Learning of loop neural circuit for memory

We have developed a time-shift map of the brain, which shows/ visualizes signal such as EEG and MEG etc. is transmitted/ propagated at each brain part where in each frequency band action potential information of time-to-time is transmitted to and processed at exact remote portion. Assuming the memory is composed of loop neural circuit in the cerebral cortex, we have also shown a feasibility of self organization of loop neural circuit for memory, association, and abstraction, which is algorithmically realizable and able to account well the time-shift map. The generated sequence from the transmitting loop corresponding to the memory is a code by pseudo random sequence including M-sequence which is now popular in spread spectrum communication or mobile CDMA. We give in this paper how a memory content expressed by loop circuit shape is copied to blank area of cerebral cortex etc. using simulation of small scale loop circuit model with 3-5 neuron cells and back-propagation learning algorithm. The model seems able to account well unifiedly the information processing function of the brain.