GLOBAL ASYMPTOTIC STABILITY OF A CLASS OF DYNAMICAL NEURAL NETWORKS

2003 ◽  
Vol 13 (01) ◽  
pp. 47-53 ◽  
Author(s):  
ANKE MEYER-BÄSE ◽  
SERGEI S. PILYUGIN
Keyword(s):  
Short Term Memory ◽  
Neural System ◽  
Short Term ◽  
Slow Dynamic ◽  
Biologically Relevant ◽  
Synaptic Modification ◽  
Slow Part ◽  
Flow Invariance ◽  
Dynamical Neural Networks ◽  
Dynamic Variables

The dynamics of cortical cognitive maps developed by self–organization must include the aspects of long and short–term memory. The behavior of the network is such characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural biologically relevant system. We present new stability conditions for analyzing the dynamics of a biological relevant system with different time scales based on the theory of flow invariance. We prove the existence and uniqueness of the equilibrium, and give a quadratic–type Lyapunov function for the flow of a competitive neural system with fast and slow dynamic variables and thus prove the global stability of the equilibrium point.

Singular Perturbation Analysis of Competitive Neural Networks with Different Time Scales

1996 ◽  
Vol 8 (8) ◽  
pp. 1731-1742 ◽  
Author(s):  
Anke Meyer-Bäse ◽  
Frank Ohl ◽  
Henning Scheich
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Equilibrium Points ◽  
Main Idea ◽  
Neural System ◽  
Slow Dynamic ◽  
Singular Perturbation Analysis ◽  
Slow Part ◽  
Stability Method ◽  
Dynamic Variables

The dynamics of complex neural networks must include the aspects of long- and short-term memory. The behavior of the network is characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural system. The main idea of this paper is to apply a stability analysis method of fixed points of the combined activity and weight dynamics for a special class of competitive neural networks. We present a quadratic-type Lyapunov function for the flow of a competitive neural system with fast and slow dynamic variables as a global stability method and a modality of detecting the local stability behavior around individual equilibrium points.

scholarly journals Enormous Information Examination using Big Data in a Distributed Environment with Profound Learning of Next Generation Interruption Identification Framework Enhancement

2019 ◽  
Vol 9 (1) ◽  
pp. 1779-1784
Keyword(s):  
Big Data ◽  
Short Term Memory ◽  
Neural System ◽  
Distributed Environment ◽  
Short Term ◽  
Term Memory ◽  
Identification Rate ◽  
Long Short Term Memory ◽  
Future Vision ◽  
Spark Framework

With the developing utilization of data innovation in all life areas, hacking has turned out to be more contrarily powerful than any other time in recent memory. Additionally, with creating advances, assaults numbers are developing exponentially like clockwork and become progressively refined so conventional I.D.S ends up wasteful recognizing them. We accomplish those outcomes by utilizing Networking Chabot, a profound intermittent neural system: Long Short Term Memory (L.S.T.M) [2]over Apache Spark Framework that has a contribution of stream traffic and traffic conglomeration and the yield is a language of two words, typical or strange. The new and proposed blending ideas of the language are preparing, relevant examination, circulated profound adapting, huge information, and oddity discovery of stream investigation. We propose a model that portrays the system dynamic typical conduct from an arrangement of a great many parcels inside their unique circumstance and examines them in close to constant to identify point, aggregate and relevant inconsistencies. The examination shows lower false positive, higher identification rate and better point abnormalities location. With respect to demonstrate of relevant and aggregate oddities identification, we talk about our case and the explanation for our speculation. Be that as it may, the investigation is done on arbitrary little subsets of the dataset as a result of equipment restrictions, so we offer examination and our future vision musings as we wish that full demonstrate will be done in future by other intrigued specialists who have preferable equipment foundation over our own..

scholarly journals Forward Forecast of Stock Price Using LSTM Machine Learning Algorithm

2020 ◽  
Vol 12 (3) ◽  
pp. 74-79
Author(s):  
Kavitha Esther Rajakumari ◽  
◽  
M. Srinivasa Kalyan ◽  
M. Vijay Bhaskar
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Neural System ◽  
Short Term ◽  
Trading System ◽  
Term Memory ◽  
The Future ◽  
Long Short Term Memory

Stock market prediction is the demonstration of attempting to decide the future estimation of an organization stock or other monetary instrument exchanged on a trade. This paper will exhibit how to perform stock expectations utilizing Machine Learning calculations. Foreseeing securities exchange costs is an intricate assignment that generally includes broad human-PC communication. Because of the connected idea of stock costs, customary bunch preparing techniques can't be used productively for securities exchange examination. In the current framework, the Sliding window calculation is used. This calculation investigates the information, with a window pushing ahead, in the wake of examining the information. It is very tedious for expectation of stocks. While, in the proposed framework, the utilization of LSTM (Long Short Term Memory) calculation, gives compelling outcomes. While analyzing, the superfluous information is overlooked. The current framework is additionally not viable, in taking care of non-straight information. What's more, it is less proficient contrasted with LSTM algorithm. So, to help defeat these, LSTM helps in dealing with the information in a productive way. Indeed, speculators are exceptionally intrigued by the exploration zone of stock value expectations. For decent and fruitful speculation, numerous financial specialists are sharp in knowing the future circumstance of the share trading system. Great and viable expectation frameworks for securities exchange encourage brokers, financial specialists, and investigators by giving steady data like the future course of the share trading system. In this work, an intermittent neural system (RNN) and Long Short-Term Memory (LSTM) are presented, a way to deal with anticipate securities exchange lists. The proposed model is a promising prescient procedure for a very non-direct time arrangement, whose designs are hard to catch by customary models.

scholarly journals Dynamic Bit Coin Value Prediction

2019 ◽  
Vol 9 (2) ◽  
pp. 3702-3707
Keyword(s):  
Price Index ◽  
Short Term Memory ◽  
Neural System ◽  
Value Change ◽  
Short Term ◽  
Term Memory ◽  
Value Prediction ◽  
Long Short Term Memory

Bitcoin is online money that is utilized worldwide to make online installments. It has thusly become a venture vehicle in itself and is exchanged a route like other open monetary forms. The capacity to foresee the value change of Bitcoin would in this way encourage future venture and installment choices. The objective of this paper is to learn with what exactness the bearing of Bitcoin cost in USD can be anticipated. The value information is sourced from the Bitcoin Price Index. The errand is accomplished with changing degrees of achievement through the usage of a Bayesian streamlined intermittent neural system (RNN) furthermore, a Long Short Term Memory (LSTM) arranges. The LSTM accomplishes the most noteworthy order precision of 59%.

Role of long-term synaptic modification in short-term memory

Hippocampus ◽  
2001 ◽  
Vol 11 (3) ◽  
pp. 240-250 ◽  
Author(s):  
Raymond P. Kesner ◽  
Edmund T. Rolls
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Synaptic Modification

Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

2016 ◽  
Vol 39 ◽  
Author(s):  
Mary C. Potter
Keyword(s):  
Working Memory ◽  
Rapid Serial Visual Presentation ◽  
Language Comprehension ◽  
Short Term Memory ◽  
Visual Presentation ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

Working, Declarative, and Procedural Memory in Children With Developmental Language Disorder

2020 ◽  
Vol 63 (12) ◽  
pp. 4162-4178
Author(s):  
Emily Jackson ◽  
Suze Leitão ◽  
Mary Claessen ◽  
Mark Boyes
Keyword(s):  
Working Memory ◽  
Short Term Memory ◽  
Language Disorder ◽  
Declarative Memory ◽  
Memory Systems ◽  
Procedural Memory ◽  
Typically Developing ◽  
Short Term ◽  
Term Memory ◽  
Visual Spatial

Purpose Previous research into the working, declarative, and procedural memory systems in children with developmental language disorder (DLD) has yielded inconsistent results. The purpose of this research was to profile these memory systems in children with DLD and their typically developing peers. Method One hundred four 5- to 8-year-old children participated in the study. Fifty had DLD, and 54 were typically developing. Aspects of the working memory system (verbal short-term memory, verbal working memory, and visual–spatial short-term memory) were assessed using a nonword repetition test and subtests from the Working Memory Test Battery for Children. Verbal and visual–spatial declarative memory were measured using the Children's Memory Scale, and an audiovisual serial reaction time task was used to evaluate procedural memory. Results The children with DLD demonstrated significant impairments in verbal short-term and working memory, visual–spatial short-term memory, verbal declarative memory, and procedural memory. However, verbal declarative memory and procedural memory were no longer impaired after controlling for working memory and nonverbal IQ. Declarative memory for visual–spatial information was unimpaired. Conclusions These findings indicate that children with DLD have deficits in the working memory system. While verbal declarative memory and procedural memory also appear to be impaired, these deficits could largely be accounted for by working memory skills. The results have implications for our understanding of the cognitive processes underlying language impairment in the DLD population; however, further investigation of the relationships between the memory systems is required using tasks that measure learning over long-term intervals. Supplemental Material https://doi.org/10.23641/asha.13250180

Comparison of Auditory, Language, Memory, and Attention Abilities in Children With and Without Listening Difficulties

2020 ◽  
Vol 29 (4) ◽  
pp. 710-727
Author(s):  
Beula M. Magimairaj ◽  
Naveen K. Nagaraj ◽  
Alexander V. Sergeev ◽  
Natalie J. Benafield
Keyword(s):  
Auditory Processing ◽  
Short Term Memory ◽  
Group Differences ◽  
Long Term Memory ◽  
Short Term ◽  
Significant Group ◽  
Term Memory ◽  
Memory And Attention ◽  
Speed And Accuracy

Objectives School-age children with and without parent-reported listening difficulties (LiD) were compared on auditory processing, language, memory, and attention abilities. The objective was to extend what is known so far in the literature about children with LiD by using multiple measures and selective novel measures across the above areas. Design Twenty-six children who were reported by their parents as having LiD and 26 age-matched typically developing children completed clinical tests of auditory processing and multiple measures of language, attention, and memory. All children had normal-range pure-tone hearing thresholds bilaterally. Group differences were examined. Results In addition to significantly poorer speech-perception-in-noise scores, children with LiD had reduced speed and accuracy of word retrieval from long-term memory, poorer short-term memory, sentence recall, and inferencing ability. Statistically significant group differences were of moderate effect size; however, standard test scores of children with LiD were not clinically poor. No statistically significant group differences were observed in attention, working memory capacity, vocabulary, and nonverbal IQ. Conclusions Mild signal-to-noise ratio loss, as reflected by the group mean of children with LiD, supported the children's functional listening problems. In addition, children's relative weakness in select areas of language performance, short-term memory, and long-term memory lexical retrieval speed and accuracy added to previous research on evidence-based areas that need to be evaluated in children with LiD who almost always have heterogenous profiles. Importantly, the functional difficulties faced by children with LiD in relation to their test results indicated, to some extent, that commonly used assessments may not be adequately capturing the children's listening challenges. Supplemental Material https://doi.org/10.23641/asha.12808607

The Link Between Verbal Short-Term Memory and Anomia Treatment Gains

2019 ◽  
Vol 28 (3) ◽  
pp. 1039-1052
Author(s):  
Reva M. Zimmerman ◽  
JoAnn P. Silkes ◽  
Diane L. Kendall ◽  
Irene Minkina
Keyword(s):  
Short Term Memory ◽  
Controlled Trial ◽  
Treatment Success ◽  
Linear Regression Models ◽  
Language Performance ◽  
Short Term ◽  
Term Memory ◽  
Treatment Gains ◽  
Post Hoc

Purpose A significant relationship between verbal short-term memory (STM) and language performance in people with aphasia has been found across studies. However, very few studies have examined the predictive value of verbal STM in treatment outcomes. This study aims to determine if verbal STM can be used as a predictor of treatment success. Method Retrospective data from 25 people with aphasia in a larger randomized controlled trial of phonomotor treatment were analyzed. Digit and word spans from immediately pretreatment were run in multiple linear regression models to determine whether they predict magnitude of change from pre- to posttreatment and follow-up naming accuracy. Pretreatment, immediately posttreatment, and 3 months posttreatment digit and word span scores were compared to determine if they changed following a novel treatment approach. Results Verbal STM, as measured by digit and word spans, did not predict magnitude of change in naming accuracy from pre- to posttreatment nor from pretreatment to 3 months posttreatment. Furthermore, digit and word spans did not change from pre- to posttreatment or from pretreatment to 3 months posttreatment in the overall analysis. A post hoc analysis revealed that only the less impaired group showed significant changes in word span scores from pretreatment to 3 months posttreatment. Discussion The results suggest that digit and word spans do not predict treatment gains. In a less severe subsample of participants, digit and word span scores can change following phonomotor treatment; however, the overall results suggest that span scores may not change significantly. The implications of these findings are discussed within the broader purview of theoretical and empirical associations between aphasic language and verbal STM processing.

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