Floating-Point Approximation Enabling Cost-Effective and High-Precision Digital Implementation of FitzHugh-Nagumo Neural Networks.

The study of neuron interactions and hardware implementations are crucial research directions in neuroscience, particularly in developing large-scale biological neural networks. The FitzHugh-Nagumo (FHN) model is a popular neuron model with highly biological plausibility, but its complexity makes it difficult to apply at scale. This paper presents a cost-saving and improved precision approximation algorithm for the digital implementation of the FHN model. By converting the computational data into floating-point numbers, the original multiplication calculations are replaced by adding the floating-point exponent part and fitting the mantissa part with piecewise linear. In the hardware implementation, shifters and adders are used, greatly reducing resource overhead. Implementing FHN neurons by this approximation calculations on FPGA reduces the normalized root mean square error (RMSE) to 3.5% of the state-of-the-art (SOTA) while maintaining a performance overhead ratio improvement of 1.09 times. Compared to implementations based on approximate multipliers, the proposed method achieves a 20% reduction in error at the cost of a 2.8% increase in overhead.This model gained additional biological properties compared to LIF while reducing the deployment scale by only 9%. Furthermore, the hardware implementation of nine coupled circular networks with eight nodes and directional diffusion was carried out to demonstrate the algorithm's effectiveness on neural networks. The error decreased to 60% compared to the single neuron of the SOTA. This hardware-friendly algorithm allows for the low-cost implementation of high-precision hardware simulation, providing a novel perspective for studying large-scale, biologically plausible neural networks.

Achieving High Core Neuron Density in a Neuromorphic Chip Through Trade-off Among Area, Power Consumption, and Data Access Bandwidth.

As a crucial component of neuromorphic chips, on-chip memory usually occupies most of the on-chip resources and limits the improvement of neuron density. The alternative of using off-chip memory may result in additional power consumption or even a bottleneck for off-chip data access. This article proposes an on- and off-chip co-design approach and a figure of merit (FOM) to achieve a trade-off between chip area, power consumption, and data access bandwidth. By evaluating the FOM of each design scheme, the scheme with the highest FOM (1.085× better than the baseline) is adopted to design a neuromorphic chip. Deep multiplexing and weight-sharing technologies are used to reduce on-chip resource overhead and data access pressure. A hybrid memory design method is proposed to optimize on- and off-chip memory distribution, which reduces on-chip storage pressure and total power consumption by 92.88% and 27.86%, respectively, while avoiding the explosion of off-chip access bandwidth. The co-designed neuromorphic chip with ten cores fabricated under standard 55 nm CMOS technology has an area of 4.4 mm 2 and a core neuron density of 4.92 K/mm 2, an improvement of 3.39  âˆ¼ 30.56× compared with previous works. After deploying a full-connected and a convolution-based spiking neural network (SNN) for ECG signal recognition, the neuromorphic chip achieves 92% and 95% accuracy, respectively. This work provides a new path for developing high-density and large-scale neuromorphic chips.

An Area- and Energy-Efficient Spiking Neural Network With Spike-Time-Dependent Plasticity Realized With SRAM Processing-in-Memory Macro and On-Chip Unsupervised Learning.

In this article, we present a spiking neural network (SNN) based on both SRAM processing-in-memory (PIM) macro and on-chip unsupervised learning with Spike-Time-Dependent Plasticity (STDP). Co-design of algorithm and hardware for hardware-friendly SNN and efficient STDP-based learning methodology is used to improve area and energy efficiency. The proposed macro utilizes charge sharing of capacitors to perform fully parallel Reconfigurable Multi-bit PIM Multiply-Accumulate (RMPMA) operations. A thermometer-coded Programmable High-precision PIM Threshold Generator (PHPTG) is designed to achieve low differential non-linearity (DNL) and high linearity. In the macro, each column of PIM cells and a comparator act as a neuron to accumulate membrane potential and fire spikes. A simplified Winner Takes All (WTA) mechanism is used in the proposed hardware-friendly architecture. By combining the hardware-friendly STDP algorithm as well as the parallel Word Lines (WLs) and Processing Bit Lines (PBLs), we realize unsupervised learning and recognize the Modified National Institute of Standards and Technology (MNIST) dataset. The chip for the hardware implementation was fabricated with a 55 nm CMOS process. The measurement shows that the chip achieves a learning efficiency of 0.47 nJ/pixel, with a learning energy efficiency of 70.38 TOPS/W. This work paves a pathway for the on-chip learning algorithm in PIM with lower power consumption and fewer hardware resources.

Braille recognition by E-skin system based on binary memristive neural network.

Braille system is widely used worldwide for communication by visually impaired people. However, there are still some visually impaired people who are unable to learn Braille system due to various factors, such as the age (too young or too old), brain damage, etc. A wearable and low-cost Braille recognition system may substantially help these people recognize Braille or assist them in Braille learning. In this work, we fabricated polydimethylsiloxane (PDMS)-based flexible pressure sensors to construct an electronic skin (E-skin) for the application of Braille recognition. The E-skin mimics human touch sensing function for collecting Braille information. Braille recognition is realized with a neural network based on memristors. We utilize a binary neural network algorithm with only two bias layers and three fully connected layers. Such neural network design remarkably reduces the calculation burden and, thus, the system cost. Experiments show that the system can achieve a recognition accuracy of up to 91.25%. This work demonstrates the possibility of realizing a wearable and low-cost Braille recognition system and a Braille learning-assistance system.

A Co-Designed Neuromorphic Chip With Compact (17.9K F2) and Weak Neuron Number-Dependent Neuron/Synapse Modules.

Many efforts have been made to improve the neuron integration efficiency on neuromorphic chips, such as using emerging memory devices and shrinking CMOS technology nodes. However, in the fully connected (FC) neuromorphic core, increasing the number of neurons will lead to a square increase in synapse & dendrite costs and a high-slope linear increase in soma costs, resulting in an explosive growth of core hardware costs. We propose a co-designed neuromorphic core (SRCcore) based on the quantized spiking neural network (SNN) technology and compact chip design methodology. The cost of the neuron/synapse module in SRCcore weakly depends on the neuron number, which effectively relieves the growth pressure of the core area caused by increasing the neuron number. In the proposed BICS chip based on SRCcore, although the neuron/synapse module implements 1∼16 times of neurons and 1∼66 times of synapses, it only costs an area of 1.79 × 107 F2, which is 7.9%∼38.6% of that in previous works. Based on the weight quantization strategy matched with SRCcore, quantized SNNs achieve 0.05%∼2.19% higher accuracy than previous works, thus supporting the design and application of SRCcore. Finally, a cross-modeling application is demonstrated based on the chip. We hope this work will accelerate the development of cortical-scale neuromorphic systems.

A dynamic AES cryptosystem based on memristive neural network.

This paper proposes an advanced encryption standard (AES) cryptosystem based on memristive neural network. A memristive chaotic neural network is constructed by using the nonlinear characteristics of a memristor. A chaotic sequence, which is sensitive to initial values and has good random characteristics, is used as the initial key of AES grouping to realize "one-time-one-secret" dynamic encryption. In addition, the Rivest-Shamir-Adleman (RSA) algorithm is applied to encrypt the initial values of the parameters of the memristive neural network. The results show that the proposed algorithm has higher security, a larger key space and stronger robustness than conventional AES. The proposed algorithm can effectively resist initial key-fixed and exhaustive attacks. Furthermore, the impact of device variability on the memristive neural network is analyzed, and a circuit architecture is proposed.

[Establishment of arsenic speciation analysis method and application in rice].

Objective: A new ion exchange column technology was used to establish an efficient and sensitive method for the detection of inorganic arsenic. Methods: Based on the new As Specia Fast Column, the pretreatment methods, liquid phase separation and mass spectrometry determination conditions of inorganic arsenic in rice were optimized. Finally, arsenic compounds were separated by As Specia Fast Column and detected by liquid chromatography inductively coupled plasma mass spectrometry. The external standard method was used for quantitative analysis. The detection limit, precision and accuracy of the method were determined by measuring the content of arsenic compounds in rice samples and rice standard samples. At the same time, three Guangdong rice samples were selected as the experimental samples of this study, and 1 g of each sample was weighed and measured in parallel three times. The method was compared with the method of liquid chromatography-atomic fluorescence spectrometry (LC-AFS) and liquid chromatography-inductively coupled plasma mass spectrometry (LC-ICP-MS) in the national standard. Results: The inorganic arsenic in rice was extracted with 0.5% nitric acid solution at 65 ℃ for 15 h, and the pH was adjusted to alkaline. The mobile phase A (8 mmol/L HNO(3), 50 mmol/L NH(3)·H(2)O) and mobile phase B (40 mmol/L HNO(3), 80 mmol/L NH(3)·H(2)O) were used as the mobile phase gradient elution (93%) . Five arsenic compounds can reach baseline separation under the conditions of RF power of 1 500 W and atomization gas flow of 0.97 L/min. The detection limits ranged from 0.114 to 0.331 µg/L, and the inorganic arsenic content in rice samples ranged from 0.063 to 0.232 mg/kg. The results of determination of arsenic compounds in rice flour reference materials were all within the uncertainty range indicated by the standard. The recoveries were 86.7%~106.7%, and the precision was 1.9%-12.5%. Compared with national standards, the results of determination of arsenate in rice were relatively close (using this method, LC-AFS, LC-ICP-MS to detect the content of arsenate in rice samples 1 was 0.231, 0.226, 0.236 mg/kg, respectively). However, due to insufficient sensitivity, the national standard method is difficult to detect low levels of arsenic compounds (Arsenobetaine was not detected in rice sample 1). The method can detect the content of arsenobetaine in rice sample 1 was 0.023 mg/kg. Conclusion: The established method can meet the requirements of inorganic arsenic determination in rice, and it is more rapid and accurate than the current national standard. It can better monitor and evaluate the content of i-As in rice, and provide accurate data for comprehensively grasping and evaluating the safety of rice consumption of residents.

Handwritten-Digit Recognition by Hybrid Convolutional Neural Network based on HfO2 Memristive Spiking-Neuron.

Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1010-1011 neurons is still of great challenge. Recently, phase change memristor neuron has been proposed to realize a human-brain level neural network operating at a high speed while consuming a small amount of power and having a high integration density. Although memristor neuron can be scaled down to nanometer, integration of 1010-1011 neurons still faces many problems in circuit complexity, chip area, power consumption, etc. In this work, we propose a CMOS compatible HfO2 memristor neuron that can be well integrated with silicon circuits. A hybrid Convolutional Neural Network (CNN) based on the HfO2 memristor neuron is proposed and constructed. In the hybrid CNN, one memristive neuron can behave as multiple physical neurons based on the Time Division Multiplexing Access (TDMA) technique. Handwritten digit recognition is demonstrated in the hybrid CNN with a memristive neuron acting as 784 physical neurons. This work paves the way towards substantially shrinking the amount of neurons required in hardware and realization of more complex or even human cerebral cortex level memristive neural networks.

The dynamic metabolomic changes throughout mouse epididymal lumen fluid potentially contribute to sperm maturation.

Epididymal lumen fluids are directly responsible for sperm maturation. However, very little is known about the molecular details of small molecule metabolites in the epididymal lumen fluids until now. Here we identified and compared the metabolic profiles of mouse caput and cauda epididymal lumen fluids using GC-MS technique. Among 236 metabolites identified in caput and cauda epididymis, 36 were significantly enriched in caput epididymis while 18 were significantly enriched in cauda epididymis. Pathway analysis identified ascorbate and aldarate metabolism and beta-alanine metabolism as most relevant pathways in caput and cauda epididymis, respectively. Ascorbate, dehydroascorbic acid and beta-alanine associated with these two pathways were firstly reported in mouse epididymal lumen fluids and might play important roles in sperm maturation.

A MoS2-based coplanar neuron transistor for logic applications.

The human brain is an extremely complex system of 1010-1011 neurons. To construct brain-like neuromorphic hardware, the neuron unit should be implemented effectively. Here, we report a neuron transistor based on a MoS2 flake, which has summation and threshold functions similar to biological neurons and may act as a basic neuron unit in neuromorphic hardware. The neuron transistor is composed of a floating gate and two control gates. A heavily doped silicon substrate serves as the floating gate, while the two control gates are capacitively coupled with the floating gate. The neuron transistor can be well controlled by the two control gates individually or simultaneously. The drain current can be modulated by the input voltages at the control gates. While the current response of the neuron transistor has a large dependence on the magnitude of the input signal, it shows little dependence on the frequency of the input signal. To demonstrate the potential neuromorphic application of the neuron transistor, functions including abacus-like function, AND logic and OR logic are realized in the neuron transistor.

Highly spectrum-selective ultraviolet photodetector based on p-NiO/n-IGZO thin film heterojunction structure.

Ultraviolet photodetector with p-n heterojunction is fabricated by magnetron sputtering deposition of n-type indium gallium zinc oxide (n-IGZO) and p-type nickel oxide (p-NiO) thin films on ITO glass. The performance of the photodetector is largely affected by the conductivity of the p-NiO thin film, which can be controlled by varying the oxygen partial pressure during the deposition of the p-NiO thin film. A highly spectrum-selective ultraviolet photodetector has been achieved with the p-NiO layer with a high conductivity. The results can be explained in terms of the "optically-filtering" function of the NiO layer.

Associative memory realized by a reconfigurable memristive Hopfield neural network.

Although synaptic behaviours of memristors have been widely demonstrated, implementation of an even simple artificial neural network is still a great challenge. In this work, we demonstrate the associative memory on the basis of a memristive Hopfield network. Different patterns can be stored into the memristive Hopfield network by tuning the resistance of the memristors, and the pre-stored patterns can be successfully retrieved directly or through some associative intermediate states, being analogous to the associative memory behaviour. Both single-associative memory and multi-associative memories can be realized with the memristive Hopfield network.

Magnetic resonance elastography with twin pneumatic drivers for wave compensation.

MR Elastography is a new technique using conventional MRI system to assess the elastic properties of tissues. When using pneumatic driver, usually one driver was put at one place of tissue. But the shear wave generated by one pneumatic driver cannot illuminate the large area due to the attenuation. So we use two pneumatic drivers driven synchronously to generate interference shear wave in our experiments. The results from the phantom study show the interference wave pattern generated by the twin pneumatic drivers can compensate the attenuation of the shear wave when propagating in phantom. Also, a finite element modeling was used to simulate twin pneumatic driver datasets. It is hoped that by twin pneumatic drivers, we can illuminate the whole brain; the liver and large areas in-vivo. Further study will be conducted with the twin pneumatic drivers in ex-vivo and in-vivo studies.

Protein adsorption, fibroblast activity and antibacterial properties of poly(3-hydroxybutyric acid-co-3-hydroxyvaleric acid) grafted with chitosan and chitooligosaccharide after immobilized with hyaluronic acid.

Poly(3-hydroxybutyric acid-co-3-hydroxyvaleric acid) (PHBV) membrane was treated with ozone and grafted with acrylic acid. The resulting membranes were further grafted with chitosan (CS) or chitooligosaccharide (COS) via esterification. Afterward hyaluronic acid (HA) was immobilized onto CS- or COS-grafting membranes. The antibacterial activity of CS and COS against Staphylococus aureus, Escherichia coli, and Pseudomonas aeruginosa was preserved after HA immobilization. Among them, CS-grafted PHBV membrane showed higher antibacterial activity than COS-grafted PHBV membrane. In addition, after CS- or COS-grafting, the L929 fibroblasts attachment and protein adsorption were improved, while the cell number was decrease. After immobilizing HA, the cell proliferation was promoted, the protein adsorption was decreased, and the cell attachment was slightly lower than CS- or COS-grafting PHBV.

The development of nao li shen and its clinical application.

The traditional Chinese medicine Nao Li Shen (containing ginseng, gastrodia tuber, chuanxiong rhizome and red sage root) is used in craniocerebral injury, cervical spondylosis and cerebrovascular diseases. The preparation, as an orally administered liquid, was tested in Mongolian gerbils and shown to increase tolerance to ischaemia and anoxia. Clinical use of the preparation resulted in improvement in 96% of 202 patients, as judged by right cerebral blood flow, TCD and CT examination. We conclude that Nao Li Shen has a positive curative effect upon craniocerebral injury and sequelae of cerebrovascular diseases.