Hippocampus is needed for both spatial working and reference memories. Here, using a radial eight-arm maze, we examined how the combined demand on these memories influenced CA1 place cell assemblies while reference memories were partially updated. This was contrasted with control tasks requiring only working memory or the update of reference memory. Reference memory update led to the reward-directed place field shifts at newly rewarded arms and to the gradual strengthening of firing in passes between newly rewarded arms but not between those passes that included a familiar-rewarded arm. At the maze center, transient network synchronization periods preferentially replayed trajectories of the next chosen arm in reference memory tasks but the previously visited arm in the working memory task. Hence, reference memory demand was uniquely associated with a gradual, goal novelty-related reorganization of place cell assemblies and with trajectory replay that reflected the animal's decision of which arm to visit next.
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http://dx.doi.org/10.1016/j.neuron.2018.11.015 | DOI Listing |
Sci Rep
January 2025
Department of Biosystems Engineering, Graduate School of Science and Engineering, Yamagata University (emeritus), Yonezawa, Japan.
We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R-R interval (RRI) estimation. Preprocessed BCG and reference ECG signals were inputted into the bidirectional long short-term memory network to train the model to minimize the loss function of the mean squared error between the predicted ECG (pECG) and genuine ECG signals. Using a dataset acquired with polyvinylidene fluoride and ECG sensors in different recumbent positions from 18 participants, we generated pECG signals from preprocessed BCG signals using the learned model and evaluated the RRI estimation performance by comparing the predicted RRI with the reference RRI obtained from the ECG signal using a leave-one-subject-out cross-validation scheme.
View Article and Find Full Text PDFSleep Adv
November 2024
Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.
Study Objectives: The "Zeigarnik effect" refers to the phenomenon where future intentions are remembered effectively only as long as they are not executed. This study investigates whether these intentions, which remain active during sleep, influence dream content.
Methods: After an adaptation night, each of the 19 participants (10 women and 9 men) received three different task plans in the evening before the experimental night, each describing how to perform specific tasks.
J Chin Film Stud
December 2024
Australian China Centre in the World, Australian National University, Building 188, Fellows Lane, ACT, 2601, Canberra, Australia.
This article discusses and explores acclaimed new media artist Huang Hsin-Chien's virtual reality (VR) experience (Shishenji, 2019). In this film, through the process of the body illusion or the body replacement program, a component specific to VR, viewers embody the soul of a political prisoner deceased during Taiwan's martial law era. juxtaposes Taiwan's traditional spiritual traditions with references to the Ghost Festival () and includes a dystopic vision of the future that appears towards the end of the film.
View Article and Find Full Text PDFAppl Neuropsychol Adult
January 2025
Department of Psychology, Loyola University Chicago, Chicago, IL, USA.
Working memory (WM), the cognitive system that briefly stores and updates information during complex tasks, is one of the most consistently identified neurocognitive deficits in individuals with ADHD. WM deficits are linked to significant challenges in daily life. Adults with ADHD often experience co-occurring anxiety and mood disorders, which are associated with more severe clinical presentations and greater WM deficits.
View Article and Find Full Text PDFJ Biomed Inform
January 2025
Department of Information Management and Business Analytics, Montclair State University, Feliciano School of Business, NJ, USA. Electronic address:
Background And Objective: Subjective cognitive decline (SCD) refers to self-reported difficulties in concentration, memory, and decision-making. Since SCD is based on subjective experiences, no specific medical test can definitively confirm its presence, making early detection challenging. Thus, it is advantageous to develop an AI model to capitalize on self-reported health complications, personality traits, and sociodemographic factors for early detection of SCD.
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