Article
Engineering, Multidisciplinary
Sergey A. Lobov, Ekaterina S. Berdnikova, Alexey I. Zharinov, Dmitry P. Kurganov, Victor B. Kazantsev
Summary: Mathematical and computer simulations of learning in neural networks have mostly focused on changes in synaptic weights. However, experimental data suggests that brain circuit plasticity also involves homeostatic and structural plasticity. This study proposes a model of structural plasticity based on activity-dependent appearance and disappearance of synaptic connections. The results show that adaptive rewiring can consolidate the effects of synaptic plasticity.
Article
Engineering, Electrical & Electronic
Jongkil Park, YeonJoo Jeong, Jaewook Kim, Suyoun Lee, Joon Young Kwak, Jong-Keuk Park, Inho Kim
Summary: In this study, a novel neuron implementation model is proposed, which enhances neural and synaptic dynamics using time-embedded floating-point arithmetic for better biological plausibility and low-power consumption. The proposed algorithm enables sharing temporal information with a membrane potential to minimize memory usage and reduce dynamic power consumption.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Engineering, Mechanical
Halgurd Taher, Daniele Avitabile, Mathieu Desroches
Summary: We report a detailed analysis on the emergence of bursting in a recently developed neural mass model that includes short-term synaptic plasticity. The study reveals the importance of synaptic dynamics in bursting activity and the complex process of bursting initiation.
NONLINEAR DYNAMICS
(2022)
Article
Computer Science, Artificial Intelligence
Chih-Hsu Huang, Chou-Ching K. Lin
Summary: The density-based neural mass model (dNMM) is a novel approach to model the dynamics of adaptive exponential integrate-and-fire neurons, capturing essential neuronal features such as voltage-dependent conductance-based synaptic interactions and adaptation of firing rate responses. It accurately estimates firing rate responses of neuronal populations to different inputs and describes the impact of spike-frequency adaptation on the generation of asynchronous irregular activity in excitatory-inhibitory cortical networks. The dNMM is a promising candidate for building large-scale network models involving multiple brain areas due to its biological realism and computational efficiency.
Article
Computer Science, Information Systems
Donghyung Yoo, Doo Seok Jeong
Summary: This study proposes a novel alternative to convolution in spiking neural networks (SNNs) called SNNs with trainable dynamic time-surfaces (DTS-SNNs), aiming to improve computational efficiency. Through evaluation on real-world event-based datasets, the results demonstrate high classification accuracies and significant improvements in computational efficiency for DTS-SNNs.
Article
Computer Science, Artificial Intelligence
Dmitrii Zendrikov, Alexander Paraskevov
Summary: This study demonstrates that networks of excitatory neurons with stochastic spontaneous spiking activity and shortterm synaptic plasticity can exhibit spontaneous repetitive synchronization, leading to population spikes. The key factor for this phenomenon is the nonlinear modulation of neuronal interactions by synaptic plasticity, resulting in circular traveling waves of population spikes in two-dimensional networks with decreased connection probability over distance. Despite non-stationary nucleation sites, the population spikes can occur regularly without stochastic resonance, resembling respiratory rhythms. The spatiotemporal effects observed also serve as an example of transient chimera patterns.
Article
Mathematics
Sergey V. V. Stasenko, Victor B. B. Kazantsev
Summary: This study investigates the mathematical model of the spiking neural network (SNN) supplied by astrocytes. Astrocytes are brain cells that do not have electrical excitability but can induce chemical modulations of neuronal firing. The study analyzes the influence of astrocytes on images encoded in the form of dynamic spiking patterns in the SNN. The activation of astrocytes significantly suppresses noise influence and improves the dynamic image representation by the SNN.
Article
Physics, Fluids & Plasmas
M. N. Najafi, S. Tizdast, J. Cheraghalizadeh, H. N. Dashti
Summary: This paper investigates invasion percolation (IP) with impermeable regions and pore structures modeled by different types of site percolation. The critical exponents change considerably only near the critical points for Ising-correlated cases, while remaining robust for ordinary percolation. Long-range interactions show completely different properties from normal IP, with distinct fractal dimensions and time dependencies in the thermodynamic limit.
Article
Biology
Soren Wainio-Theberge, Annemarie Wolff, Georg Northoff
Summary: Wainio-Theberge et al. investigated the relationship between spontaneous and evoked neural activity using large-scale magnetoencephalographic and electroencephalographic datasets. They demonstrated that multiple electrophysiological variables exhibit distinct relationships between their spontaneous and evoked activity, which has implications for experimental design and analysis.
COMMUNICATIONS BIOLOGY
(2021)
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
Operations Research & Management Science
Stefanny Ramirez, Dario Bauso
Summary: We study dynamic games with strategic complements, where each player is modeled by a scalar flow dynamical system. We prove that two-threshold strategies, like the (s, S) strategies used in inventory control, are mean-field equilibrium strategies in dynamic games with a large number of players. Furthermore, we provide conditions for the convergence of the nonstationary mean-field equilibrium to the stationary one in the limit.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Zuogong Yue, Johan Thunberg, Wei Pan, Lennart Ljung, Jorge Goncalves
Summary: This paper addresses the reconstruction of dynamic networks from heterogeneous datasets under the assumption that the underlying networks share the same Boolean structure across all experiments. Parametric models and group sparsity methods are proposed to assure network sparsity and structure consistency. The performance of the proposed methods is benchmarked in numerical simulation.
Article
Chemistry, Physical
C. Henchiri, T. Mnasri, A. Benali, E. Dhahri, M. A. Valente
Summary: In this study, critical exponents for La1-x?xMnO3 compounds prepared by the sol-gel method were investigated. The samples exhibited second-order transition in line with the Banerjee Criterion, with the best models being mean field and tricritical mean field models. The critical exponents determined by various methods were consistent with the experimental results, confirming the universality class.
CHEMICAL PHYSICS LETTERS
(2021)
Article
Engineering, Multidisciplinary
Yongkang Zhou, Xiaoqiong Li, Junjie Zhou, Xingfen Tang
Summary: A hardware-oriented framework for compressing and enhancing infrared images is proposed in this study. The framework aims to achieve high-quality imaging with minimal delay. Key improvements, such as anisotropic guided filtering for layering, dynamic range compression algorithm based on local information histogram statistics, and low-complexity adaptive two-dimensional non-local mean algorithm, significantly reduce latency and resource requirements.
Article
Neurosciences
Stefan Dasbach, Tom Tetzlaff, Markus Diesmann, Johanna Senk
Summary: This study explores the effects of limited synaptic weight resolution on the dynamics of spiking neuronal networks, finding that a naive discretization may distort spike-train statistics, but preserving the mean and variance of total synaptic input currents can maintain firing statistics for certain network types. Even with a discretization of synaptic weights, substantial deviations in firing statistics may occur, emphasizing the importance of careful validation and preservation of specific network characteristics.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Endocrinology & Metabolism
Alessia Tagliavini, Joel Tabak, Richard Bertram, Morten Gram Pedersen
AMERICAN JOURNAL OF PHYSIOLOGY-ENDOCRINOLOGY AND METABOLISM
(2016)
Article
Endocrinology & Metabolism
Peter J. Duncan, Joel Tabak, Peter Ruth, Richard Bertram, Michael J. Shipston
Article
Biology
Dvir Blivis, Gal Haspel, Philip Z. Mannes, Michael J. O'Donovan, Michael J. Iadarola
Article
Biology
Melanie Falgairolle, Joshua G. Puhl, Avinash Pujala, Wenfang Liu, Michael J. O'Donovan
Review
Endocrinology & Metabolism
Eder Zavala, Kyle C. A. Wedgwood, Margaritis Voliotis, Joel Tabak, Francesca Spiga, Stafford L. Lightman, Krasimira Tsaneva-Atanasova
TRENDS IN ENDOCRINOLOGY AND METABOLISM
(2019)
Article
Biochemistry & Molecular Biology
Melanie Falgairolle, Michael J. O'Donovan
Article
Biology
Nathan H. Williamson, Rea Ravin, Dan Benjamini, Hellmut Merkle, Melanie Falgairolle, Michael James O'Donovan, Dvir Blivis, Dave Ide, Teddy X. Cai, Nima S. Ghorashi, Ruiliang Bai, Peter J. Basser
Article
Biology
Elif Koksal Ersoz, Mathieu Desroches, Antoni Guillamon, John Rinzel, Joel Tabak
JOURNAL OF MATHEMATICAL BIOLOGY
(2020)
Review
Neurosciences
Melanie Falgairolle, Michael J. O'Donovan
FRONTIERS IN MOLECULAR NEUROSCIENCE
(2020)
Article
Biochemical Research Methods
Nathan H. Williamson, Rea Ravin, Teddy X. Cai, Dan Benjamini, Melanie Falgairolle, Michael J. O'Donovan, Peter J. Basser
JOURNAL OF MAGNETIC RESONANCE
(2020)
Article
Physiology
Eder Zavala, Margaritis Voliotis, Tanja Zerenner, Joel Tabak, Jamie J. Walker, Xiao Feng Li, John R. Terry, Stafford L. Lightman, Kevin O'Byrne, Krasimira Tsaneva-Atanasova
FRONTIERS IN PHYSIOLOGY
(2020)
Article
Neurosciences
Melanie Falgairolle, Michael J. O'Donovan
Summary: Research has shown that the depolarization of spinal inhibitory interneurons expressing channelrhodopsin can have opposite effects on locomotor rhythms depending on the activation mode of the locomotor circuitry. This suggests that the function of V1 neurons is dependent on how the locomotor rhythm is activated, and that the functional organization of corresponding locomotor networks also differs.
JOURNAL OF NEUROSCIENCE
(2021)
Review
Physiology
Melanie Falgairolle, Michael J. O'Donovan
CURRENT OPINION IN PHYSIOLOGY
(2019)
Article
Multidisciplinary Sciences
Dvir Blivis, Melanie Falgairolle, Michael J. O'Donovan
SCIENTIFIC REPORTS
(2019)
Article
Neurosciences
Avinash Pujala, Dvir Blivis, Michael J. O'Donovan