Apoptotic self-organized electronic device using thin-film transistors for artificial neural networks with unsupervised learning functions

Kimura, Mutsumi; Miyatani, Tomoaki; Fujita, Yusuke; Kasakawa, Tomohiro

doi:10.1109/am-fpd.2014.6867164

Cited by 1 publication

(1 citation statement)

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“…This apoptotic self-organized electronic device can be either a mixture of conventional devices or a completely novel device, and it is difficult to classify this device to conventional generations of neural networks. Although the current contents are improvements and expansions of the previously reported devices of the authors, [19][20][21][22] particularly in this study, the neural network is extremely compact and is capable of learning multiple logical operations. We will systematically explain the neural network, which will be convenient for the readers although there will be some repetition of the prior publications.…”

Section: Introductionmentioning

confidence: 82%

Apoptotic self-organized electronic device using thin-film transistors for artificial neural networks with unsupervised learning functions

Kimura

Miyatani

Fujita

et al. 2015

Jpn. J. Appl. Phys.

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Artificial neural networks are promising systems for information processing with many advantages, such as self-teaching and parallel distributed computing. However, conventional networks consist of extremely intricate circuits to guarantee accurate behaviors of the neurons and synapses. We demonstrate an apoptotic self-organized electronic device using thin-film transistors for artificial neural networks with unsupervised learning functions. First, we formed a "neuron" from only eight transistors and reduced a "synapse" to only one transistor by employing the characteristic degradations of the synapse transistors to adjust the synaptic connection strength. Second, we classified the synapses into two types, "concordant" and "discordant" synapses, and composed a local interconnective network optimized for integrated electronic circuits. Finally, we confirmed that the device is feasible and can learn multiple logical operations, including AND, OR, and XOR.

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Section: Introductionmentioning

confidence: 82%