Category Archives: Connectionism
Plastic Brain
How does the brain learn? Understanding that process would allow one to write software that could learn the same way humans do. But, prior to 1949, no one had a very good answer. It had already been well established that the functional unit of the brain was the neuron, and the structure of these neuronal cells had been studied extensively. However, psychologists did not have a good theory about how neurons produced human behavior.
Enter Donald Hebb, a Canadian Psychologist who was fascinated by how the brain worked. Hebb postulated that neurons form cell assemblies, collections of neurons that act in concert to produce behavior. This idea formed the beginnings of the field of connectionism, an approach to the mind that views complex behavior as emerging from an interconnected network of simpler units. But how do these networks form? To answer this, Hebb proposed a mechanism which has come to be known as “Hebbian Learning.” The idea stated simply is: “Neurons that fire together, wire together.”
The brain is fully connected at birth, but the strength of these connections changes through time as we learn, forming the cell assemblies that Hebb theorized were responsible for behavior. Hebbian learning postulates that when neuron A activates, and then causes neuron B to activate, then the connection strength between the two neurons is increased, and it will be easier for A to activate B in the future. The idea sounds simple, but it goes a long way in explaining how neural networks form in the brain. Not every learning process in the brain can be explained by Hebbian learning, but it does provide an explanation of how complex networks of neurons could form.
After Hebbian learning made its debut in the 1949 book, “The Organization of Behavior,” it then became possible to program computers with the Hebbian rule, giving them the ability to learn. Today, many different types of artificial neural networks (ANNs) are used extensively in the field of artificial intelligence, including applications in face identification, speech and handwriting recognition, financial applications, data mining, and even autonomous vehicles. Hebb’s discovery spawned a whole branch of artificial intelligence and methods for constructing learning mechanisms on computers. ANNs are not yet sufficient for creating human-level intelligence on a computer though. Real neurons are complex biochemical engines, whose behavior can only be approximated with ANNs. Also, human brains come pre-configured to some degree, and without understanding this innate structure well, building large artificial neural networks is not practical.
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