Article
Neurosciences
Ya-Ting Chen, Freek van Ede, Bo-Cheng Kuo
Summary: This study investigates the neural basis of working memory capacity by exploiting the content dependence of memory materials. The results show that alpha oscillations track memory capacity in a content-specific manner, dependent not only on the number of items but also on their complexity.
JOURNAL OF NEUROSCIENCE
(2022)
Article
Geochemistry & Geophysics
Haijun Wang, Wenlai Ma, Shengyan Zhang, Wei Hao
Summary: This letter introduces an efficient and effective hierarchical feature pooling transformer (HFPT) for UAV object tracking. The HFPT method reduces feature length, captures rich contextual information, and enriches encoded detailed information to handle small targets. Experimental results demonstrate that HFPT achieves better performance than current top-performing trackers in multiple UAV benchmarks.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Neurosciences
Fang-Wen Chen, Chun-Hui Li, Bo-Cheng Kuo
Summary: This study investigated the effects of temporal expectations on neural responses and subsequent performance during the retention interval of working memory (WM). The results showed that smaller duration variability and predictable experimental tasks led to greater alpha-power attenuation over the left frontal and parietal regions during WM retention. Moreover, there was a positive relationship between alpha-power attenuation in the left posterior parietal regions and the variability difference in the response benefit. Overall, these findings suggest the importance of temporal expectations in WM maintenance.
Article
Engineering, Multidisciplinary
Kevin Linka, Amelie Schafer, Xuhui Meng, Zongren Zou, George Em Karniadakis, Ellen Kuhl
Summary: Understanding real-world dynamical phenomena is challenging, and machine learning has become the go-to technology for analyzing and making decisions based on these phenomena. However, traditional neural networks often ignore the fundamental laws of physics and fail to make accurate predictions. In this study, the combination of neural networks, physics informed modeling, and Bayesian inference is used to integrate data, physics, and uncertainties, improving the predictive potential of neural network models.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Acoustics
Zelin Qiu, Jianjun Gu, Dingding Yao, Junfeng Li, Yonghong Yan
Summary: In this paper, a novel time-frequency neuro-steered speaker extractor (TF-NSSE) is proposed. It leverages time-frequency transformation to match the temporal resolution of speech signals with neural signals, significantly reducing computational complexity. Additionally, an interaction module is introduced to effectively fuse attention information and address the issue of insufficient data. Experimental results demonstrate that TF-NSSE outperforms existing time-domain methods in terms of extraction performance and resource consumption.
Article
Computer Science, Artificial Intelligence
parametric dynamical Kazem Meidani, Amir Barati Farimani
Summary: Data-driven frameworks for the identification of nonlinear dynamical systems facilitate system prediction and control in various applications. By learning the impact of parameters, a parametric form identification model using Integer Programming (IP2) is proposed, which is capable of generalization. Through the use of basis function libraries, dimension analysis, and the assumption of integer coefficients, exact forms of parametric mechanical dynamical systems can be identified. Additionally, state and energy equations of these systems can be identified from videos using object tracking techniques and a sequential filtering scheme, resulting in improved robustness to noise compared to previous models, particularly for the inverted pendulum on a cart.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics, Interdisciplinary Applications
Ankit Mandal, Yash Tiwari, Prasanta K. Panigrahi, Mayukha Pal
Summary: It has been demonstrated that synchronizing physical prior with a neural network reduces training requirements for learning non-linear physical systems. Recent research shows that parameterizing Lagrangian and Hamiltonian using neural network weights and biases, and then executing the equations of motion, leads to more efficient prediction of non-linear dynamical systems compared to conventional neural networks.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Psychology, Mathematical
Yin-Ting Lin, Garry Kong, Daryl Fougnie
Summary: The study found that object-based selection is stronger than feature-based selection in attention mechanisms, and a similar pattern exists in memory updating. This indicates the presence of object-based attention effects in visual working memory, suggesting shared attentional mechanisms between perception and memory.
PSYCHONOMIC BULLETIN & REVIEW
(2021)
Article
Automation & Control Systems
Stefanie Winkler, Andreas Koerner, Felix Breitenecker
Summary: The text describes the use of hybrid approaches in various industry branches to solve complex application problems. Different tools for simulation and identification of hybrid systems have been developed over the last decades. The integration of artificial feed-forward neural networks into the modelling process of HDS allows for interdisciplinary exchange and introduces specific modelling methods and challenges.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2021)
Article
Computer Science, Theory & Methods
Christian Legaard, Thomas Schranz, Gerald Schweiger, Jan Drgona, Basak Falay, Claudio Gomes, Alexandros Iosifidis, Mahdi Abkar, Peter Larsen
Summary: Dynamical systems are extensively used in natural sciences and engineering disciplines. While simple systems can be described by differential equations derived from fundamental physical laws, more complex systems require data-driven modeling approaches. This article surveys the use of neural networks to construct models of dynamical systems, reviews related literature, identifies significant challenges, and discusses promising research areas.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Davide Zambrano, Pieter R. Roelfsema, Sander Bohte
Summary: The article introduces a model named CT-AuGMEnT based on continuous-time reinforcement learning to study animals' ability to make decisions based on sensory evidence, demonstrating its effectiveness in learning tasks and accumulating relevant evidence.
Article
Environmental Sciences
Bradley Koskowich, Michael Starek, Scott A. King
Summary: This study explores the feasibility of applying monoplotting to video data from security cameras and image data from UAS surveys to create a mapping product overlaying traffic flow onto aerial orthomosaic. While manual monoplotting achieved some success, attempts to automate the process on video faced challenges.
Article
Neurosciences
Yi-Fang Hsu, Jarmo A. Hamalainen
Summary: This study investigates the relationship between alpha suppression and working memory performance, and finds that alpha suppression increases with higher working memory load and is more prominent in individuals with poor working memory capacity.
Article
Multidisciplinary Sciences
Alon Loeffler, Adrian Diaz-Alvarez, Ruomin Zhu, Natesh Ganesh, James M. Shine, Tomonobu Nakayama, Zdenka Kuncic
Summary: Nanowire networks (NWNs) can mimic the connectivity and dynamics of the brain, including the synaptic processes involved in higher-order cognitive functions. In this study, NWNs were used to replicate variations of the n-back task, a commonly used measure of human working memory. The NWNs were able to retain information in working memory for at least seven steps back, similar to the capacity of human subjects. Simulations further revealed the plasticity of NWN junctions and how memory consolidation occurs through strengthening and pruning of synaptic conductance pathways.
Article
Automation & Control Systems
Mariana Ballesteros, Andrey Polyakov, Denis Efimov, Isaac Chairez, Alexander S. Poznyak
Summary: This study aims to design a non-parametric identifier for homogeneous systems based on a class of artificial neural networks with continuous dynamics. The main contributions include extending the universal approximation property of neural networks for continuous homogeneous systems and developing a differential non-parametric identifier based on homogeneous neural networks. The effectiveness of the proposed identifier is verified through simulations, showing faster convergence and less oscillations compared to a classical ANN identifier.
Article
Neurosciences
Lourdes Delgado Reyes, Sobanawartiny Wijeakumar, Vincent A. Magnotta, Samuel H. Forbes, John P. Spencer
Article
Psychology
Aaron T. Buss, Vincent A. Magnotta, Will Penny, Gregor Schoener, Theodore J. Huppert, John P. Spencer
Summary: The study demonstrates how an integrative cognitive neuroscience approach, utilizing multilevel Bayesian statistics, can predict patterns of brain activity, explain behavioral data, and explore the impact of neural activity on errors in change detection in a study of visual working memory. The model-based analysis challenges traditional cognitive theories by suggesting that key areas in the dorsal attention network play a central role in change detection rather than working memory maintenance, contrary to previous interpretations of fMRI studies.
PSYCHOLOGICAL REVIEW
(2021)
Article
Clinical Neurology
Eric S. Jackson, Sobanawartiny Wijeakumar, Deryk S. Beal, Bryan Brown, Patricia M. Zebrowski, John P. Spencer
Summary: This study examined the neural mechanisms of speech planning and execution in 9-12 year-old children who stutter and controls using functional near-infrared spectroscopy. It was found that during planning, children who stutter exhibited atypical activation in bilateral inferior frontal gyrus and right supramarginal gyrus, while during execution, atypical activation was observed in bilateral precentral gyrus, inferior frontal gyrus, right supramarginal gyrus, and superior temporal gyrus. Some activation patterns in children who stutter were similar to adults who stutter, but differences were also identified, indicating possible impairments in sensorimotor integration and inhibitory control for children who stutter.
JOURNAL OF CLINICAL NEUROSCIENCE
(2021)
Article
Neurosciences
Aaron T. Buss, Vincent Magnotta, Eliot Hazeltine, Kaleb Kinder, John P. Spencer
Summary: This study investigated the functional roles of different cortical regions in task switching, revealing that frontal cortex is involved in shifting dimensional attention while temporal and occipital cortices are activated less during stimulus-response conflict. Occipital regions demonstrated a complex pattern of activation sensitive to both conflict resolution and dimensional attention shifting.
JOURNAL OF COGNITIVE NEUROSCIENCE
(2021)
Article
Neurosciences
Samuel H. Forbes, Sobanawartiny Wijeakumar, Adam T. Eggebrecht, Vincent A. Magnotta, John P. Spencer
Summary: An innovative integrated pipeline for image reconstruction of fNIRS data was presented, demonstrating the cleaning and preparation of data for analysis. High temporal correlations were shown between channel-based and image-based fNIRS data, and good correspondence between data in channel space and image reconstructed data.
Article
Psychology, Developmental
John P. Spencer, Shannon Ross-Sheehy, Bret Eschman
Summary: The study used a dynamic field model to explain changes in infant spatial attention. The results supported the model's predictions, showing that infants had higher orienting accuracy and slower reaction times in competitive conditions. The model's fits to the experimental results provided support for its explanation of neuro-developmental changes.
Article
Psychology
Ajaz A. Bhat, John P. Spencer, Larissa K. Samuelson
Summary: Research shows that infants, children, and adults have the ability to learn new words across ambiguous naming situations. WOLVES, an implementation-level account of cross-situational word learning, captures data from studies with adults and children more accurately than competitor models. The study also provides the first developmental explanation of cross-situational word learning, revealing changes in memory processes from infancy to adulthood.
PSYCHOLOGICAL REVIEW
(2022)
Article
Multidisciplinary Sciences
Jeffrey S. Johnson, Amanda E. van Lamsweerde, Evelina Dineva, John P. Spencer
Summary: In the study of working memory for simple visual features, previous perspectives assumed independent storage of items in working memory. However, recent evidence suggests that there are systematic biases and interactions between items during active maintenance. This study replicates the repulsion bias between metrically similar colors during active storage in working memory and finds that these colors are stored with lower resolution compared to a unique color. Quantitative simulations of neurodynamical models of working memory account for these effects, highlighting the importance of lateral inhibition and spatial pathways in maintaining distinct neural representations.
SCIENTIFIC REPORTS
(2022)
Article
Biology
John P. Spencer, Samuel H. Forbes, Sophie Naylor, Vinay P. Singh, Kiara Jackson, Sean Deoni, Madhuri Tiwari, Aarti Kumar
Summary: A study found that air quality in the first year of a baby's life is linked to cognitive deficits, especially for infants from homes that use solid cooking materials. These infants showed lower visual working memory scores and slower visual processing speed.
Article
Psychology, Biological
Sobanawartiny Wijeakumar, Samuel H. Forbes, Vincent A. Magnotta, Sean Deoni, Kiara Jackson, Vinay P. Singh, Madhuri Tiwari, Aarti Kumar, John P. Spencer
Summary: Stunting in infancy has a negative impact on visual working memory and attention in infants, affecting their long-term cognitive outcomes. Intervention efforts should focus on improving working memory and reducing distractibility in infancy.
NATURE HUMAN BEHAVIOUR
(2023)
Article
Psychology, Educational
Ajaz A. Bhat, Larissa K. Samuelson, John P. Spencer
Summary: The interaction between visual exploration and auditory processing is crucial for early cognitive development, particularly in the context of word learning. By generalizing a formal neural process model and conducting studies, researchers can gain insights into the effects of labels on visual and auditory attention, as well as explore the interface between formal theories and empirical findings.
Article
Psychology, Experimental
Gavin W. Jenkins, Larissa K. Samuelson, Will Penny, John P. Spencer
Summary: The study found that early word learning follows the "suspicious coincidence effect", where learners tend to generalize novel names more narrowly when associated with multiple identical exemplars. However, as experimental conditions change, the manifestation of this effect is also influenced.
Article
Psychology, Multidisciplinary
John P. Spencer
CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE
(2020)
Article
Psychology, Developmental
Sobanawartiny Wijeakumar, Aarti Kumar, Lourdes M. Delgado Reyes, Madhuri Tiwari, John P. Spencer
DEVELOPMENTAL SCIENCE
(2019)
Article
Psychology, Educational
Sammy Perone, Daniel J. Plebanek, Megan G. Lorenz, John P. Spencer, Larissa K. Samuelson