A model cortical circuit for the storage of temporal sequences

Despite the fact that temporal information processing is of particular significance in biological memory systems, not much has yet been explored about how these systems manage to store temporal information involved in sequences of stimuli. A neural network model capable of learning and recalling temporal sequences is proposed, based on a neural mechanism in which the sequences are expanded into a series of periodic rectangular oscillations. Thus, the mathematical framework underlying the model, to some extent, is concerned with the Walsh function series. The oscillatory activities generated by the interplay between excitatory and inhibitory neuron pools are transmitted to another neuron pool whose role in learning and retrieval is to modify the rhythms and phases of the rectangular oscillations. Thus, a basic functional neural circuit involves three different neuron pools. The modifiability of rhythms and phases is incorporated into the model with the aim of improving the quality of the retrieval. Numerical simulations were conducted to show the characteristic features of the learning as well as the performance of the model in memory recall.

[1]  Y. Miyashita.  Neuronal correlate of visual associative long-term memory in the primate temporal cortex , 1988, Nature.

[2]  S Dehaene,et al.  Neural networks that learn temporal sequences by selection. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Colin Blakemore,et al.  Patterns of Local Connectivity in the Neocortex , 1993, Neural Computation.

[4]  Joachim M. Buhmann,et al.  Noise-driven temporal association in neural networks , 1987 .

[5]  Shiino,et al.  Asymmetric neural networks incorporating the Dale hypothesis and noise-driven chaos. , 1990, Physical review letters.

[6]  J. Walsh.  A Closed Set of Normal Orthogonal Functions , 1923 .

[7]  P. Peretto,et al.  Collective Properties of Neural Networks , 1986 .

[8]  Anthony C. C. Coolen,et al.  Delays in Neural Networks , 1988 .

[9]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[10]  Kevan A. C. Martin,et al.  A Canonical Microcircuit for Neocortex , 1989, Neural Computation.

[11]  Toshio Aoyagi.  A Network of Bursting Neurons for Temporal Association , 1992 .

[12]  Riedel,et al.  Temporal sequences and chaos in neural nets. , 1988, Physical review. A, General physics.

[13]  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.

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

[15]  Daniel J. Amit,et al.  Conversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors , 1999, Neural Computation.