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Temporal learning neural network
| Details |
Inventors: Shigematsu, Yukifumi; Matsumoto, Gen;
Assignee: Agency of Industrial Science & Technology, Ministry of International (Tokyo, JP)
Primary Examiner: Hafiz; Tariq R.
Assistant Examiner: Rhodes; Jason W.
Attorney, Agent or Firm: Oblon, Spivak, McClelland, Maier & Neustadt, P.C.
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. |
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DETAILED DESCRIPTION To attain the above object, the present invention provides a temporal learning neural network comprising a substrate, a plurality of temporal learning neural processing elements integrated on the substrate, and an input/output control section; each of the processing elements comprising calculation means and learning means.
The calculation means can comprise means for inputting a plurality of signal pulses, an input memory section for holding a plurality of input signals input from the means, and a response calculation circuit.
The response calculation circuit calculates a sum of (a) a total summation value of a product of (i) input values to the input memory section and (ii) corresponding input terminal connection efficacies, and (b) an internal potential remaining in a processing element at a current time, which is obtained by decaying the internal potential of the processing element at a preceding time.
The sum forms an accumulated value.
The response calculation circuit compares the accumulated value with a predetermined threshold value and, (1) when the accumulated value exceeds the predetermined threshold value, outputs an output 1 signal and stores, as the internal potential of the processing element at a subsequent time, a value obtained by deducting a constant from the accumulated value, and (2) when the accumulated value does not exceed the predetermined value, outputs an output 0 signal and stores the accumulated value as the internal potential of the processing element of subsequent time.
The learning means can comprise a learning processing circuit and a history evaluation circuit.
When the response calculation circuit has produced an output 1 signal, the learning processing circuit receives from the history evaluation circuit an input history evaluation value (positive/negative/zero) to (1) strengthen the connection efficacies when the evaluation value is positive, (2) weaken the efficacies when the evaluation value is negative and (3) leave the connection efficacies unchanged when the evaluation value is zero, thereby generating new connection efficacies
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Fault Detection 
