A local and neurobiologically plausible method of learning correlated patterns
The problem of learning correlated patterns in a neurobiologically plausible way without multiple iterations is discussed. Three guiding principles are outlined for solving this problem in a manner suggestive of how the memory modules of the human brain do it. The first, already known, principle of minimum disturbance is applied quantitatively to minimise the number of learning iterations. The second guiding principle involves a self-induced remedy to undo any damage caused to the stabilities of the old stored patterns after a new one has been learnt. The third involves localising connectivities between neurons depending on the structure of the input patterns. A neurobiologically plausible network and a learning rule are constructed based on these principles. Satisfactory use of this network in learning English words as sequences of their letters is demonstrated by means of computer simulation.
correlated patterns learning rules associative memory minimum disturbance principle stabilities of patterns