than first-time snowboarders, or why an artificial neural network that is trained for object detection then fine-tuned for face recognition will often arrive at a better solution than one trained strictly, from scratch, on the same face recognition dataset.

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Their topologies are far simpler, they are orders of magnitude smaller, and learning algorithms are comparatively naive. Moreover, they cannot yet be trained to work well for many heterogeneous tasks simultaneously. As we continue to build ANNs to solve hard problems,such as detecting previously unknown types of malware, we continue to also learn more about how the human brain accomplishes tasks. Action potentials in the brain propagate in thousandths of seconds; while ANNs can classify data orders of magnitudes faster.

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For other tasks, the strengths of ANNs supplement and augment the capabilities of even the best human minds automating large workflows. In the near future, ANNs will begin to perform additional classes of tasks at near-human and even superhuman levels, perhaps becoming mathematically and structurally more similar to biological neural networks. Thank you for your article.

I am just starting to learn about neural networks was a very helpful overview, Stu Kupferman. You are commenting using your WordPress. You are commenting using your Google account. You are commenting using your Twitter account. You are commenting using your Facebook account. Notify me of new comments via email. This site uses Akismet to reduce spam. Learn how your comment data is processed.

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Fun with neurons Using biological neural networks , learning emerges from the interconnections between myriad neurons in the brain. What is an Artificial Neural Network? At the high level, a neural network consists of four components: neurons, of course topology — the connectivity path between neurons weights, and a learning algorithm. Image credit: Wikipedia Anatomy of a biological neural network Biological neurons, depicted in schematic form in Figure 1, consist of a cell nucleus, which receives input from other neurons through a web of input terminals, or branches, called dendrites.

Image credit: Quanta Magazine Learning in Biological Networks In biological neural networks like the human brain, learning is achieved by making small tweaks to an existing representation — its configuration contains significant information before any learning is conducted. Broad Exposure is Good We have long known in humans that the more children are exposed to the world the better they learn, even if this learning is sometimes painful.

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The differences between Artificial and Biological Neural Networks

I am just starting to learn about neural networks was a very helpful overview, Stu Kupferman Reply. Share your thoughts with other customers. Write a customer review.

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