Article
Multidisciplinary Sciences
Yuxuan Zhao, Yi Zeng, Guang Qiao
Summary: Classical conditioning plays a critical role in the learning process of biological brains, and our brain-inspired BICC model can replicate a broader set of findings and offer better computational explainability for both the experimental phenomena and the biological mechanisms of classical conditioning.
Review
Neurosciences
Kyoung-Doo Hwang, Sang Jeong Kim, Yong-Seok Lee
Summary: The cerebellum plays a critical role in modulating fear memory network and prediction, with involvement at the cellular and synaptic levels. Understanding the contributions of distinct cerebellar structures to fear learning and memory may lead to more effective treatment strategies for fear-related affective disorders.
FRONTIERS IN CELLULAR NEUROSCIENCE
(2022)
Article
Mathematics, Applied
Sara Pollock, Leo G. Rebholz
Summary: This work introduces, analyzes, and demonstrates an efficient and theoretically sound filtering strategy to ensure the condition of the least-squares problem solved at each iteration of Anderson acceleration. The combined strategy, consisting of controlling the length disparity between least-squares matrix columns and enforcing a lower bound on the angles between subspaces spanned by those columns, effectively controls the condition number of the matrix. The method is particularly effective for problems with a large initial iterate distance from the solution and distinct preasymptotic and asymptotic phases.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2023)
Article
Multidisciplinary Sciences
Stefan P. Ewers, Timo M. Dreier, Siham Al-Bas, Peter Schwenkreis, Burkhard Pleger
Summary: This proof-of-concept study investigated the possibility of applying the influence of TMS on cortical excitability to classical conditioning. The results showed significant enhancement of motor evoked potentials paired with the conditioned tone compared to the control tone, indicating successful conditioning through TMS.
SCIENTIFIC REPORTS
(2023)
Letter
Engineering, Aerospace
Shozo Mori, David F. Crouse
Summary: This note revisits the theoretical foundation of Daum-Huang particle filter concepts, specifically focusing on the necessary and sufficient condition for a flow to generate a specific homotopy between the a priori and a posteriori probability density functions. The study demonstrates that two well-known flows satisfy this condition in the case of linear-Gaussian, restating significant results with an alternative proof from recent papers coauthored by Fred Daum, which offers a valuable perspective for future flow-based filter developments.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Astronomy & Astrophysics
Riccardo Gonzo, Canxin Shi
Summary: This study extends the Kerr-Schild double copy to the case of a probe particle moving in the Kerr-Schild background. It solves Wong's equations and finds a new double copy map, enabling the recovery of geodesic equations for Schwarzschild and Kerr, naturally applicable to both bound and unbound orbits.
Article
Geography, Physical
Oliver Korup
Summary: The growing amount and diversity of data require informed predictions under uncertainty, with the adverse impacts of climate change and natural hazards driving the search for reliable predictions. Introducing Bayesian methods can help geomorphologists capture and explain uncertainties more effectively.
EARTH SURFACE PROCESSES AND LANDFORMS
(2021)
Article
Automation & Control Systems
Jie Zhang, Xusheng Yang, Wen-An Zhang
Summary: This article studies the Bayesian filtering problem for nonlinear systems with heavy-tailed noises. It proposes a progressive Bayesian filtering framework to overcome the limitations of Gaussian distribution or particle sets in expressing the posterior probability density distribution. The framework divides the measurement update into steps and uses intermediate posterior distributions as importance proposal distributions to improve the approximation of posterior probability density distributions. Termination conditions are also proposed to enhance the robustness against outliers. A simulation example is provided to illustrate the effectiveness and superiority of the proposed framework.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Rajasekharreddy Poreddy, E. S. Gopi
Summary: This paper investigates the issue of poor performance of multi-class classifiers in applications such as decision support systems and sports game prediction. By treating the poorly performing classifier as a discrete memoryless channel model, the study proposes the use of M-ary Mini-Max technique and Particle Swarm Optimization to improve the classifier's performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Statistics & Probability
Parfait Munezero, Mattias Villani, Robert Kohn, Robert Kohn
Summary: A mixture of experts model is used to model the conditional density of a response variable by using a mixture of regression models with covariate-dependent mixture weights. The model is extended to allow the parameters in both the mixture components and the weights to evolve over time. Inference for time-varying parameters in a richly parameterized mixture of experts models is challenging, and a sequential Monte Carlo algorithm is proposed for online inference. The method provides a unified treatment for mixtures with time-varying parameters, including the special case of static parameters. The properties of the method are assessed using simulated data and real industrial data for predicting software faults in a continuously upgraded large-scale software project.
Article
Engineering, Biomedical
Chao Yang, Xiaoping Wang, Zhanfei Chen, Sen Zhang, Zhigang Zeng
Summary: This article proposes an OC-CC cascaded circuit that simulates biological learning and adaptation capabilities. By using OC and CC circuits, the circuit achieves bio-like functions and can perform online learning and computing. The simulation results demonstrate the advantages of the circuit in power consumption and hardware overhead, providing a feasible approach for large-scale bionic learning.
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
(2022)
Article
Physics, Fluids & Plasmas
Ashot Matevosyan, Armen E. Allahverdyan
Summary: The Bohr-Van Leeuwen theorem states that an external static magnetic field does not affect the state of a classical equilibrium system. However, when a classical charged Brownian particle interacts with an equilibrium bath in the presence of a static magnetic field, the long-time state of the bath is influenced. The magnetic field induces an average angular momentum for the bath, while the energy and linear momentum are dissipated by the bath.
Article
Automation & Control Systems
Chao Yang, Xiaoping Wang, Zhanfei Chen, Zilu Wang, Sen Zhang, Zhigang Zeng
Summary: This work proposes a bio-inspired decision-making memristive circuit drawing on Hull's secondary learning system. The circuit can mimic decision-making processes initiated by secondary drive stimuli and shaped by secondary reinforcers through classical conditioning and operant conditioning. It also considers factors influencing decision-making, such as demand states, incentive motivation, and habit strength. The proposed circuit includes modules for classical conditioning, drive regulation, habit memory, incentive generation, and winner-takes-all, designed using a modular hierarchical circuit design method. The circuit utilizes memristors to perform brain-like online learning in an in-memory computing manner, providing power and area advantages. PSPICE-based simulations demonstrate the circuit's strong adaptive decision-making ability, making it applicable to bionic intelligent robots for complex tasks like detection and rescue.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Biology
Jan L. Klee, Bryan C. Souza, Francesco P. Battaglia
Summary: This study used high-density electrophysiological recordings from the hippocampal CA1 area and the prefrontal cortex (PFC) in mice to investigate how sounds guide anticipatory licking during classical conditioning. The results showed distinct learning-dependent changes at the single-cell level in CA1 and PFC neurons, as well as the maintenance of cue identity at the population level. Additionally, task-related neuronal assemblies in CA1 and PFC exhibited reactivation during hippocampal awake Sharp-Wave Ripples (aSWRs), supporting the idea that persistent firing and reactivation of task-related neural activity patterns in these areas contribute to learning during classical conditioning.
Article
Optics
Mohit Lal Bera, Manabendra Nath Bera
Summary: The traditional Bayes' rule is not applicable in quantum mechanics as it leads to inconsistencies in quantum measurement inferences. We propose a solution by introducing a quantum Bayes' rule that can consistently describe quantum processes, even with coherent superposition and nonlocal correlations.