Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4947401 | Neurocomputing | 2017 | 23 Pages |
Abstract
We have developed a cellular neural network formed by simplified processing elements composed of thin-film transistors. First, we simplified the neuron circuit into a two-inverter two-switch circuit and the synapse device into only a transistor. Next, we composed the processing elements of thin-film transistors, which are promising for giant microelectronics applications, and formed a cellular neural network by the processing elements. Finally, we confirmed that the cellular neural network can learn multiple logics even in a small-scale neural network. Moreover, we verified that the cellular neural network can simultaneously recognize multiple simple alphabet letters. These results should serve as the theoretical bases to realize ultra-large scale integration for brain-type integrated circuits.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Mutsumi Kimura, Ryohei Morita, Sumio Sugisaki, Tokiyoshi Matsuda, Tomoya Kameda, Yasuhiko Nakashima,