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1.
Sensors (Basel) ; 22(21)2022 Oct 22.
Artigo em Inglês | MEDLINE | ID: mdl-36365785

RESUMO

Automatic pain intensity assessment from physiological signals has become an appealing approach, but it remains a largely unexplored research topic. Most studies have used machine learning approaches built on carefully designed features based on the domain knowledge available in the literature on the time series of physiological signals. However, a deep learning framework can automate the feature engineering step, enabling the model to directly deal with the raw input signals for real-time pain monitoring. We investigated a personalized Bidirectional Long short-term memory Recurrent Neural Networks (BiLSTM RNN), and an ensemble of BiLSTM RNN and Extreme Gradient Boosting Decision Trees (XGB) for four-category pain intensity classification. We recorded Electrodermal Activity (EDA) signals from 29 subjects during the cold pressor test. We decomposed EDA signals into tonic and phasic components and augmented them to original signals. The BiLSTM-XGB model outperformed the BiLSTM classification performance and achieved an average F1-score of 0.81 and an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.93 over four pain states: no pain, low pain, medium pain, and high pain. We also explored a concatenation of the deep-learning feature representations and a set of fourteen knowledge-based features extracted from EDA signals. The XGB model trained on this fused feature set showed better performance than when it was trained on component feature sets individually. This study showed that deep learning could let us go beyond expert knowledge and benefit from the generated deep representations of physiological signals for pain assessment.


Assuntos
Algoritmos , Redes Neurais de Computação , Humanos , Medição da Dor , Aprendizado de Máquina , Memória de Longo Prazo , Dor
2.
Sensors (Basel) ; 22(21)2022 Oct 30.
Artigo em Inglês | MEDLINE | ID: mdl-36366021

RESUMO

An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed.


Assuntos
Redes Neurais de Computação , Análise de Ondaletas , Reprodutibilidade dos Testes , Previsões , Memória de Longo Prazo
3.
Sensors (Basel) ; 22(21)2022 Nov 04.
Artigo em Inglês | MEDLINE | ID: mdl-36366180

RESUMO

When tracking maneuvering targets, recurrent neural networks (RNNs), especially long short-term memory (LSTM) networks, are widely applied to sequentially capture the motion states of targets from observations. However, LSTMs can only extract features of trajectories stepwise; thus, their modeling of maneuvering motion lacks globality. Meanwhile, trajectory datasets are often generated within a large, but fixed distance range. Therefore, the uncertainty of the initial position of targets increases the complexity of network training, and the fixed distance range reduces the generalization of the network to trajectories outside the dataset. In this study, we propose a transformer-based network (TBN) that consists of an encoder part (transformer layers) and a decoder part (one-dimensional convolutional layers), to track maneuvering targets. Assisted by the attention mechanism of the transformer network, the TBN can capture the long short-term dependencies of target states from a global perspective. Moreover, we propose a center-max normalization to reduce the complexity of TBN training and improve its generalization. The experimental results show that our proposed methods outperform the LSTM-based tracking network.


Assuntos
Memória de Longo Prazo , Redes Neurais de Computação , Movimento (Física)
4.
Transl Psychiatry ; 12(1): 475, 2022 11 12.
Artigo em Inglês | MEDLINE | ID: mdl-36371406

RESUMO

Fear conditioning leads to long-term fear memory formation and is a model for studying fear-related psychopathological conditions such as phobias and post-traumatic stress disorder. Long-term fear memory formation is believed to involve alterations of synaptic efficacy mediated by changes in synaptic transmission and morphology in lateral amygdala (LA). Nck1 is a key neuronal adaptor protein involved in the regulation of the actin cytoskeleton and the neuronal processes believed to be involved in memory formation. However, the role of Nck1 in memory formation is not known. Here we explored the role of Nck1 in fear memory formation in lateral amygdala (LA). Reduction of Nck1 in excitatory neurons in LA enhanced long-term, but not short-term, auditory fear conditioning memory. Activation of Nck1, by using a photoactivatable Nck1 (PA-Nck1), during auditory fear conditioning in excitatory neurons in LA impaired long-term, but not short-term, fear memory. Activation of Nck1 immediately or a day after fear conditioning did not affect fear memory. The hippocampal-mediated contextual fear memory was not affected by the reduction or activation of Nck1 in LA. We show that Nck1 is localized to the presynapses in LA. Nck1 activation in LA excitatory neurons decreased the frequency of AMPA receptors-mediated miniature excitatory synaptic currents (mEPSCs). Nck1 activation did not affect GABA receptor-mediated inhibitory synaptic currents (mIPSCs). These results show that Nck1 activity in excitatory neurons in LA regulates glutamate release and sets the threshold for fear memory formation. Moreover, our research shows that Nck1 may serve as a target for pharmacological treatment of fear and anxiety disorders.


Assuntos
Tonsila do Cerebelo , Complexo Nuclear Basolateral da Amígdala , Tonsila do Cerebelo/metabolismo , Medo/fisiologia , Complexo Nuclear Basolateral da Amígdala/metabolismo , Memória de Longo Prazo , Receptores de AMPA/metabolismo
5.
Comput Intell Neurosci ; 2022: 3214255, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36348654

RESUMO

The Arabic syntactic diacritics restoration problem is often solved using long short-term memory (LSTM) networks. Handcrafted features are used to augment these LSTM networks or taggers to improve performance. A transformer-based machine learning technique known as bidirectional encoder representations from transformers (BERT) has become the state-of-the-art method for natural language understanding in recent years. In this paper, we present a novel tagger based on BERT models to restore Arabic syntactic diacritics. We formulated the syntactic diacritics restoration as a token sequence classification task similar to named-entity recognition (NER). Using the Arabic TreeBank (ATB) corpus, the developed BERT tagger achieves a 1.36% absolute case-ending error rate (CEER) over other systems.


Assuntos
Idioma , Processamento de Linguagem Natural , Aprendizado de Máquina , Memória de Longo Prazo , Reconhecimento Psicológico
6.
Cogn Neurosci ; 13(3-4): 220-222, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36200870

RESUMO

Working memory (WM) is the ability to maintain and manipulate internal representations. WM recruits varying brain regions based on task demands. Although the hippocampus has historically been associated with long-term memory (LTM), several studies provide evidence for its involvement during WM tasks. Slotnick (this issue) posits that this involvement is due to LTM processes. This argument rests on the assumption that processes are not shared among WM and LTM, and that WM processes are necessarily sustained. We argue that there are processes utilized by both WM and LTM, and that such processes need not be sustained to support WM.


Assuntos
Mapeamento Encefálico , Memória de Curto Prazo , Humanos , Imageamento por Ressonância Magnética , Hipocampo , Memória de Longo Prazo
7.
Cogn Neurosci ; 13(3-4): 212-214, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36209434

RESUMO

Recent studies suggest the hippocampus is involved in working memory (WM). Slotnick (this issue) critically reviewed relevant fMRI findings and concludes WM 'does not activate the hippocampus.' We extend Slotnick's review by discussing observations from human intracranial and lesion research. These studies do suggest hippocampal contributions to WM (beyond novelty encoding), which however are difficult to capture with conventional fMRI. Still, the advent of new fMRI techniques combined with a stronger emphasis on shared hippocampal mechanisms across short- and long-term memory pave an exciting path forward.


Assuntos
Hipocampo , Memória de Curto Prazo , Humanos , Memória de Curto Prazo/fisiologia , Hipocampo/diagnóstico por imagem , Hipocampo/fisiologia , Memória de Longo Prazo , Imageamento por Ressonância Magnética/métodos
8.
Cogn Neurosci ; 13(3-4): 215-217, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36218261

RESUMO

Working memory (WM) and long-term memory (LTM) tests have both overlapping and distinct neurocognitive processes. Hippocampal activity in fMRI studies-a hallmark of LTM-also occurs on WM tasks, typically during encoding or retrieval and sometimes (albeit rarely) through 'late-delay' periods. The Synaptic Theory of WM suggests that 'activity-silent' synaptic weights retain temporary, WM-relevant codes without sustained, elevated activity. The hippocampus temporarily retains item-context bindings during WM-delays that are typically 'silent' to fMRI, probably via oscillatory patterns of informational connectivity among task-relevant regions of cortex. Advancing WM theory will require modeling this dynamic interplay, as in the 'Dynamic Processing Model of WM.


Assuntos
Memória de Longo Prazo , Memória de Curto Prazo , Humanos , Hipocampo/diagnóstico por imagem , Imageamento por Ressonância Magnética
9.
Sensors (Basel) ; 22(19)2022 Sep 29.
Artigo em Inglês | MEDLINE | ID: mdl-36236504

RESUMO

Monitoring bodily pressure could provide valuable medical information for both doctors and patients. Long-term implantation of in vivo sensors is highly desirable in situations where perception reconstruction is needed. In particular, for fecal incontinence, artificial anal sphincters without perceptions could not remind patients when to defecate and even cause ischemic tissue necrosis due to uncontrolled clamping pressure. To address these issues, a novel self-packaging strain gauge sensor system is designed for in vivo perception reconstruction. In addition, long short-term memory (LSTM) networks, which show excellent performance in processing time series-related features and fitting properties, are used in this article to improve the prediction accuracy of the perception model. The proposed system has been tested and compared with the traditional linear regression (LR) approach using data from in vitro experiments. The results show that the Root-Mean-Square Error (RMSE) is reduced by more than 69%, which demonstrates that the prediction accuracy of the proposed LSTM model is higher than that of the LR model to reach a more accurate prediction of the amount of intestinal content. Furthermore, outcomes of in vivo experiments show that the robustness of the novel sensor system based on long short-term memory networks is verified through experiments with limited data.


Assuntos
Incontinência Fecal , Memória de Curto Prazo , Canal Anal/cirurgia , Incontinência Fecal/cirurgia , Humanos , Memória de Longo Prazo , Percepção
10.
Sensors (Basel) ; 22(19)2022 Oct 10.
Artigo em Inglês | MEDLINE | ID: mdl-36236789

RESUMO

Deep summarization models have succeeded in the video summarization field based on the development of gated recursive unit (GRU) and long and short-term memory (LSTM) technology. However, for some long videos, GRU and LSTM cannot effectively capture long-term dependencies. This paper proposes a deep summarization network with auxiliary summarization losses to address this problem. We introduce an unsupervised auxiliary summarization loss module with LSTM and a swish activation function to capture the long-term dependencies for video summarization, which can be easily integrated with various networks. The proposed model is an unsupervised framework for deep reinforcement learning that does not depend on any labels or user interactions. Additionally, we implement a reward function (R(S)) that jointly considers the consistency, diversity, and representativeness of generated summaries. Furthermore, the proposed model is lightweight and can be successfully deployed on mobile devices and enhance the experience of mobile users and reduce pressure on server operations. We conducted experiments on two benchmark datasets and the results demonstrate that our proposed unsupervised approach can obtain better summaries than existing video summarization methods. Furthermore, the proposed algorithm can generate higher F scores with a nearly 6.3% increase on the SumMe dataset and a 2.2% increase on the TVSum dataset compared to the DR-DSN model.


Assuntos
Algoritmos , Memória de Longo Prazo , Memória de Longo Prazo/fisiologia
11.
Comput Intell Neurosci ; 2022: 6826573, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36188679

RESUMO

Credit evaluation is a difficult problem in the process of financing and loan for small and medium-sized enterprises. Due to the high dimension and nonlinearity of enterprise behavior data, traditional logistic regression (LR), random forest (RF), and other methods, when the feature space is very large, it is easy to show low accuracy and lack of robustness. However, recurrent neural network (RNN) will have a serious gradient disappearance problem under long sequence training. This paper proposes a compound neural network model based on the attention mechanism to meet the needs of enterprise credit evaluation. The convolutional neural network (CNN) and the long short-term memory (LSTM) network were used to establish the model, using soft attention, the gradient propagates back to other parts of the model through the attention mechanism module. In the multimodel comparison experiment and three different enterprise data experiments, the CNN-LSTM-ATT model proposed in this paper is superior to the traditional models LR, RF, CNN, LSTM, and CNN-LSTM in most cases. The experimental results under multimodel comparison reflect the higher accuracy of the model, and the group test reflects the higher robustness of the model.


Assuntos
Memória de Longo Prazo , Redes Neurais de Computação , Modelos Logísticos
12.
Comput Intell Neurosci ; 2022: 1835798, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36188702

RESUMO

At present, there is a phenomenon of network data packet loss in the trajectory tracking control system, which will degrade or even destabilize the system's performance. Therefore, this work first explains the theory of the deep long-short term memory (LSTM) neural network model, the kinematic model of mobile robots, and the trajectory tracking error model. The reasons for data packet loss in the control system are analyzed. Second, a prediction model based on the LSTM network is designed according to the theory mentioned above. Finally, the training effect of the LSTM model and the robot trajectory tracking effect based on the model are tested by setting up simulation experiments. The research results are as follows: (1) The pose test error of the mobile robot will eventually tend to zero through the simulation curve generated by the pose parameters (x, y, θ) of the mobile robot. (2) The trajectory tracking error of the deep LSTM neural network prediction and compensation method with the packet loss rate of 5% is less than that with the packet loss rate of 10%. (3) The linear velocity υ of the mobile robot based on the prediction model of the LSTM network varies greatly but is always in the interval (-2, 2). Its angular velocity ω initially fluctuates greatly but gradually tends to zero after about 13 s. (4) When the prediction model tracks the trajectory of the robot, the horizontal position x, the vertical position y, and the angle θ coincide with the reference trajectory. The exploration is conducted to provide a reference for the research on data packet loss in the networked mobile robot trajectory tracking system.


Assuntos
Robótica , Simulação por Computador , Memória de Longo Prazo , Memória de Curto Prazo , Redes Neurais de Computação
13.
Comput Intell Neurosci ; 2022: 7450637, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36275954

RESUMO

When exploring facial expression recognition methods, it is found that existing algorithms make insufficient use of information about the key parts that express emotion. For this problem, on the basis of a convolutional neural network and long short-term memory (CNN-LSTM), we propose a facial expression recognition method that incorporates an attention mechanism (CNN-ALSTM). Compared with the general CNN-LSTM algorithm, it can mine the information of important regions more effectively. Furthermore, a CNN-LSTM facial expression recognition method incorporating a two-layer attention mechanism (ACNN-ALSTM) is proposed. We conducted comparative experiments on Fer2013 and processed CK + datasets with CNN-ALSTM, ACNN-ALSTM, patch based ACNN (pACNN), Facial expression recognition with attention net (FERAtt), and other networks. The results show that the proposed ACNN-ALSTM hybrid neural network model is superior to related work in expression recognition.


Assuntos
Reconhecimento Facial , Redes Neurais de Computação , Memória de Longo Prazo , Algoritmos , Reconhecimento Psicológico
14.
PLoS One ; 17(10): e0275061, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36190977

RESUMO

Recent neurocognitive models of second language learning have posited specific roles for declarative and procedural memory in the processing of novel linguistic stimuli. Pursuing this line of investigation, the present exploratory study examined the role of declarative and procedural memory abilities in the early stages of adult comprehension of sentences in a miniature language with natural language characteristics (BrocantoJ). Thirty-six native Italian young adults were aurally exposed to BrocantoJ in the context of a computer game over three sessions on consecutive days. Following vocabulary training and passive exposure, participants were asked to perform game moves described by aural sentences in the language. Game trials differed with respect to the information the visual context offered. In part of the trials processing of relationships between grammatical properties of the language (word order and morphological case marking) and noun semantics (thematic role) was necessary in order reach an accurate outcome, whereas in others nongrammatical contextual cues were sufficient. Declarative and procedural learning abilities were respectively indexed by visual and verbal declarative memory measures and by a measure of visual implicit sequence learning. Overall, the results indicated a substantial role of declarative learning ability in the early stages of sentence comprehension, thus confirming theoretical predictions and the findings of previous similar studies in miniature artificial language paradigms. However, for trials that specifically probed the learning of relationships between morphosyntax and semantics, a positive interaction between declarative and procedural learning ability also emerged, indicating the cooperative engagement of both types of learning abilities in the processing of relationships between ruled-based grammar and interpretation in the early stages of exposure to a new language in adults.


Assuntos
Idioma , Aprendizagem , Compreensão , Humanos , Desenvolvimento da Linguagem , Memória de Longo Prazo , Semântica , Vocabulário , Adulto Jovem
15.
Sci Rep ; 12(1): 16758, 2022 Oct 06.
Artigo em Inglês | MEDLINE | ID: mdl-36202900

RESUMO

Since the existing visual question answering model lacks long-term memory modules for answering complex questions, it is easy to cause the loss of effective information. In order to further improve the accuracy of the visual question answering model, this paper applies the multiple attention mechanism combining channel attention and spatial attention to memory networks for the first time and proposes a dynamic memory network model (DMN-MA) based on the multiple attention mechanism. The model uses the multiple attention mechanism in the situational memory module to obtain the most relevant visual vectors for answering questions based on continuous memory updating, storage and iterative inference of the questions, and effectively uses contextual information for answer inference. The experimental results show that the accuracy of the model in this paper reaches 64.57% and 67.18% on the large-scale public datasets COCO-QA and VQA2.0, respectively.


Assuntos
Armazenamento e Recuperação da Informação , Memória de Longo Prazo
16.
Int J Mol Sci ; 23(20)2022 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-36293213

RESUMO

Being involved in development of Huntington's, Parkinson's and Alzheimer's diseases, kynurenine pathway (KP) of tryptophan metabolism plays a significant role in modulation of neuropathology. Accumulation of a prooxidant 3-hydroxykynurenine (3-HOK) leads to oxidative stress and neuronal cell apoptosis. Drosophila mutant cardinal (cd1) with 3-HOK excess shows age-dependent neurodegeneration and short-term memory impairments, thereby presenting a model for senile dementia. Although cd gene for phenoxazinone synthase (PHS) catalyzing 3-HOK dimerization has been presumed to harbor the cd1 mutation, its molecular nature remained obscure. Using next generation sequencing, we have shown that the cd gene in cd1 carries a long deletion leading to PHS active site destruction. Contrary to the wild type Canton-S (CS), cd1 males showed defective long-term memory (LTM) in conditioned courtship suppression paradigm (CCSP) at days 5-29 after eclosion. The number of dopaminergic neurons (DAN) regulating fly locomotor activity showed an age-dependent tendency to decrease in cd1 relative to CS. Thus, in accordance with the concept "from the gene to behavior" proclaimed by S. Benzer, we have shown that the aberrant PHS sequence in cd1 provokes drastic LTM impairments and DAN alterations.


Assuntos
Proteínas de Drosophila , Drosophila , Animais , Masculino , Drosophila/metabolismo , Cinurenina/metabolismo , Triptofano/metabolismo , Domínio Catalítico , Memória de Longo Prazo , Mutação , Proteínas de Drosophila/genética , Proteínas de Drosophila/metabolismo , Drosophila melanogaster/genética , Drosophila melanogaster/metabolismo
17.
Cogn Neurosci ; 13(3-4): 113-114, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36165735

RESUMO

This special issue of Cognitive Neuroscience focuses on the roles of the hippocampus during long-term memory. A discussion paper by Tallman, Clark, and Smith (this issue) found that functional connectivity of the hippocampus with the parahippocampal cortex and fusiform gyrus decreased with memory age, providing support for systems consolidation. Commentaries were received by Berdugo-Vega and Gräff (this issue), Feld and Gerchen (this issue), Gellersen and Simons (this issue), Gobbo, Mitchell-Heggs, and Tse (this issue), Gilmore, Audrain, and Martin (this issue), Kirwan (this issue), Manns (this issue), Runyan and Brooks (this issue), Santangelo (this issue), and Yang (this issue). The author response considered the content and context of memorial information along with neuroanatomy and functional specialization and conducted new analyses to clarify their findings. An empirical fMRI paper by Thakral, Yu, and Rugg (this issue) reported that the hippocampus was sensitive to the amount of contextual information retrieved, regardless of remember-know status. Another empirical study by Bjornn, Van, and Kirwan (this issue) found that hippocampal activation changes were correlated with the number of fixations at study for correct but not incorrect mnemonic discrimination judgments. A second discussion paper (Slotnick, this issue) concluded that no fMRI studies have provided evidence that the hippocampus is associated with working memory. Commentaries were received by Courtney (this issue), Kessels and Bergmann (this issue), Peters and Reithler (this issue), Rose and Chao (this issue), Stern and Hasselmo (this issue), and Wood, Clark, and Nee (this issue). The articles in this special issue illustrate that the roles of the hippocampus in long-term memory (and other types of memory) are under active investigation and provide many directions for research in the immediate future.


Assuntos
Hipocampo , Memória de Longo Prazo , Humanos , Hipocampo/diagnóstico por imagem , Hipocampo/fisiologia , Memória de Longo Prazo/fisiologia , Lobo Temporal/fisiologia , Rememoração Mental/fisiologia , Imageamento por Ressonância Magnética , Memória de Curto Prazo , Mapeamento Encefálico
18.
Neurosci Res ; 185: 62-66, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36096270

RESUMO

In the fruit fly Drosophila melanogaster, environmental light is required for maintaining long-term memory (LTM). Furthermore, the Pigment dispersing factor (Pdf), which is a circadian neuropeptide, and the neuronal activity of Pdf neurons are essential for light-dependent maintenance of courtship LTM. Since Pdf neurons can sense light directly via circadian photoreceptors [Rhodopsin 7 (Rh7) and Cryptochrome (Cry)], it is possible that Rh7 and Cry in Pdf neurons are involved in the maintenance of LTM. In this study, using a courtship conditioning assay, we demonstrated that circadian photoreceptors in Pdf neurons are required for maintaining courtship LTM.


Assuntos
Proteínas de Drosophila , Drosophila , Animais , Drosophila/fisiologia , Drosophila melanogaster/fisiologia , Ritmo Circadiano/fisiologia , Memória de Longo Prazo , Rodopsina , Criptocromos
19.
Neurobiol Learn Mem ; 195: 107686, 2022 11.
Artigo em Inglês | MEDLINE | ID: mdl-36174889

RESUMO

The content of long-term memory is neither fixed nor permanent. Reminder cues can destabilize consolidated memories, rendering them amenable to change before being reconsolidated. However, not all memories destabilize following reactivation. Characteristics of a memory, such as its age or strength, impose boundaries on destabilization. Previously, we demonstrated that presentation of salient novel information at the time of reactivation can readily destabilize resistant object memories in rats and this form of novelty-induced destabilization is dependent upon acetylcholine (ACh) activity at muscarinic receptors (mAChRs). In the present study, we sought to determine if this same mechanism for initiating destabilization of resistant object memories is present in mice and further expand our understanding of the mechanisms through which ACh modulates object memory destabilization by investigating the role of nicotinic receptors (nAChRs). We provide evidence that in mice mAChRs are necessary for destabilizing object memories that are readily destabilized and those that are resistant to destabilization. Conversely, nAChRs were found to be necessary only when memories are readily destabilized. We then investigated the role of both receptors in the reconsolidation of destabilized object memory traces and determined that nAChRs, but not mAChRs, are necessary for object memory reconsolidation. Together, these results suggest that nAChRs may play a more selective role in the re-storage of object memories following destabilization and that ACh acts through mAChRs to act as an override signal to initiate destabilization of resistant object memories following reactivation with novelty. These findings expand our current understanding of the role of ACh in the dynamic storage of long-term memory.


Assuntos
Memória de Longo Prazo , Receptores Nicotínicos , Ratos , Camundongos , Animais , Memória de Longo Prazo/fisiologia , Acetilcolina , Receptores Muscarínicos/metabolismo , Colinérgicos
20.
J Cogn Neurosci ; 34(12): 2360-2374, 2022 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-36122353

RESUMO

Although storage in working memory (WM) can be tracked via measurements of ongoing neural activity, past work has shown that observers can maintain access to that information despite temporary interruptions of those neural patterns. This observation has been regarded as evidence for a neurally silent form of WM storage. Alternatively, however, unattended information could be retrieved from episodic long-term memory (eLTM) rather than being maintained in WM during the activity-silent period. Here, we tested between these possibilities by examining whether WM performance showed evidence of proactive interference (PI)-a hallmark of retrieval from eLTM-following such interruptions. Participants remembered the colors (Experiments 1-3) or locations (Experiment 4) of serially presented objects. We found PI for set sizes larger than 4, but not for smaller set sizes, suggesting that eLTM may have supported performance when WM capacity was exceeded. Critically, performance with small set sizes remained resistant to PI, even following prolonged interruptions by a challenging distractor task. Thus, we found evidence for PI-resistant memories that were maintained across likely interruptions of storage-related neural activity, an empirical pattern that implies activity-silent storage in WM.


Assuntos
Memória de Longo Prazo , Memória de Curto Prazo , Humanos , Rememoração Mental
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