Article
Optics
Xiaoxiao Hu, Feihao Zhang, Yansong Li, Guilu Long
Summary: This scheme uses optimal control theory to optimize quantum gates in quantum computation on decoherence-free subspaces, providing efficiency, robustness, and better performance compared to previous methods.
Article
Multidisciplinary Sciences
Bruno B. Averbeck
Summary: Adolescent development is marked by improved cognitive processes, but a decline in the ability to learn new skills. Pruning of synapses occurs during this period. Our study shows that pruned neural networks perform better on certain tasks but learn new problems more slowly, indicating that overproduction and subsequent pruning of synapses is a computationally advantageous approach to developing a competent brain.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Geriatrics & Gerontology
Haakon R. Hol, Marianne M. Flak, Linda Chang, Gro Christine Christensen Lohaugen, Knut Jorgen Bjuland, Lars M. Rimol, Andreas Engvig, Jon Skranes, Thomas Ernst, Bengt-Ove Madsen, Susanne S. Hernes
Summary: In patients with MCI, both adaptive and non-adaptive WM training did not lead to improved cortical thickness. However, individuals with LMX1A-AA genotype showed increased cortical thickness trajectories or lack of decrease post-training.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Article
Neurosciences
Renata Figueiredo Anomal, Daniel Soares Brandao, Rafaela Faustino Lacerda de Souza, Sostenes Silva de Oliveira, Silvia Beltrame Porto, Izabel Augusta Hazin Pires, Antonio Pereira Jr
Summary: The search for a cortical signature of intelligent behavior has led to a focus on the frontoparietal network (FPN) and the correlation between intelligence and activity in this cortical circuit. In this study, alpha event-related spectral perturbation (ERSP) was used to indirectly measure cortical activity during mental rotation tasks. The results suggest that frontal alpha ERSP is correlated with working memory score in mental rotation tasks.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Behavioral Sciences
Katya Olmos-Solis, Anouk M. van Loon, Christian N. L. Olivers
Summary: The study showed a division of labor across brain regions during working memory tasks, with posterior areas preferentially coding for content and frontal areas carrying information about the relevance status regardless of the category. The findings provide further evidence for a dissociation between content and control networks in working memory.
Article
Multidisciplinary Sciences
Matteo Cucchi, Christopher Gruener, Lautaro Petrauskas, Peter Steiner, Hsin Tseng, Axel Fischer, Bogdan Penkovsky, Christian Matthus, Peter Birkholz, Hans Kleemann, Karl Leo
Summary: Early detection of malign patterns in patients' biological signals is crucial, and organic electrochemical devices are considered ideal for biosignal monitoring. The study demonstrates the potential of brain-inspired networks composed of organic electrochemical transistors for time-series predictions and classification tasks, showing promise for biofluid monitoring and biosignal analysis.
Article
Computer Science, Artificial Intelligence
Zhehui Wang, Tao Luo, Rick Siow Mong Goh, Wei Zhang, Weng-Fai Wong
Summary: In-memory deep learning executes neural network models where they are stored, thus avoiding long-distance communication between memory and computation units, resulting in considerable savings in energy and time. In-memory deep learning has already demonstrated orders of magnitude higher performance density and energy efficiency. The use of emerging memory technology (EMT) promises to increase density, energy, and performance even further. However, EMT is intrinsically unstable, resulting in random data read fluctuations. This can translate to nonnegligible accuracy loss, potentially nullifying the gains. In this article, we propose three optimization techniques that can mathematically overcome the instability problem of EMT. They can improve the accuracy of the in-memory deep learning model while maximizing its energy efficiency. Experiments show that our solution can fully recover most models' state-of-the-art (SOTA) accuracy and achieves at least an order of magnitude higher energy efficiency than the SOTA.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Neurosciences
Qiong Wu, Isabelle Ripp, Monica Emch, Kathrin Koch
Summary: The study found that working memory training has a certain impact on the morphology of the brain, mainly manifested in increased cortical gyrification, volume and thickness, as well as changes in surface area. Particularly, the affected brain regions, especially the parietal regions, may provide a better brain processing environment for higher working memory load.
HUMAN BRAIN MAPPING
(2021)
Article
Clinical Neurology
Anna Krogh Andreassen, Rikke Lambek, Nicoline Hemager, Christina Bruun Knudsen, Lotte Veddum, Anders Helles Carlsen, Anette Faurskov Bundgaard, Anne Sondergaard, Julie Marie Brandt, Maja Gregersen, Mette Falkenberg Krantz, Birgitte Klee Burton, Jens Richardt Mollegaard Jepsen, Anne Amalie Elgaard Thorup, Merete Nordentoft, Ole Mors, Vibeke Fuglsang Bliksted, Aja Greve
Summary: Despite genetic overlap, working memory impairments are mainly found in children of parents with schizophrenia. Using a data-driven approach, researchers found that a subset of children at familial high risk of schizophrenia or bipolar disorder exhibited persistent working memory impairments throughout middle childhood, which may serve as a vulnerability marker of transition to severe mental illness.
JOURNAL OF AFFECTIVE DISORDERS
(2023)
Article
Mathematical & Computational Biology
Lixing Lei, Mengya Zhang, Tingyu Li, Yelin Dong, Da-Hui Wang
Summary: A ring model based on visual working memory can explain the phenomenon of color clustering in memory as well as the Gaussian distribution of report errors.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2023)
Article
Multidisciplinary Sciences
Ivan Voitov, Thomas D. Mrsic-Flogel
Summary: Working memory is an essential component of cognition, but the mechanisms by which neural populations represent and maintain working memory are still unclear. In this study using mice, researchers found that distributed areas of the neocortex were selectively involved in the maintenance of working memory during a visual task. They also discovered that working memory representations were embedded in high-dimensional population activity in visual area AM and premotor area M2, persisting throughout the inter-stimulus delay period.
Article
Multidisciplinary Sciences
Robin Moss, Eike M. Wuelfers, Raphaela Lewetag, Tibor Hornyik, Stefanie Perez-Feliz, Tim Strohbach, Marius Menza, Axel Krafft, Katja E. Odening, Gunnar Seemann
Summary: Researchers developed a computational model to simulate the electrophysiological properties of the rabbit heart and validated it using measured body surface potential signals. They found that the activation sequence of the heart and the gradients in action potential duration have important effects on body surface signals.
Article
Neurosciences
Baiwei Liu, Xinyu Li, Jan Theeuwes, Benchi Wang
Summary: It has been traditionally believed that information retrieved from long-term memory (LTM) needs to be brought back into working memory (WM). However, this study demonstrates that retrieval from LTM is possible even when WM capacity is fully occupied. EEG results indicate that retrieving items from LTM while WM is fully engaged enhances the suppression of alpha oscillations, suggesting alternative mechanisms for accessing LTM when WM is fully occupied.
Article
Neurosciences
Shinnosuke Yoshiiwa, Hironobu Takano, Keisuke Ido, Mitsuo Kawato, Ken-ichi Morishige
Summary: Electroencephalographic studies have explored the retention of information in working memory, but this study aims to understand the mechanism behind it. The researchers measured scalp electroencephalography data and found significant differences in current amplitudes and power spectra between different working memory conditions. Furthermore, the classification of these conditions was successfully achieved based on the analysis of the current amplitudes and power spectra during encoding and retention periods. It suggests that executive control over memory retention involves both persistent neural activity and oscillatory representations in multiple cortical regions.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Animesh Renanse, Alok Sharma, Rohitash Chandra
Summary: This paper examines the memory capacity of matrix-based RNNs and compares it with conventional neural networks. The study finds that neural networks with matrix representations have better memory capacity and the performance is enhanced when external memory is introduced.