Pulsating neuron produced by electron-trapping materials

Mimicking the retinal neuron functions by a photoresponsive single transistor with a double gate

To realize a low-cost neuromorphic visual system, employing an artificial neuron capable of mimicking the retinal neuron functions is essential. A photoresponsive single transistor neuron composed of a vertical silicon nanowire is proposed. Similar to retinal neurons, various photoresponsive characteristics of the single transistor neuron can be modulated by light intensity as well as wavelength and have a high responsivity to green light like the human eye. The device is designed with a cylindrical surrounding double-gate structure, enclosed by an independently controlled outer gate and inner gate. The outer gate has the function of selectively inhibiting neuron activity, which can mimic lateral inhibition of amacrine cells to ganglion cells, and the inner gate can be utilized for the adjustment of the firing threshold voltage, which can be used to mimic the regulation of photoresponsivity by horizontal cells for adaptive visual perception. Furthermore, a myelination function that controls the speed of information transmission is obtained according to the inherent asymmetric source/drain structure of a vertical silicon nanowire. This work can enable photoresponsive neuronal function using only a single transistor, providing a promising hardware implementation for building miniaturized neuromorphic vision systems at low cost.

Optical realization of bioinspired spiking neurons in the electron trapping material thin film

A thin film of electron-trapping material (ETM), when combined with suitable optical bistability, is considered as a medium for optical implementation of bioinspired neural nets. The optical mechanism of ETM under blue light and near-infrared exposure has the inherent ability at the material level to mimic the crucial components of the stylized Hodgkin-Huxley model of biological neurons. Combining this unique property with the high-resolution capability of ETM, a dense network of bioinspired neurons can be realized in a thin film of this infrared stimulable storage phosphor. When combined with suitable optical bistability and optical interconnectivity, it has the potential of producing an artificial nonlinear excitable medium analog to cortical tissue.

Electron-trapping materials and electron-beam-addressed electron-trapping material devices: an improved model

An improved model governing the dynamics of electron-trapping materials (ETM's) under simultaneous illumination of blue and IR light is developed. The new model takes into account previously neglected effects, such as electron-trap-density saturation and dependence of electron-trapping efficiency on the existing level of trapped-electron density. Inclusion of both effects in a model is vitally important for effective use of ETM's in optoelectronic neurocomputing with pulsating neurons. Experimental verification of the new model is given. General issues of addressing ETM's with an electron beam are also studied, and a general design equation for electron-beam-addressed ETM devices is developed. In particular, two electron-beam-addressed ETM devices that are of special interest, the field-emission microcathode ETM spatial light modulator and the ETM-based image intensifier, are presented.

Optoelectronic neural dendritic tree processing with electron-trapping materials

We show that under simultaneous illumination of pulsed blue light and constant IR light the dynamic responses of electron-trapping materials could be employed to implement optically controlled neural dendritic responses. The importance of neurocomputing with biology-oriented spiking neurons and the role played by dendritic trees are discussed. Computer simulations of dendritic responses in biological neuron and experimental results of electron-trapping material dynamics are presented. These results show that electron-trapping materials are well suited for implementing optically controlled dendritic responses for use in large-scale biology-oriented optoelectronic spiking neural networks.