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

Artificial neural networks are promising systems for information processing with many advantages, such as self-teaching and parallel distributed computing. However, conventional ones 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 could work and learn multiple logical operations, including AND and OR.