Temporal learning neural network

Details Temporal learning neural network Temporal learning neural network

1995-03-17

1997-12-30

Hafiz, Tariq R.

395 21, 395 23, 395 24, G06F 1518

Patent

active

057040168

ABSTRACT:
A temporal learning neural network includes a plurality of temporal learning neural processing elements and an input/output control section. Each element includes a calculation device and a learning device. The calculation device includes an input memory section and a response calculation circuit. The learning device includes a learning processing circuit and a history evaluation circuit. The calculation circuit calculates a sum of a total summation value of a product of input values and connection efficacies, and an internal potential, compares the sum with a predetermined threshold value, outputs a 1 or 0 signal depending on the comparison and substitutes internal potential of a next time for the sum. The processing circuit receives an input history evaluation value when the calculation circuit has produced an output 1 signal which strengthens, weakens or leaves unchanged the connection efficacies depending on the comparison. The evaluation circuit obtains an input history value, compares the obtained input history value with the learning threshold value, generates an evaluation signal and distributes the evaluation signal to the input memory section. The input/output control section is provided with input terminals and output terminals, sends signals input from the calculation circuit and evaluation circuit to the input memory section, receives signals output from the calculation circuit and evaluation circuit, and effects communication with each of the processing elements. This process is an input temporal associative learning process.

REFERENCES:
patent: 4994982 (1991-02-01), Duranton et al.
patent: 5402522 (1995-03-01), Alkon et al.
patent: 5452402 (1995-09-01), Sakiyama et al.
International Symposium on Neural Information Processing, Jul. 12-15, 1992, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan, A Dynamic Neural Cell Model Driven by Pulse Train, Yukifumi Shigematsu, Gen Matsumoto and Syuji Akiyama Electrotechnical Laboratory, pp. 130-133.
Haykin, Simon. Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Co. pp. 49-53, 1994.

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