Neural networks handling sequential patterns
In order to model thinking process in human brain, it is necessary to construct neural network models handling time-varying inputs. Such networks are required to be able to retain information of their past behaviors. This motivates us to introduce a concept stimulus-accumulation-effect. In our models, each artificial neuron accumulates past stimulus effect until it is excited by the influence of current input as well as the accumulation. This effect makes it possible for the neural networks to scan (recall) all embedded memories sequentially, and to associate temporal sequences (such as melodies) with corresponding static patterns (their images and names).
neural network sequential pattern stimulus-accumulation-effect