A Flexible Memristor Model With Electronic Resistive Switching Memory Behavior and Its Application in Spiking Neural Network.

Memristive technologies are attractive due to their non-volatility, high-density, low-power and compatibility with CMOS. For memristive devices, a model corresponding to practical behavioral characteristics is highly favorable for the realization of its neuromorphic system and applications. This paper presents a novel flexible memristor model with electronic resistive switching memory behavior. Firstly, the Ag-Au / MoSe2-doped Se / Au-Ag memristor is prepared using hydrothermal synthesis method and magnetron sputtering method, and its performance test is conducted on an electrochemical workstation. Then, the mathematical model and SPICE circuit model of the Ag-Au / MoSe2-doped Se / Au-Ag memristor are constructed. The model accuracy is verified by using the electrochemical data derived from the performance test. Furthermore, the proposed model is applied to the circuit implementation of spiking neural network with biological mechanism. Finally, computer simulations and analysis are carried out to verify the validity and effectiveness of the entire scheme.

A speedy cardiovascular diseases classifier using multiple criteria decision analysis.

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented.

Compact image representation model based on both nCRF and reverse control mechanisms.

The aim of this paper is to construct a bio-inspired hierarchical neural network that could accurately represent visual images and facilitate follow-up processing. Our computational model adopted a ganglion cell (GC) mechanism with a receptive field that dynamically self-adjusts according to the characteristics of an input image. For each GC, a micro neural circuit and a reverse control circuit were developed to self-adaptively resize the receptive field. An array was also designed to imitate the layer of GCs that perform image representation. Results revealed that this GC array could represent images from the external environment with a low processing cost, and this nonclassical receptive field mechanism could substantially improve both segmentation and integration processing. This model enables automatic extraction of blocks from images, which makes multiscale representation feasible. Importantly, once an original pixel-level image was reorganized into a GC array, semantic-level features emerged. Because GCs, like symbols, are discrete and separable, this GC-grained compact representation is open to operations that can manipulate images partially and selectively. Thus, the GC-array model provides a basic infrastructure and allows for high-level image processing.