• The Korea Journal of Herbology
• /
• v.28 no.5
• /
• pp.69-77
• /
• 2013

Objectives : The purpose of this study was to characterize the effect of the Ethanolic extract of Stachys sieboldii and Lycopus lucidus on the learning and memory impairments induced by scopolamine. Methods : The genetic difference of Stachys sieboldii and Lycopus lucidus were observed with RAPD analysis. The cognition-enhancing effect of Stachys sieboldii and Lycopus lucidus was investigated using a passive avoidance test, Y-maze test and the Morris water maze test in mice. Drug-induced amnesia was induced by treating animals with scopolamine (1 mg/kg, i.p.). Results : As a result of RAPD analysis, Stachys sieboldii and Lycopus lucidus Radix was found to be genetically different and The results of learning memory analysis showed that Stachys sieboldii extract-treated group (500 mg/kg, p.o.) and the tacrine-treated group (10 mg/kg, p.o.) significantly ameliorated scopolamine-induced amnesia based on the Passive avoidance Y-maze test and Water maze test. And these results are same manner in DPPH radical scavenger effect and Acetylcholineseterase inhibition effect. These results suggest that Stachys sieboldii extract maybe a useful cognitive impairment treatment, and its beneficial effects are depending on the origin plants. Conclusions : Commercially available Stachys sieboldii Radix consists of two original plant, one of them people misuse. To clarify the origin of the plant Memory tests were performed. These results suggest that 80% Ethanol extract of Stachys sieboldii showed significant anti-amnestic and cognitive-enhancing activities related to the memory processes, and these activities were parallel to treatment duration and dependent of the learning models.

• Toxicological Research
• /
• v.24 no.3
• /
• pp.183-188
• /
• 2008

The behavioral phenotypes of out-bred ICR mice were compared with those of in-bred C57BL/6 and BALB/c mice. In particular, this study examined the locomotor activity and two forms of hippocampus-dependent learning paradigms, passive avoidance and object recognition memory. The basal open-field activity of the ICR strain was greater than that of the C57BL/6 and BALB/c strains. In the passive avoidance task, all the mice showed a significant increase in the cross-over latency when tested 24 hours after training. The strength of memory retention in the ICR mice was relatively weak and measurable, as indicated by the shorter cross-over latency than the C57BL/6 and BALB/c mice. In the object recognition memory test, all strains had a significant preference for the novel object during testing. The index for the preference of a novel object was lower for the ICR and BALB/c mice. Nevertheless, the variance and the standard deviation in these strains were comparable. Overall, these results confirm the strain differences on locomotor activity and hippocampus-dependent learning and memory in mice.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.1
• /
• pp.90-94
• /
• 2020

Artificial intelligence (AI) is software that learns large amounts of data and provides the desired results for certain patterns. In other words, learning a large amount of data is very important, and the role of memory in terms of computing systems is important. Massive data means wider bandwidth, and the design of the memory system that can provide it becomes even more important. Providing wide bandwidth in AI systems is also related to power consumption. AlphaGo, for example, consumes 170 kW of power using 1202 CPUs and 176 GPUs. Since more than 50% of the consumption of memory is usually used by system chips, a lot of investment is being made in memory technology for AI chips. MRAM, PRAM, ReRAM and Hybrid RAM are mainly studied. This study presents various memory technologies that are being studied in artificial intelligence chip design. Especially, MRAM and PRAM are commerciallized for the next generation memory. They have two significant advantages that are ultra low power consumption and nearly zero leakage power. This paper describes a comparative analysis of the four representative new memory technologies.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.43 no.4 s.310
• /
• pp.37-45
• /
• 2006

In this paper, we propose the development of MHLA(Modulatory Hippocampus Learning Algorithm) which remodel a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 3 steps system(DG, CA3, CAl) and improve speed of learning by addition of modulator to long-term memory learning. In hippocampal system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labelled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CAI region, convergence of connection weight which is used long-term memory is learned fast by neural networks which is applied modulator. To measure performance of MHLA, PCA(Principal Component Analysis) is applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by MHLA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• BMB Reports
• /
• v.49 no.4
• /
• pp.199-200
• /
• 2016

Gene regulation in the brain is essential for long-term plasticity and memory formation. Despite this established notion, the quantitative translational map in the brain during memory formation has not been reported. To systematically probe the changes in protein synthesis during memory formation, our recent study exploited ribosome profiling using the mouse hippocampal tissues at multiple time points after a learning event. Analysis of the resulting database revealed novel types of gene regulation after learning. First, the translation of a group of genes was rapidly suppressed without change in mRNA levels. At later time points, the expression of another group of genes was downregulated through reduction in mRNA levels. This reduction was predicted to be downstream of inhibition of ESR1 (Estrogen Receptor 1) signaling. Overexpressing Nrsn1, one of the genes whose translation was suppressed, or activating ESR1 by injecting an agonist interfered with memory formation, suggesting the functional importance of these findings. Moreover, the translation of genes encoding the translational machineries was found to be suppressed, among other genes in the mouse hippocampus. Together, this unbiased approach has revealed previously unidentified characteristics of gene regulation in the brain and highlighted the importance of repressive controls.

• Journal of Microbiology and Biotechnology
• /
• v.23 no.12
• /
• pp.1779-1784
• /
• 2013

Although enzyme-hydrolyzed silk fibroin has been reported to enhance cognitive function before, it has been still unknown which peptides can improve memory. Here we report that amino acid sequences of three novel peptides were identified from fibroin hydrolysate. Fibroin hydrolysate was obtained by hydrolysis with protease after partial hydrolysis with 5M $CaCl_2$. Synthesized peptides derived from these sequences improved scopolamine-induced memory impairments in mice. We confirmed this hydrolysate had effects that improved learning and memory abilities by performing the Rey-Kim test. From this hydrolysate of silk fibroin, amino acid sequences of eight peptides were identified by LC-MS/MS. Three peptides (GAGAGTGSSGFGPY, GAGAGSGAGSGAGAGSGAGAGY, and SGAGSGAGAGSGAGAGSGA) were synthesized to investigate whether they could improve memory. Passive avoidance test and Morris water maze test were performed, and all peptides showed memory-enhancing abilities on scopolamine-induced memory impairments in mice. In this study, we identified three novel peptides that could improve memory, and that silk fibroin hydrolysate was a mixture of various active peptides that could enhance memory.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.47 no.3
• /
• pp.195-203
• /
• 2014

We investigated the effect low salt (2 or 4% salt) concentrations jeotgal made from Todarodes pacificus on the learning and memory impairments in scopolamine-induced (2 mg/kg, i.p.) dementia rats. Rats treated with oral BF-7 (200 mg/kg, p.o.) as a positive control and Todarodes pacificus jeotgal had significantly reduced scopolamine-induced memory deficits in the passive avoidance test. The Morris water maze test or treatment with 2% salt jeotgal made from Todarodes pacificus significantly ameliorated the scopolamine-induced memory deficits in the formation of long- and short-term memory. The acetylcholine content and acetylcholinesterase acitivity paralleled the results of the behavior experiment. There were no significant differences in the brain acetylcholine contents of the experimental groups, while the brain acetylcholine content of the group treated with 2% salt Todarodes pacificus jeotgal was higher than that of the control group. The inhibitory effect of 2% salt jeotgal made from Todarodes pacificus on the acetylcholinesterase activity in the brain was lower than that of the control group. These trends were similar to those of the gamma-aminobutyric acid content. We suggest that Todarodes pacificus jeotgal enhances learning memory and cognitive function by regulating cholinergic enzymes.

• Structural Monitoring and Maintenance
• /
• v.5 no.1
• /
• pp.51-65
• /
• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.482-482
• /
• 2000

The essential requirements of neural network for human skill transfer are fast convergence, high storage capacity, and strong noise immunity. Bidirectional associative memory(BAM) suffering from low storage capacity and abundance of spurious memories is rarely used for skill transfer application though it has fast and wide association characteristics for visual data. This paper suggests generalization of classical BAM structure and new learning algorithm which uses supervised learning to guarantee perfect recall starting with correlation matrix. The generalization is validated to accelerate convergence speed, to increase storage capacity, to lessen spurious memories, to enhance noise immunity, and to enable multiple association using simulation work.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.11
• /
• pp.4246-4267
• /
• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.