Real time distributed processing of multiple associated pulse pattern sequences
A Real Time Distributed Associative Memory Artificial Neural Network (RTANN) is described. This network associates groups of pulse pattern sequences. The subsequent reoccurrence of some sequences will cause the remainder to be regenerated. Training is carried out in real time simply by feeding pattern sequences directly into the network. The connections between units incorporate a wide range of transmission delays. During training the network enhances connection weights on units where coincidences occur between input and delayed pulses. Pattern regeneration utilises the reoccurrence of coincidences between delayed pulses. The simulation of an RTANN is presented. Continuous dual pattern sequences from notional sensors monitoring the shape and colour of an object were associated directly with a third dual pattern sequence having the form These objects look colour. After training the network was able to correctly generate sentences describing combinations of object and colour not encountered during training.
real time distributed processing pattern sequence associative memory coincidence distributed memory