scholarly journals A Modeling Framework for Deriving the Structural and Functional Architecture of a Short-Term Memory Microcircuit

Neuron ◽  
2013 ◽  
Vol 79 (5) ◽  
pp. 987-1000 ◽  
Author(s):  
Dimitry Fisher ◽  
Itsaso Olasagasti ◽  
David W. Tank ◽  
Emre R.F. Aksay ◽  
Mark S. Goldman
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Modeling Framework ◽  
Functional Architecture ◽  
Term Memory

scholarly journals Top-Down Contributions to Attention Shifting and Disengagement: A Template Model of Visual Attention

2018 ◽  
Author(s):  
Motonori Yamaguchi
Keyword(s):  
Visual Attention ◽  
Computational Models ◽  
Short Term Memory ◽  
Top Down ◽  
Short Term ◽  
Modeling Framework ◽  
Term Memory ◽  
Visual Item ◽  
Driven System ◽  
Fixation Offset

Two separate systems are involved in the control of spatial attention; one that is driven by a goal, and the other that is driven by stimuli. While the goal- and stimulus-driven systems follow different general principles, they also interplay with each other. However, the mechanism by which the goal-driven system influences the stimulus-driven system is still debated. The present study examined top-down contributions to two components of attention orienting, shifting and disengagement, with an experimental paradigm in which participants held a visual item in short-term memory and performed a prosaccade task with a manipulation of the gap between fixation offset and target onset. Four experiments showed that the short-term memory content accelerated shifting and impaired disengagement, but the influence on disengagement depended on the utility of short-term memory in guiding attention toward the target. Thus, the use of short-term memory was strategic. Computational models of visual attention were fitted to the experimental data, which suggested that the top-down contributions to shifting was more prominent than those to disengagement. The present study shows that the current modeling framework was particularly useful when examining the contributions of theoretical constructs for the control of visual attention, but it also suggests limitations.

scholarly journals Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks

2021 ◽  
Author(s):  
Rajesh Maddu ◽  
Abhishek Reddy Vanga ◽  
Jashwanth Kumar Sajja ◽  
Ghouse Basha ◽  
Rehana Shaik
Keyword(s):  
Neural Networks ◽  
Surface Temperature ◽  
Land Surface ◽  
Short Term Memory ◽  
Dew Point ◽  
Short Term ◽  
Modeling Framework ◽  
Term Memory ◽  
Coastal Cities ◽  
Long Short Term Memory

Abstract Surface Temperature (ST) is important in terms of surface energy and terrestrial water balances affecting urban ecosystems. In this study, to process the nonlinear changes of climatological variables by leveraging the distinct advantages of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), we propose an LSTM-BiLSTM hybrid deep learning model which extracts multi-dimension features of inputs, i.e., backward (future to past) or forward (past to future) to predict ST. This study assessed the climatological variables, i.e., wind speed, wind direction, relative humidity, dew point temperature, and atmospheric pressure impact on ST using five major coastal cities of India: Chennai, Mangalore, Visakhapatnam, Cuddalore, and Cochin. The Recurrent Neural Networks (RNN) and hybrid LSTM-BiLSTM models have effectively predicted ST and outperformed the standalone Artificial Neural Networks (ANN), LSTM, and BiLSTM models. The RNN and LSTM-BiLSTM models have performed better in predicting ST for Mangalore (Nash-Sutcliffe efficiency (NSE)=0.91), followed by Cochin (NSE=0.89), Chennai (NSE=0.88), Cuddalore (NSE=0.88), and Vishakhapatnam (NSE=0.81). The hybrid data-driven modeling framework indicated that coupling the LSTM and BiLSTM models were proven effective in predicting the ST of coastal cities.

Long Short-Term Memory Networks for Real-Time Runoff Forecasting using Remotely Sensed Data

2021 ◽  
Author(s):  
Paul Muñoz ◽  
David F. Muñoz ◽  
Johanna Orellana-Alvear ◽  
Hamed Moftakhari ◽  
Hamid Moradkhani ◽  
...  
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Data Representation ◽  
Tropical Andes ◽  
Short Term ◽  
Modeling Framework ◽  
Term Memory ◽  
Runoff Forecasting ◽  
Long Short Term Memory ◽  
Hydrological Applications

<p>Current efforts on Deep Learning-based modeling are being put for solving real world problems with complex or even not-fully understood interactions between predictors and target variables. A special artificial neural network, the Long Short-Term Memory (LSTM) is a promising data-driven modeling approach for dynamic systems yet little has been explored in hydrological applications such as runoff forecasting. An aditional challenge to the forecasting task arises from the uncertainties generated when using readily-available Remote Sensing (RS) imagery aimed to overcome lack of in-situ data describing the runoff governing processes. Here, we proposed a runoff forecasting framework for a 300-km<sup>2 </sup>mountain catchment located in the tropical Andes of Ecuador. The framework consists on real-time data acquisition, preprocessing and runoff forecasting for lead times between 1 and 12 hours. LSTM models were fed with 18 years of hourly runoff, and precipitation data from the novel PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now) product. Model efficiencies according to the NSE metric ranged from 0.959 to 0.554, for the 1- to 12-hour models, respectively. Considering that the concentration time of the catchment is approximately 4 hours, the proposed framework becomes a useful tool for delivering runoff forecasts to decision makers, stakeholders and the public. This study has shown the suitability of using the PDIR-Now product in a LSTM-modeling framework for real-time hydrological applications. Future endeavors must focus on improving data representation and data assimilation through feature engineering strategies.</p><p><strong>Keywords: </strong>Long Short-Term Memory; PDIR-Now; Hydroinformatics; Runoff forecasting; Tropical Andes</p>

scholarly journals Top-Down Contributions to Attention Shifting and Disengagement: A Template Model of Visual Attention

2018 ◽  
Author(s):  
Motonori Yamaguchi
Keyword(s):  
Visual Attention ◽  
Computational Models ◽  
Short Term Memory ◽  
Top Down ◽  
Short Term ◽  
Modeling Framework ◽  
Term Memory ◽  
Visual Item ◽  
Driven System ◽  
Fixation Offset

Two separate systems are involved in the control of spatial attention; one that is driven by a goal, and the other that is driven by stimuli. While the goal- and stimulus-driven systems follow different general principles, they also interplay with each other. However, the mechanism by which the goal-driven system influences the stimulus-driven system is still debated. The present study examined top-down contributions to two components of attention orienting, shifting and disengagement, with an experimental paradigm in which participants held a visual item in short-term memory and performed a prosaccade task with a manipulation of the gap between fixation offset and target onset. Four experiments showed that the short-term memory content accelerated shifting and impaired disengagement, but the influence on disengagement depended on the utility of short-term memory in guiding attention toward the target. Thus, the use of short-term memory was strategic. Computational models of visual attention were fitted to the experimental data, which suggested that the top-down contributions to shifting was more prominent than those to disengagement. The present study indicates that the current modeling framework was particularly useful when examining the contributions of theoretical constructs for the control of visual attention, but it also suggests limitations.

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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