Techniques for minimizing the complexity of synaptic patterns as a key to keeping A-factors high
(from P.A.Wozniak, Economics of Learning)
The most important principle of effective knowledge representation in systems based on active recall and repetition spacing is minimization of the complexity of synaptic patterns involved in storing memory engrams. This principle translates to keeping the content of question-answer items simple, specific, graphic, consistent, comprehensible and univocal. The main purpose of such an approach is to make sure that the spatiotemporal pattern of firing during the learning task is the same in each successive repetition. In other words, there should be minimum change to the synaptic pattern in the course of repetition as a result of pattern extraction. The entire concept of optimum repetition spacing is based on dealing with uniform pieces of information whose memory engrams are uniform and static, and consequently can be treated as atomic entities. If the neuronal firing was to change its course over a number of repetition, a subset of synapses in the relevant synaptic pattern would not receive sufficient enhancement resulting in partial loss of the learned information.
Using examples from the microeconomics knowledge system, I will show all the distinguishable facets of minimization of synaptic patterns involved in representing individual pieces of information stored in the knowledge system: