scholarly journals A Neural Model of Schemas and Memory Consolidation

2018 ◽  
Author(s):  
Tiffany Hwu ◽  
Jeffrey L. Krichmar
Keyword(s):  
Neural Network ◽  
Memory Consolidation ◽  
Hebbian Learning ◽  
Dynamic Environment ◽  
Neural Model ◽  
Vital Role ◽  
Learning To Learn ◽  
New Information ◽  
Context Dependent

AbstractThe ability to behave differently according to the situation is essential for survival in a dynamic environment. This requires past experiences to be encoded and retrieved alongside the contextual schemas in which they occurred. The complementary learning systems theory suggests that these schemas are acquired through gradual learning via the neocortex and rapid learning via the hippocampus. However, it has also been shown that new information matching a preexisting schema can bypass the gradual learning process and be acquired rapidly, suggesting that the separation of memories into schemas is useful for flexible learning. While there are theories of the role of schemas in memory consolidation, we lack a full understanding of the mechanisms underlying this function. For this reason, we created a biologically plausible neural network model of schema consolidation that studies several brain areas and their interactions. The model uses a rate-coded multilayer neural network with contrastive Hebbian learning to learn context-specific tasks. Our model suggests that the medial prefrontal cortex supports context-dependent behaviors by learning representations of schemas. Additionally, sparse random connections in the model from the ventral hippocampus to the hidden layers of the network gate neuronal activity depending on their involvement within the current schema, thus separating the representations of new and prior schemas. Contrastive Hebbian learning may function similarly to oscillations in the hippocampus, alternating between clamping and unclamping the output layer of the network to drive learning. Lastly, the model shows the vital role of neuromodulation, as a neuromodulatory area detects the certainty of whether new information is consistent with prior schemas and modulates the speed of memory encoding accordingly. Along with the insights that this model brings to the neurobiology of memory, it further provides a basis for creating context-dependent memories while preventing catastrophic forgetting in artificial neural networks.

scholarly journals COVID-19: Role of Deep Learning and Cloud Through Identification of Kidney, Pancreas and Intestine

2021 ◽  
Author(s):  
Arulmozhiselvan L ◽  
Uma E ◽  
Jayasri R
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Acute Pancreatitis ◽  
Vital Role ◽  
Patient Records ◽  
Smart System ◽  
Warning Message ◽  
User Data ◽  
Post Infectious

Worldwide human health and economic has been affected due to the ongoing pandemic of corona virus (COVID-19). The major COVID-19 challenges are prevention, monitoring and FDA approved vaccines. IOT and cloud computing play vital role in epidemic prevention and blocking COVID-19 transmission. Mostly lungs and hearts are affected. Other than lungs many parts are affected which are not considered as prominent conversational cue. In this paper, we have proposed smart system that is effective through detection of pancreas, kidney and intestine. It detects acute pancreatitis, protein leak, microscopic blood leak, post infectious dysmotility and gastrointestinal bleeding. The data from the edge devices are collected and mapped into the cloud layer. The cloud consists of COVID-19 patients medical records which compare the user data with the existing patient records. Once the data matches it sends warning message to the user regarding the result of affected parts. Based on the result from KPI system, it analyzes with all data and using deep Convolutional Neural Network (CNN) it classifies whether the pancreas, kidney and intestine are affected or not due to COVID-19.

scholarly journals Detecting Generic Music Features with Single Layer Feedforward Network using Unsupervised Hebbian Computation

2020 ◽  
Vol 12 (2) ◽  
pp. 1-20
Author(s):  
Sourav Das ◽  
Anup Kumar Kolya
Keyword(s):  
Neural Network ◽  
Feature Detection ◽  
Feature Recognition ◽  
Hebbian Learning ◽  
Feature Learning ◽  
Single Layer ◽  
Feedforward Network ◽  
New Information ◽  
Unsupervised Training ◽  
And Performance

In this work, the authors extract information on distinct baseline features from a popular open-source music corpus and explore new recognition techniques by applying unsupervised Hebbian learning techniques on our single-layer neural network using the same dataset. They show the detailed empirical findings to simulate how such an algorithm can help a single layer feedforward network in training for music feature learning as patterns. The unsupervised training algorithm enhances the proposed neural network to achieve an accuracy of 90.36% for successful music feature detection. For comparative analysis against similar tasks, they put their results with the likes of several previous benchmark works. They further discuss the limitations and thorough error analysis of the work. They hope to discover and gather new information about this particular classification technique and performance, also further understand future potential directions that could improve the art of computational music feature recognition.

scholarly journals Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1832
Author(s):  
Wojciech Sitek ◽  
Jacek Trzaska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vital Role ◽  
Materials Engineering ◽  
The Neural Network ◽  
Critical Issues ◽  
Artificial Neural ◽  
The Individual

Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.

Do Teachers Facilitate the Strategies their Pupils Need?

2017 ◽  
Vol 12 (1) ◽  
pp. 1-11
Author(s):  
Ivana Hrozková
Keyword(s):  
Foreign Language ◽  
Teaching And Learning ◽  
Vital Role ◽  
Learning To Learn ◽  
Unique Role ◽  
Language Curriculum ◽  
Strategic Teaching ◽  
Study Results ◽  
Learner Strategies

AbstractLearner strategies play an important and unique role in second or foreign language teaching and learning. Knowledge of effective steps and techniques helps pupils reach better study results and become more independent and autonomous learners. The role of a teacher shifts and changes in autonomous learning; rather than a teacher, he or she is a consultant, helper and facilitator of learner strategies. The study brings an insight into strategic teaching of 13 Czech teachers who taught 202 pupils with completed primary education and who also focused on the strategies teachers facilitate, but their pupils do not apply. The study aims at a model of strategy facilitation which helps teachers implement strategy-based teaching into a foreign language curriculum. The article also aspires to address teachers of different subjects as giving advice on learning to learn plays a vital role in teaching. Furthermore, learner strategies are an important part of competence to learn, which helps teachers to understand why some learners are more successful than their peers.

scholarly journals Stochastic Consolidation of Lifelong Memory

2021 ◽  
Author(s):  
Nimrod Shaham ◽  
Jay Chandra ◽  
Gabriel Kreiman ◽  
Haim Sompolinsky
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Decay Time ◽  
Computational Neuroscience ◽  
Memory Consolidation ◽  
Hebbian Learning ◽  
Basin Of Attraction ◽  
Memory Impairments ◽  
The Brain ◽  
Continual Learning

Humans have the remarkable ability to continually store new memories, while maintaining old memories for a lifetime. How the brain avoids catastrophic forgetting of memories due to interference between encoded memories is an open problem in computational neuroscience. Here we present a model for continual learning in a recurrent neural network combining Hebbian learning, synaptic decay and a novel memory consolidation mechanism. Memories undergo stochastic rehearsals with rates proportional to the memory's basin of attraction, causing self-amplified consolidation, giving rise to memory lifetimes that extend much longer than synaptic decay time, and capacity proportional to a power of the number of neurons. Perturbations to the circuit model cause temporally-graded retrograde and anterograde deficits, mimicking observed memory impairments following neurological trauma.

scholarly journals International Society of Nephrology-Hydration and Kidney Health Initiative - Expanding Research and Knowledge

2016 ◽  
Vol 68 (Suppl. 2) ◽  
pp. 32-34
Author(s):  
Louise M. Moist ◽  
William F. Clark ◽  
Luca Segantini ◽  
Sandrine Damster ◽  
Laurent Le Bellego ◽  
...  
Keyword(s):  
International Society ◽  
World Literature ◽  
Vital Role ◽  
Medical Community ◽  
New Information ◽  
Research Initiative ◽  
Advance Research ◽  
Research Initiatives ◽  
Kidney Health

The purpose of this manuscript is to describe a collaborative research initiative to explore the role of hydration in kidney health. Our understanding of the effects of hydration in health and disease is surprisingly limited, particularly when we consider the vital role of hydration in basic human physiology. Recent initiatives and research outcomes have challenged the global medical community to expand our knowledge about hydration, including the differences between water, sugared beverages and other consumables. Identification of the potential mechanisms contributing to the benefits of hydration has stimulated the global nephrology community to advance research regarding hydration for kidney health. Hydration and kidney health has been a focus of research for several research centers with a rapidly expanding world literature and knowledge. The International Society of Nephrology has collaborated with Danone Nutricia Research to promote development of kidney research initiatives, which focus on the role of hydration in kidney health and the global translation of this new information. This initiative supports the use of existing data in different regions and countries to expand dialogue among experts in the field of hydration and health, and to increase scientific interaction and productivity with the ultimate goal of improving kidney health.

scholarly journals Three Melanin Pathway Genes, TH, yellow, and aaNAT, Regulate Pigmentation in the Twin-Spotted Assassin Bug, Platymeris biguttatus (Linnaeus)

2019 ◽  
Vol 20 (11) ◽  
pp. 2728 ◽  
Author(s):  
Yinqiao Zhang ◽  
Hu Li ◽  
Juan Du ◽  
Junzheng Zhang ◽  
Jie Shen ◽  
...  
Keyword(s):  
Vital Role ◽  
Black Pigment ◽  
White Pigment ◽  
New Information ◽  
Hemimetabolous Insect ◽  
Survival And Reproduction ◽  
Yellow Coloration ◽  
Assassin Bug ◽  
Melanin Pathway

Pigmentation plays a vital role in insect survival and reproduction. Many melanin pathway genes have been studied in holometabolous insects; however, they have only been studied in two hemimetabolous insect genera, Oncopeltus and Periplaneta. Here we analyzed three melanin pathway genes (TH, yellow, and aaNAT) using RNA interference (RNAi) in another hemimetabolous insect, namely the twin-spotted assassin bug, Platymeris biguttatus. TH was highly expressed in freshly molted nymphs and adults. TH RNAi resulted in a complete loss of black pigment, with yellow coloration maintained. Therefore, black pigment in this assassin bug is solely generated from the melanin pathway, whereas yellow pigment is generated from other unknown pigmentation pathways. yellow and aaNAT were highly expressed in the white spot of the hemelytra. Downregulation of yellow caused a brown phenotype with high mortality, indicating an important role of yellow functions in cuticle formation and in the process of converting melanin from brown to black. Interestingly, aaNAT RNAi caused not only loss of white pigment, but also loss of yellow and red pigments. This phenotype of aaNAT has not been reported in other insects. Our results provide new information for understanding the melanin pathway in which aaNAT is essential for the formation of colorless patterns.

scholarly journals WAVELET BASED FEATURE EXTRACTOR AND ANN BASED CLASSIFIER FOR OPTIMAL ECG INTERPRETATION

2014 ◽  
pp. 62-65
Author(s):  
RITU NAZNEEN ARA BEGUM ◽  
AMBALIKA SHARMA
Keyword(s):  
Neural Network ◽  
Automatic Detection ◽  
Vital Role ◽  
The Body ◽  
Cardiac Abnormality ◽  
Qrs Complex ◽  
Feature Extractor ◽  
Ecg Interpretation ◽  
Wave Shapes

The heart plays the most vital role of supplying nutrients and oxygen in any organism. Any abnormality in its function renders the body to many complications which may sometimes even lead to death. Hence, timely and early diagnosis of any abnormality is extremely important. Another requirement of the hour is the Automatic detection. Several techniques have been developed till date, but efficiency achieved so far leaves room for improvement. This paper also, presents a technique that aims at automatic detection of cardiac abnormality using an Artificial Neural Network. The detection is done on the basis of the wave shapes of different QRS complexes for different arrhythmias which are extracted from the ECG beats using Wavelet Transform. As the Daubechies wavelets are similar in shape to the QRS complex of the ECG, db4 has been used in the above context. The performance accuracies achieved for training, testing known data and unknown data have been found to be 99.7%, 99.2% and 96.2% respectively. The MIT-BIH database has been used for the present study and an altogether of seven different beats have been used for classification.

scholarly journals Rehearsal initiates systems memory consolidation, sleep makes it last

2019 ◽  
Vol 5 (4) ◽  
pp. eaav1695 ◽  
Author(s):  
L. Himmer ◽  
M. Schönauer ◽  
D. P. J. Heib ◽  
M. Schabus ◽  
S. Gais
Keyword(s):  
Memory Consolidation ◽  
Posterior Parietal Cortex ◽  
Memory Systems ◽  
Long Term Memory ◽  
Systems Consolidation ◽  
New Information ◽  
Study Session ◽  
Posterior Parietal

After encoding, memories undergo a transitional process termed systems memory consolidation. It allows fast acquisition of new information by the hippocampus, as well as stable storage in neocortical long-term networks, where memory is protected from interference. Whereas this process is generally thought to occur slowly over time and sleep, we recently found a rapid memory systems transition from hippocampus to posterior parietal cortex (PPC) that occurs over repeated rehearsal within one study session. Here, we use fMRI to demonstrate that this transition is stabilized over sleep, whereas wakefulness leads to a reset to naïve responses, such as observed during early encoding. The role of sleep therefore seems to go beyond providing additional rehearsal through memory trace reactivation, as previously thought. We conclude that repeated study induces systems consolidation, while sleep ensures that these transformations become stable and long lasting. Thus, sleep and repeated rehearsal jointly contribute to long-term memory consolidation.

Asiana Airlines Flight 214

2014 ◽  
Vol 4 (2) ◽  
pp. 113-121 ◽  
Author(s):  
Stephanie Chow ◽  
Stephen Yortsos ◽  
Najmedin Meshkati
Keyword(s):  
Human Factors ◽  
Aviation Safety ◽  
Cultural Factors ◽  
Vital Role ◽  
Cultural Issues ◽  
Flight Crew ◽  
Pilot Performance ◽  
Engineering Designs ◽  
Flight Deck

This article focuses on a major human factors–related issue that includes the undeniable role of cultural factors and cockpit automation and their serious impact on flight crew performance, communication, and aviation safety. The report concentrates on the flight crew performance of the Boeing 777–Asiana Airlines Flight 214 accident, by exploring issues concerning mode confusion and autothrottle systems. It also further reviews the vital role of cultural factors in aviation safety and provides a brief overview of past, related accidents. Automation progressions have been created in an attempt to design an error-free flight deck. However, to do that, the pilot must still thoroughly understand every component of the flight deck – most importantly, the automation. Otherwise, if pilots are not completely competent in terms of their automation, the slightest errors can lead to fatal accidents. As seen in the case of Asiana Flight 214, even though engineering designs and pilot training have greatly evolved over the years, there are many cultural, design, and communication factors that affect pilot performance. It is concluded that aviation systems designers, in cooperation with pilots and regulatory bodies, should lead the strategic effort of systematically addressing the serious issues of cockpit automation, human factors, and cultural issues, including their interactions, which will certainly lead to better solutions for safer flights.

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