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).

[1]  H Sompolinsky,et al.  Associative neural network model for the generation of temporal patterns. Theory and application to central pattern generators. , 1988, Biophysical journal.

[2]  Yutaka Sakaguchi,et al.  A robot organizing purposive behavior by itself , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[3]  J Nagumo,et al.  [A model of associative memory]. , 1972, Iyo denshi to seitai kogaku. Japanese journal of medical electronics and biological engineering.

[4]  T. Tsuboi,et al.  Double beta decay nuclear matrix element of 48Ca , 1984 .

[5]  Alexander H. Waibel,et al.  Modular Construction of Time-Delay Neural Networks for Speech Recognition , 1989, Neural Computation.

[6]  Michael I. Jordan.  Serial Order: A Parallel Distributed Processing Approach , 1997 .

[7]  E. Caianiello.  Outline of a theory of thought-processes and thinking machines. , 1961, Journal of theoretical biology.

[8]  Subhash Kak.  Self indexing of neural memories , 1990 .

[9]  K. Aihara,et al.  Chaotic neural networks , 1990 .

[10]  D Kleinfeld,et al.  Sequential state generation by model neural networks. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Kanter,et al.  Temporal association in asymmetric neural networks. , 1986, Physical review letters.

[12]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..