| 摘要 |
An artificial neural network that can act like the real neural network according to the input history of signals input. The network includes a learning circuit that stores an input history of an input signal, an output circuit that is connected to the learning circuit, and a reset circuit that resets the input history stored in the learning circuit. The learning circuit changes a potential-change characteristic of an internal node included in the output circuit, according to the input history. The output circuit starts an output operation of data when a potential at the internal node exceeds a threshold value. The artificial neural network of this invention can operate almost in the same way as the real neural network, because it performs an output operation, such as an oscillating operation, in response to the history of the input signal.
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