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A novel CMOS-based neural interface device equipped with an integrated micro light source array was proposed and demonstrated. Target application of the device is optogenetics. GaInN LED array formed on sapphire substrate was successfully assembled with a multifunctional CMOS image sensor which is capable of injecting current via any of the pixel. We demonstrated addressable LED operation with the present device. The device has advantages such as simultaneous multi-site stimulation and on-chip optical imaging, that are not available with previously reported LED array device for optogenetics.

184Multifunctional devices have also be proposed to perform optical stimulation and electric neural measurement simultaneously. Optrode is a group of devices consisting of light-guide structure and electrode for electric measurement 7)~9) . In most cases, one or several light-guides are implemented onto an electrode array. Light is deliverd to the optorode using optical fibers. The optical fiber and wire harness prevent to use the optorodes in freely moving situations. A device with capabilities of local light stimulation and electric neural measurement with fexible interconnection is required for freely-moving situations. In this work, we propose a CMOS-based optoelectronic device which is capable of not only stimulating the neural system with integrated micro light source array, Abstract A CMOS-based optoelectronic device is proposed for on-chip neural stimulation and observation with optogenetic methodology. The device is capable of local light delivery for stimulation and electrical neural signal recording. The device consists of an array of InGaN light emitting diodes (LEDs) and Au stacked bump electrodes integrated on a CMOS image sensor. Capabilities of on-chip light stimulation and signal recordingwere quantitatively characterized. We have also confirmed that neuron-like cells can be cultured on the surface of the device.

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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