Learning by Training a Network or Connectionist System
Neural models of intelligence emphasize the brain's ability to adapt to the world in which it is situated by modifying the relationships between individual neurons. Rather than representing knowledge in explicit logical sentences, they capture it implicitly, as a property of patterns of relationships.
- George F. Luger
... "To develop a feel for this analogy, let us consider a few facts from neurobiology. The human brain is estimated to contain a densely interconnected network of approximately 1011 neurons, each connected, on average, to 104 others. Neuron activity is typically excited or inhibited through connections to other neurons. The fastest neuron switching times are known to be on the order of 10-3 seconds---quite slow compared to computer switching speeds of 10-10 seconds. Yet humans are able to make surprisingly complex decisions, surprisingly quickly. For example, it requires approximately 10-1 seconds to visually recognize your mother. Notice the sequence of neuron firings that can take place during this 10-1-second interval cannot possibly be longer than a few hundred steps, giving the switching speed of single neurons. This observation has led many to speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. One motivation for ANN systems is to capture this kind of highly parallel computation based on distributed representations." [From Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).]