Hierarchical Temporal Memory using Memristor Networks: A Survey

This paper presents a survey of the currently available hardware designs for implementation of the human cortex inspired algorithm, Hierarchical Temporal Memory (HTM). In this review, we focus on the state of the art advances of memristive HTM implementation and related HTM applications. With the advent of edge computing, HTM can be a potential algorithm to implement on-chip near sensor data processing. The comparison of analog memristive circuit implementations with the digital and mixed-signal solutions are provided. The advantages of memristive HTM over digital implementations against performance metrics such as processing speed, reduced on-chip area and power dissipation are discussed. The limitations and open problems concerning the memristive HTM, such as the design scalability, sneak currents, leakage, parasitic effects, lack of the analog learning circuits implementations and unreliability of the memristive devices integrated with CMOS circuits are also discussed

Introduction to Memristive HTM Circuits

Hierarchical temporal memory (HTM) is a cognitive learning algorithm intended to mimic the working principles of neocortex, part of the human brain said to be responsible for data classification, learning, and making predictions. Based on the combination of various concepts of neuroscience, it has already been shown that the software realization of HTM is effective on different recognition, detection, and prediction making tasks. However, its distinctive features, expressed in terms of hierarchy, modularity, and sparsity, suggest that hardware realization of HTM can be attractive in terms of providing faster processing speed as well as small memory requirements, on-chip area, and total power consumption. Despite there are few works done on hardware realization for HTM, there are promising results which illustrate effectiveness of incorporating an emerging memristor device technology to solve this open-research problem. Hence, this chapter reviews hardware designs for HTM with specific focus on memristive HTM circuits

L-NAME-induced preeclampsia: correction of functional disorders of the hemostasis system with resveratrol and nicorandil

Preeclampsia is a formidable disease of the second half of pregnancy, leading to severe complications, including disability and even death. Many authors have recognized the correlation between the severity of preeclampsia and the degree of disturbances in the hemostasis system. In this regard, the objective of this study was to assess inhibition of platelet aggregation and the possibility of its correction with resverаtrol and nicorand

Search and evaluation of pharmacodynamic and pharmacokinetic parameters of selective blocker of TRPA₁ ion channels from the group of substituted pyrazinopyrimidinones

Search considerations evaluation of pharmacodynamic and pharmacokinetic parameters of selective blocker of TRPA₁ ion channels from the group of substituted pyrazinopyrimidinone

rAAV expressing recombinant antibody for emergency prevention and long-term prophylaxis of COVID-19

IntroductionNumerous agents for prophylaxis of SARS-CoV-2-induced diseases are currently registered for the clinical use. Formation of the immunity happens within several weeks following vaccine administration which is their key disadvantage. In contrast, drugs based on monoclonal antibodies, enable rapid passive immunization and therefore can be used for emergency pre- and post-exposure prophylaxis of COVID-19. However rapid elimination of antibody-based drugs from the circulation limits their usage for prolonged pre-exposure prophylaxis.MethodsIn current work we developed a recombinant adeno-associated viral vector (rAAV), expressing a SARS-CoV-2 spike receptor-binding domain (RBD)-specific antibody P2C5 fused with a human IgG1 Fc fragment (P2C5-Fc) using methods of molecular biotechnology and bioprocessing.Results and discussionsA P2C5-Fc antibody expressed by a proposed rAAV (rAAV-P2C5-Fc) was shown to circulate within more than 300 days in blood of transduced mice and protect animals from lethal SARS-CoV-2 virus (B.1.1.1 and Omicron BA.5 variants) lethal dose of 105 TCID50. In addition, rAAV-P2C5-Fc demonstrated 100% protective activity as emergency prevention and long-term prophylaxis, respectively. It was also demonstrated that high titers of neutralizing antibodies to the SARS-CoV-2 virus were detected in the blood serum of animals that received rAAV-P2C5-Fc for more than 10 months from the moment of administration.Our data therefore indicate applicability of an rAAV for passive immunization and induction of a rapid long-term protection against various SARS-CoV-2 variants

A BRUTE-FORCE CNN MODEL SELECTION FOR ACCURATE CLASSIFICATION OF SENSORIMOTOR RHYTHMS IN BCIS

The ultimate goal of Brain-Computer Interface (BCI) research is to enable individuals to interact with their environment by translating their mental imagery. In this regard, a salient issue is the identification of brain activity patterns that can be used to classify intention. Using Electroencephalographic (EEG) signals as archetypical, this classification problem generally possesses two stages: (i) extracting features from collected EEG waveforms; and (ii) constructing a classifier using extracted features. With the advent of deep learning, however, the former stage is generally absorbed into the latter. Nevertheless, the burden has now shifted from trying a number of feature extraction methods to tuning a large number of hyperparameters and architectures. Among existing deep learning architectures used in BCI, Convolutional Neural Networks (CNN) have become an attractive choice. Most of the existing studies that use these networks are based on well-known architectures such as AlexNet or ResNet, use the domain knowledge to construct the final architecture or have an unclear strategy deployed for model selection. This raises the question as to whether constructing accurate CNN-based classifiers is possible using a principled model selection, with the most straightforward one being the brute-force search or, alternatively, experience and developing high intuition regarding hyperparameters combined with an ad hoc approach is the most prudent way to go about designing them. To this end, in this paper, we first define a space of hyperparameters restricted by our computing power. Then we show that an exhaustive search within this limited space of CNN hyperparameters leads to accurate classification of sensorimotor rhythms that arise during motor imagery tasks

Memristor-based Synaptic Sampling Machines [Article]

https://arxiv.org/ftp/arxiv/papers/1808/1808.00679.pdfSynaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar structure with the synaptic sampling cell (SSC) being used as a basic stochastic unit. The increase in the edge computing devices in the Internet of things era, drives the need for hardware acceleration for data processing and computing. The computational considerations of the processing speed and possibility for the real-time realization pushes the synaptic sampling algorithm that demonstrated promising results on software for hardware implementation