1
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Kumar M, Gupta S. Route to synchronization in coupled phase oscillators with frequency-dependent coupling: Explosive or continuous? Phys Rev E 2022; 106:044310. [PMID: 36397479 DOI: 10.1103/physreve.106.044310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
Interconnected dynamical systems often transition between states of incoherence and synchronization due to changes in system parameters. These transitions could be continuous (gradual) or explosive (sudden) and may result in failures, which makes determining their nature important. In this study, we abstract dynamical networks as an ensemble of globally coupled Kuramoto-like phase oscillators with frequency-dependent coupling and investigate the mechanisms for transition between incoherent and synchronized dynamics. The characteristics that dictate a continuous or explosive route to synchronization are the distribution of the natural frequencies of the oscillators, quantified by a probability density function g(ω), and the relation between the coupling strength and natural frequency of an oscillator, defined by a frequency-coupling strength correlation function f(ω). Our main results are conditions on f(ω) and g(ω) that result in continuous or explosive routes to synchronization and explain the underlying physics. The analytical developments are validated through numerical examples.
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Affiliation(s)
- Mohit Kumar
- Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, India
| | - Sayan Gupta
- Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai 600036, India and Complex Systems and Dynamics Group, Indian Institute of Technology Madras, Chennai, 600036, India
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2
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Servilha-Menezes G, Garcia-Cairasco N. A complex systems view on the current hypotheses of epilepsy pharmacoresistance. Epilepsia Open 2022; 7 Suppl 1:S8-S22. [PMID: 35253410 PMCID: PMC9340300 DOI: 10.1002/epi4.12588] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 02/22/2022] [Accepted: 02/27/2022] [Indexed: 11/11/2022] Open
Abstract
Drug-resistant epilepsy remains to this day as a highly prevalent condition affecting around one-third of patients with epilepsy, despite all the research and the development of several new antiseizure medications (ASMs) over the last decades. Epilepsies are multifactorial complex diseases, commonly associated with psychiatric, neurological, and somatic comorbidities. Thus, to solve the puzzling problem of pharmacoresistance, the diagnosis and modeling of epilepsy and comorbidities need to change toward a complex system approach. In this review, we have summarized the sequence of events for the definition of epilepsies and comorbidities, the search for mechanisms, and the major hypotheses of pharmacoresistance, drawing attention to some of the many converging aspects between the proposed mechanisms, their supporting evidence, and comorbidities-related alterations. The use of systems biology applied to epileptology may lead to the discovery of new targets and the development of new ASMs, as may advance our understanding of the epilepsies and their comorbidities, providing much deeper insight on multidrug pharmacoresistance.
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Affiliation(s)
- Gabriel Servilha-Menezes
- Department of Physiology, Ribeirão Preto Medical School, University of São Paulo (FMRP-SP), Ribeirão Preto, São Paulo, Brazil
| | - Norberto Garcia-Cairasco
- Department of Physiology, Ribeirão Preto Medical School, University of São Paulo (FMRP-SP), Ribeirão Preto, São Paulo, Brazil.,Department of Neurosciences and Behavioral Sciences, Ribeirão Preto Medical School, University of São Paulo (FMRP-SP), Ribeirão Preto, São Paulo, Brazil
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3
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Lomas JD, Lin A, Dikker S, Forster D, Lupetti ML, Huisman G, Habekost J, Beardow C, Pandey P, Ahmad N, Miyapuram K, Mullen T, Cooper P, van der Maden W, Cross ES. Resonance as a Design Strategy for AI and Social Robots. Front Neurorobot 2022; 16:850489. [PMID: 35574227 PMCID: PMC9097027 DOI: 10.3389/fnbot.2022.850489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/23/2022] [Indexed: 11/20/2022] Open
Abstract
Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human-robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of "sympathetic resonance" as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human-robot interactions.
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Affiliation(s)
- James Derek Lomas
- Department of Human Centered Design, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Albert Lin
- Center for Human Frontiers, Qualcomm Institute, University of California, San Diego, San Diego, CA, United States
| | - Suzanne Dikker
- Department of Psychology, New York University, New York, NY, United States
- Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Deborah Forster
- Center for Human Frontiers, Qualcomm Institute, University of California, San Diego, San Diego, CA, United States
| | - Maria Luce Lupetti
- Department of Human Centered Design, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Gijs Huisman
- Department of Human Centered Design, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Julika Habekost
- The Design Lab, California Institute of Information and Communication Technologies, University of California, San Diego, San Diego, CA, United States
| | - Caiseal Beardow
- Department of Human Centered Design, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Pankaj Pandey
- Centre for Cognitive and Brain Sciences, Indian Institute of Technology, Gandhinagar, India
| | - Nashra Ahmad
- Centre for Cognitive and Brain Sciences, Indian Institute of Technology, Gandhinagar, India
| | - Krishna Miyapuram
- Centre for Cognitive and Brain Sciences, Indian Institute of Technology, Gandhinagar, India
| | - Tim Mullen
- Intheon Labs, San Diego, CA, United States
| | - Patrick Cooper
- Department of Physics, Duquesne University, Pittsburgh, PA, United States
| | - Willem van der Maden
- Department of Human Centered Design, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Emily S. Cross
- Social Robotics, Institute of Neuroscience and Psychology, School of Computing Science, University of Glasgow, Glasgow, United Kingdom
- SOBA Lab, School of Psychology, Macquarie University, Sydney, NSW, Australia
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4
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Effects of Synaptic Pruning on Phase Synchronization in Chimera States of Neural Network. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041942] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
This research explores the effect of synaptic pruning on a ring-shaped neural network of non-locally coupled FitzHugh–Nagumo (FHN) oscillators. The neurons in the pruned region synchronize with each other, and they repel the coherent domain of the chimera states. Furthermore, the width of the pruned region decides the precision and efficiency of the control effect on the position of coherent domains. This phenomenon gives a systematic comprehension of the relation between pruning and synchronization in neural networks from a new aspect that has never been addressed. An explanation of this mechanism is also given.
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5
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Cao J, Zhao Y, Shan X, Wei H, Guo Y, Chen L, Erkoyuncu JA, Sarrigiannis PG. Brain functional and effective connectivity based on electroencephalography recordings: A review. Hum Brain Mapp 2022; 43:860-879. [PMID: 34668603 PMCID: PMC8720201 DOI: 10.1002/hbm.25683] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 09/10/2021] [Accepted: 09/27/2021] [Indexed: 12/02/2022] Open
Abstract
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.
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Affiliation(s)
- Jun Cao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
| | - Yifan Zhao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
| | - Xiaocai Shan
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
- Institute of Geology and Geophysics, Chinese Academy of SciencesBeijingChina
| | - Hua‐liang Wei
- Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
| | - Yuzhu Guo
- School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
| | - Liangyu Chen
- Department of NeurosurgeryShengjing Hospital of China Medical UniversityShenyangChina
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6
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Sawicki J, Hartmann L, Bader R, Schöll E. Modelling the perception of music in brain network dynamics. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:910920. [PMID: 36926090 PMCID: PMC10013054 DOI: 10.3389/fnetp.2022.910920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022]
Abstract
We analyze the influence of music in a network of FitzHugh-Nagumo oscillators with empirical structural connectivity measured in healthy human subjects. We report an increase of coherence between the global dynamics in our network and the input signal induced by a specific music song. We show that the level of coherence depends crucially on the frequency band. We compare our results with experimental data, which also describe global neural synchronization between different brain regions in the gamma-band range in a time-dependent manner correlated with musical large-scale form, showing increased synchronization just before transitions between different parts in a musical piece (musical high-level events). The results also suggest a separation in musical form-related brain synchronization between high brain frequencies, associated with neocortical activity, and low frequencies in the range of dance movements, associated with interactivity between cortical and subcortical regions.
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Affiliation(s)
- Jakub Sawicki
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Institut für Musikpädagogik, Universität der Künste Berlin, Berlin, Germany.,Fachhochschule Nordwestschweiz FHNW, Basel, Switzerland.,Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
| | - Lenz Hartmann
- Institute of Systematic Musicology, University of Hamburg, Hamburg, Germany
| | - Rolf Bader
- Institute of Systematic Musicology, University of Hamburg, Hamburg, Germany
| | - Eckehard Schöll
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany.,Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universität, Berlin, Germany
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7
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Ding K, Wang H, Li C, Liu F, Yu D. Decreased Right Prefrontal Synchronization Strength and Asymmetry During Joint Attention in the Left-Behind Children: A Functional Near-Infrared Spectroscopy Study. Front Physiol 2021; 12:759788. [PMID: 34867465 PMCID: PMC8634881 DOI: 10.3389/fphys.2021.759788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/11/2021] [Indexed: 11/24/2022] Open
Abstract
Although there are millions of left-behind children in China, the researches on brain structure and functions in left-behind children are not sufficient at the brain imaging level. This study aimed to explore whether there is decreased prefrontal synchronization during joint attention in left-behind children. Sixty children (65.12 ± 6.54 months, 29 males) with 34 left-behind children were recruited. The functional near-infrared spectroscopy (fNIRS) imaging data from the prefrontal cortex during joint attention, as well as behavioral measures (associated with family income, intelligence, language, and social-emotional abilities), were collected. Results verified that brain imaging data and behavioral measures are correlative and support that left-behind children have deficits in social-emotional abilities. More importantly, left-behind children showed decreased synchronization strength and asymmetry in the right middle frontal gyrus during joint attention. The findings suggest that decreased right prefrontal synchronization strength and asymmetry during joint attention might be vulnerability factors in the development of left-behind children.
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Affiliation(s)
- Keya Ding
- Key Laboratory of Child Development and Learning Science, Research Center for Learning Science, Southeast University, Nanjing, China.,School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Hongan Wang
- Key Laboratory of Child Development and Learning Science, Research Center for Learning Science, Southeast University, Nanjing, China.,School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Chuanjiang Li
- Hangzhou College of Early Childhood Teachers' Education, Zhejiang Normal University, Hangzhou, China
| | - Fulin Liu
- Key Laboratory of Child Development and Learning Science, Research Center for Learning Science, Southeast University, Nanjing, China.,School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Dongchuan Yu
- Key Laboratory of Child Development and Learning Science, Research Center for Learning Science, Southeast University, Nanjing, China.,School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.,Department of Child Development and Behavior, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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8
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A Phase Control Method for the Dynamical Attractor of the HR Neuron Model: The Rotation-Transition Process and Its Experimental Realization. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10568-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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9
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Barioni AED, de Aguiar MAM. Ott-Antonsen ansatz for the D-dimensional Kuramoto model: A constructive approach. CHAOS (WOODBURY, N.Y.) 2021; 31:113141. [PMID: 34881619 DOI: 10.1063/5.0069350] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 11/03/2021] [Indexed: 06/13/2023]
Abstract
Kuramoto's original model describes the dynamics and synchronization behavior of a set of interacting oscillators represented by their phases. The system can also be pictured as a set of particles moving on a circle in two dimensions, which allows a direct generalization to particles moving on the surface of higher dimensional spheres. One of the key features of the 2D system is the presence of a continuous phase transition to synchronization as the coupling intensity increases. Ott and Antonsen proposed an ansatz for the distribution of oscillators that allowed them to describe the dynamics of the order parameter with a single differential equation. A similar ansatz was later proposed for the D-dimensional model by using the same functional form of the 2D ansatz and adjusting its parameters. In this article, we develop a constructive method to find the ansatz, similarly to the procedure used in 2D. The method is based on our previous work for the 3D Kuramoto model where the ansatz was constructed using the spherical harmonics decomposition of the distribution function. In the case of motion in a D-dimensional sphere, the ansatz is based on the hyperspherical harmonics decomposition. Our result differs from the previously proposed ansatz and provides a simpler and more direct connection between the order parameter and the ansatz.
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Affiliation(s)
- Ana Elisa D Barioni
- Instituto de Física "Gleb Wataghin," Universidade Estadual de Campinas, Unicamp, 13083-970 Campinas, São Paulo, Brazil
| | - Marcus A M de Aguiar
- Instituto de Física "Gleb Wataghin," Universidade Estadual de Campinas, Unicamp, 13083-970 Campinas, São Paulo, Brazil
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10
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Chutani M, Tadić B, Gupte N. Hysteresis and synchronization processes of Kuramoto oscillators on high-dimensional simplicial complexes with competing simplex-encoded couplings. Phys Rev E 2021; 104:034206. [PMID: 34654179 DOI: 10.1103/physreve.104.034206] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 08/24/2021] [Indexed: 01/21/2023]
Abstract
Recent studies of dynamic properties in complex systems point out the profound impact of hidden geometry features known as simplicial complexes, which enable geometrically conditioned many-body interactions. Studies of collective behaviors on the controlled-structure complexes can reveal the subtle interplay of geometry and dynamics. Here we investigate the phase synchronization (Kuramoto) dynamics under the competing interactions embedded on 1-simplex (edges) and 2-simplex (triangles) faces of a homogeneous four-dimensional simplicial complex. Its underlying network is a 1-hyperbolic graph with the assortative correlations among the node's degrees and the spectral dimension that exceeds d_{s}=4. By numerically solving the set of coupled equations for the phase oscillators associated with the network nodes, we determine the time-averaged system's order parameter to characterize the synchronization level. Our results reveal a variety of synchronization and desynchronization scenarios, including partially synchronized states and nonsymmetrical hysteresis loops, depending on the sign and strength of the pairwise interactions and the geometric frustrations promoted by couplings on triangle faces. For substantial triangle-based interactions, the frustration effects prevail, preventing the complete synchronization and the abrupt desynchronization transition disappears. These findings shed new light on the mechanisms by which the high-dimensional simplicial complexes in natural systems, such as human connectomes, can modulate their native synchronization processes.
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Affiliation(s)
- Malayaja Chutani
- Department of Physics, Indian Institute of Technology Madras, Chennai 600036, India
| | - Bosiljka Tadić
- Department of Theoretical Physics, Jožef Stefan Institute, Ljubljana, Slovenia.,Complexity Science Hub Vienna, Vienna, Austria
| | - Neelima Gupte
- Department of Physics, Indian Institute of Technology Madras, Chennai 600036, India.,Complex Systems and Dynamics Group, Indian Institute of Technology Madras, Chennai 600036, India
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11
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Vejmola Č, Tylš F, Piorecká V, Koudelka V, Kadeřábek L, Novák T, Páleníček T. Psilocin, LSD, mescaline, and DOB all induce broadband desynchronization of EEG and disconnection in rats with robust translational validity. Transl Psychiatry 2021; 11:506. [PMID: 34601495 PMCID: PMC8487430 DOI: 10.1038/s41398-021-01603-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 07/16/2021] [Accepted: 08/18/2021] [Indexed: 12/22/2022] Open
Abstract
Serotonergic psychedelics are recently gaining a lot of attention as a potential treatment of several neuropsychiatric disorders. Broadband desynchronization of EEG activity and disconnection in humans have been repeatedly shown; however, translational data from animals are completely lacking. Therefore, the main aim of our study was to assess the effects of tryptamine and phenethylamine psychedelics (psilocin 4 mg/kg, LSD 0.2 mg/kg, mescaline 100 mg/kg, and DOB 5 mg/kg) on EEG in freely moving rats. A system consisting of 14 cortical EEG electrodes, co-registration of behavioral activity of animals with subsequent analysis only in segments corresponding to behavioral inactivity (resting-state-like EEG) was used in order to reach a high level of translational validity. Analyses of the mean power, topographic brain-mapping, and functional connectivity revealed that all of the psychedelics irrespective of the structural family induced overall and time-dependent global decrease/desynchronization of EEG activity and disconnection within 1-40 Hz. Major changes in activity were localized on the large areas of the frontal and sensorimotor cortex showing some subtle spatial patterns characterizing each substance. A rebound of occipital theta (4-8 Hz) activity was detected at later stages after treatment with mescaline and LSD. Connectivity analyses showed an overall decrease in global connectivity for both the components of cross-spectral and phase-lagged coherence. Since our results show almost identical effects to those known from human EEG/MEG studies, we conclude that our method has robust translational validity.
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Affiliation(s)
- Čestmír Vejmola
- National Institute of Mental Health, Klecany, Czechia
- Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Filip Tylš
- National Institute of Mental Health, Klecany, Czechia
- Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Václava Piorecká
- National Institute of Mental Health, Klecany, Czechia
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czechia
| | | | | | - Tomáš Novák
- National Institute of Mental Health, Klecany, Czechia
| | - Tomáš Páleníček
- National Institute of Mental Health, Klecany, Czechia.
- Third Faculty of Medicine, Charles University, Prague, Czechia.
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12
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Paulo G, Tasinkevych M. Binary mixtures of locally coupled mobile oscillators. Phys Rev E 2021; 104:014204. [PMID: 34412317 DOI: 10.1103/physreve.104.014204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 06/08/2021] [Indexed: 11/07/2022]
Abstract
Synchronized behavior in a system of coupled dynamic objects is a fascinating example of an emerged cooperative phenomena which has been observed in systems as diverse as a group of insects, neural networks, or networks of computers. In many instances, however, the synchronization is undesired because it may lead to system malfunctioning, as in the case of Alzheimer's and Parkinson's diseases, for example. Recent studies of static networks of oscillators have shown that the presence of a small fraction of so-called contrarian oscillators can suppress the undesired network synchronization. On the other hand, it is also known that the mobility of the oscillators can significantly impact their synchronization dynamics. Here, we combine these two ideas-the oscillator mobility and the presence of heterogeneous interactions-and study numerically binary mixtures of phase oscillators performing two-dimensional random walks. Within the framework of a generalized Kuramoto model, we introduce two phase-coupling schemes. The first one is invariant when the types of any two oscillators are swapped, while the second model is not. We demonstrate that the symmetric model does not allow for a complete suppression of the synchronized state. However, it provides means for a robust control of the synchronization timescale by varying the overall number density and the composition of the mixture and the strength of the off-diagonal Kuramoto coupling constant. Instead, the asymmetric model predicts that the coherent state can be eliminated within a subpopulation of normal oscillators and evoked within a subpopulation of the contrarians.
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Affiliation(s)
- Gonçalo Paulo
- Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal and Centro de Física Teórica e Computacional, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Mykola Tasinkevych
- Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal and Centro de Física Teórica e Computacional, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
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13
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Pyragas K, Fedaravičius AP, Pyragienė T. Suppression of synchronous spiking in two interacting populations of excitatory and inhibitory quadratic integrate-and-fire neurons. Phys Rev E 2021; 104:014203. [PMID: 34412351 DOI: 10.1103/physreve.104.014203] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/14/2021] [Indexed: 01/28/2023]
Abstract
Collective oscillations and their suppression by external stimulation are analyzed in a large-scale neural network consisting of two interacting populations of excitatory and inhibitory quadratic integrate-and-fire neurons. In the limit of an infinite number of neurons, the microscopic model of this network can be reduced to an exact low-dimensional system of mean-field equations. Bifurcation analysis of these equations reveals three different dynamic modes in a free network: a stable resting state, a stable limit cycle, and bistability with a coexisting resting state and a limit cycle. We show that in the limit cycle mode, high-frequency stimulation of an inhibitory population can stabilize an unstable resting state and effectively suppress collective oscillations. We also show that in the bistable mode, the dynamics of the network can be switched from a stable limit cycle to a stable resting state by applying an inhibitory pulse to the excitatory population. The results obtained from the mean-field equations are confirmed by numerical simulation of the microscopic model.
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Affiliation(s)
- Kestutis Pyragas
- Department of Fundamental Research, Center for Physical Sciences and Technology, LT-10257 Vilnius, Lithuania
| | - Augustinas P Fedaravičius
- Department of Fundamental Research, Center for Physical Sciences and Technology, LT-10257 Vilnius, Lithuania
| | - Tatjana Pyragienė
- Department of Fundamental Research, Center for Physical Sciences and Technology, LT-10257 Vilnius, Lithuania
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14
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Barne LC, Cravo AM, de Lange FP, Spaak E. Temporal prediction elicits rhythmic preactivation of relevant sensory cortices. Eur J Neurosci 2021; 55:3324-3339. [PMID: 34322927 PMCID: PMC9545120 DOI: 10.1111/ejn.15405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 07/10/2021] [Accepted: 07/24/2021] [Indexed: 11/28/2022]
Abstract
Being able to anticipate events before they happen facilitates stimulus processing. The anticipation of the contents of events is thought to be implemented by the elicitation of prestimulus templates in sensory cortex. In contrast, the anticipation of the timing of events is typically associated with entrainment of neural oscillations. It is so far unknown whether and in which conditions temporal expectations interact with feature‐based expectations, and, consequently, whether entrainment modulates the generation of content‐specific sensory templates. In this study, we investigated the role of temporal expectations in a sensory discrimination task. We presented participants with rhythmically interleaved visual and auditory streams of relevant and irrelevant stimuli while measuring neural activity using magnetoencephalography. We found no evidence that rhythmic stimulation induced prestimulus feature templates. However, we did observe clear anticipatory rhythmic preactivation of the relevant sensory cortices. This oscillatory activity peaked at behaviourally relevant, in‐phase, intervals. Our results suggest that temporal expectations about stimulus features do not behave similarly to explicitly cued, nonrhythmic, expectations, yet elicit a distinct form of modality‐specific preactivation.
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Affiliation(s)
- Louise Catheryne Barne
- Center for Mathematics, Computing and Cognition, Universidade Federal do ABC (UFABC), São Bernardo do Campo, Sao Paolo, Brazil.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.,Département Traitement de l'Information et Systèmes, ONERA, Salon-de-Provence, France
| | - André Mascioli Cravo
- Center for Mathematics, Computing and Cognition, Universidade Federal do ABC (UFABC), São Bernardo do Campo, Sao Paolo, Brazil
| | - Floris P de Lange
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Eelke Spaak
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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15
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One-way dependent clusters and stability of cluster synchronization in directed networks. Nat Commun 2021; 12:4073. [PMID: 34210969 PMCID: PMC8249607 DOI: 10.1038/s41467-021-24363-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 06/16/2021] [Indexed: 11/23/2022] Open
Abstract
Cluster synchronization in networks of coupled oscillators is the subject of broad interest from the scientific community, with applications ranging from neural to social and animal networks and technological systems. Most of these networks are directed, with flows of information or energy that propagate unidirectionally from given nodes to other nodes. Nevertheless, most of the work on cluster synchronization has focused on undirected networks. Here we characterize cluster synchronization in general directed networks. Our first observation is that, in directed networks, a cluster A of nodes might be one-way dependent on another cluster B: in this case, A may remain synchronized provided that B is stable, but the opposite does not hold. The main contribution of this paper is a method to transform the cluster stability problem in an irreducible form. In this way, we decompose the original problem into subproblems of the lowest dimension, which allows us to immediately detect inter-dependencies among clusters. We apply our analysis to two examples of interest, a human network of violin players executing a musical piece for which directed interactions may be either activated or deactivated by the musicians, and a multilayer neural network with directed layer-to-layer connections. Mechanisms of cluster formation in networks with directed links differ from those in undirected networks. Lodi et al. propose a method to compute interdependencies among clusters of nodes in directed networks. They show that clusters can be one-way dependent, as found in social and neural networks.
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On a Quantitative Approach to Clinical Neuroscience in Psychiatry: Lessons from the Kuramoto Model. Harv Rev Psychiatry 2021; 29:318-326. [PMID: 34049338 DOI: 10.1097/hrp.0000000000000301] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The human brain is a complex system comprising subregions that dynamically exchange information between its various parts through synchronization. These dynamic, complex interactions ultimately play a role in perception, emotion, cognition, and behavior, as well as in various maladaptive neurologic and psychiatric processes. It is therefore important to understand how brain dynamics might be implicated in these processes. Over the past few years, network neuroscience and computational neuroscience have highlighted the importance of measures such as metastability (a property whereby members of an oscillating system tend to linger at the edge of synchronicity without permanently becoming synchronized) in quantifying brain dynamics. Altered metastability has been implicated in various psychiatric illnesses, such as traumatic brain injury and Alzheimer's disease. Computational models, which range in complexity, have been used to assess how various parameters affect metastability, synchronization, and functional connectivity. These models, though limited, can act as heuristics in understanding brain dynamics. This article (aimed at the clinical psychiatrist who might not possess an extensive mathematical background) is intended to provide a brief and qualitative summary of studies that have used a specific, highly simplified computational model of coupled oscillators (Kuramoto model) for understanding brain dynamics-which might bear some relevance to clinical psychiatry.
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Simo GR, Njougouo T, Aristides RP, Louodop P, Tchitnga R, Cerdeira HA. Chimera states in a neuronal network under the action of an electric field. Phys Rev E 2021; 103:062304. [PMID: 34271625 DOI: 10.1103/physreve.103.062304] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 05/25/2021] [Indexed: 11/07/2022]
Abstract
The phenomenon of the chimera state symbolizes the coexistence of coherent and incoherent sections of a given population. This phenomenon identified in several physical and biological systems presents several variants, including the multichimera states and the traveling chimera state. Here, we numerically study the influence of a weak external electric field on the dynamics of a network of Hindmarsh-Rose (HR) neurons coupled locally by an electrical interaction and nonlocally by a chemical one. We first focus on the phenomena of traveling chimera states and multicluster oscillating breathers that appear in the electric field's absence. Then in the field's presence, we highlight the presence of a chimera state, a multichimera state, an alternating chimera state, and a multicluster traveling chimera.
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Affiliation(s)
- Gaël R Simo
- Research Unit Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Sciences, University of Dschang, P.O. Box 67, Dschang, Cameroon
| | - Thierry Njougouo
- Research Unit Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Sciences, University of Dschang, P.O. Box 67, Dschang, Cameroon
| | - R P Aristides
- São Paulo State University (UNESP), Instituto de Física Teórica, Rua Doutor Bento Teobaldo Ferraz 271, Bloco II, Barra Funda, 01140-070 São Paulo, Brazil
| | - Patrick Louodop
- Research Unit Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Sciences, University of Dschang, P.O. Box 67, Dschang, Cameroon
| | - Robert Tchitnga
- Research Unit Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Sciences, University of Dschang, P.O. Box 67, Dschang, Cameroon.,Institute of Surface Chemistry and Catalysis, University of Ulm, Albert-Einstein-Allee 47, 89081 Ulm, Germany
| | - Hilda A Cerdeira
- São Paulo State University (UNESP), Instituto de Física Teórica, Rua Doutor Bento Teobaldo Ferraz 271, Bloco II, Barra Funda, 01140-070 São Paulo, Brazil.,Epistemic, Gomez & Gomez Ltda. ME, Avenida Professor Lineu Prestes 2242, Cietec, Sala 244, 05508-000 São Paulo, Brazil
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Shan X, Huo S, Yang L, Cao J, Zou J, Chen L, Sarrigiannis PG, Zhao Y. A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals. IEEE Trans Neural Syst Rehabil Eng 2021; 29:841-851. [PMID: 33909567 DOI: 10.1109/tnsre.2021.3076311] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The time-varying cross-spectrum method has been used to effectively study transient and dynamic brain functional connectivity between non-stationary electroencephalography (EEG) signals. Wavelet-based cross-spectrum is one of the most widely implemented methods, but it is limited by the spectral leakage caused by the finite length of the basic function that impacts the time and frequency resolutions. This paper proposes a new time-frequency brain functional connectivity analysis framework to track the non-stationary association of two EEG signals based on a Revised Hilbert-Huang Transform (RHHT). The framework can estimate the cross-spectrum of decomposed components of EEG, followed by a surrogate significance test. The results of two simulation examples demonstrate that, within a certain statistical confidence level, the proposed framework outperforms the wavelet-based method in terms of accuracy and time-frequency resolution. A case study on classifying epileptic patients and healthy controls using interictal seizure-free EEG data is also presented. The result suggests that the proposed method has the potential to better differentiate these two groups benefiting from the enhanced measure of dynamic time-frequency association.
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Cao J, Grajcar K, Shan X, Zhao Y, Zou J, Chen L, Li Z, Grunewald R, Zis P, De Marco M, Unwin Z, Blackburn D, Sarrigiannis PG. Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102554] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Lodi M, Della Rossa F, Sorrentino F, Storace M. Analyzing synchronized clusters in neuron networks. Sci Rep 2020; 10:16336. [PMID: 33004897 PMCID: PMC7530773 DOI: 10.1038/s41598-020-73269-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 09/15/2020] [Indexed: 11/08/2022] Open
Abstract
The presence of synchronized clusters in neuron networks is a hallmark of information transmission and processing. Common approaches to study cluster synchronization in networks of coupled oscillators ground on simplifying assumptions, which often neglect key biological features of neuron networks. Here we propose a general framework to study presence and stability of synchronous clusters in more realistic models of neuron networks, characterized by the presence of delays, different kinds of neurons and synapses. Application of this framework to two examples with different size and features (the directed network of the macaque cerebral cortex and the swim central pattern generator of a mollusc) provides an interpretation key to explain known functional mechanisms emerging from the combination of anatomy and neuron dynamics. The cluster synchronization analysis is carried out also by changing parameters and studying bifurcations. Despite some modeling simplifications in one of the examples, the obtained results are in good agreement with previously reported biological data.
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Affiliation(s)
- Matteo Lodi
- DITEN, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy
| | - Fabio Della Rossa
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM, 87131, USA
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133, Milan, Italy
| | - Francesco Sorrentino
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Marco Storace
- DITEN, University of Genoa, Via Opera Pia 11a, 16145, Genova, Italy.
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Guevara R, Mateos DM, Pérez Velázquez JL. Consciousness as an Emergent Phenomenon: A Tale of Different Levels of Description. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E921. [PMID: 33286690 PMCID: PMC7597170 DOI: 10.3390/e22090921] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 08/15/2020] [Accepted: 08/19/2020] [Indexed: 01/17/2023]
Abstract
One of the biggest queries in cognitive sciences is the emergence of consciousness from matter. Modern neurobiological theories of consciousness propose that conscious experience is the result of interactions between large-scale neuronal networks in the brain, traditionally described within the realm of classical physics. Here, we propose a generalized connectionist framework in which the emergence of "conscious networks" is not exclusive of large brain areas, but can be identified in subcellular networks exhibiting nontrivial quantum phenomena. The essential feature of such networks is the existence of strong correlations in the system (classical or quantum coherence) and the presence of an optimal point at which the system's complexity and energy dissipation are maximized, whereas free-energy is minimized. This is expressed either by maximization of the information content in large scale functional networks or by achieving optimal efficiency through the quantum Goldilock effect.
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Affiliation(s)
- Ramón Guevara
- Integrative Neuroscience and Cognition Centre (INCC UMR8002), University of Paris and CNRS, 75270 Paris, France
- Department of Physics and Astronomy, University of Padova, 35131 Padova, Italy
| | - Diego M. Mateos
- Department of Science and Technology, Universidad Autónoma de Entre Ríos, Paraná 3100, Argentina;
- Instituto de Matemática Aplicada del Litoral (IMAL-CONICET-UNL), Santa Fe 3000, Argentina
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Fang Y, Wang Z, Gomez J, Datta S, Khan AI, Raychowdhury A. A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks. Front Neurosci 2019; 13:855. [PMID: 31456659 PMCID: PMC6700359 DOI: 10.3389/fnins.2019.00855] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 07/30/2019] [Indexed: 11/13/2022] Open
Abstract
As computational models inspired by the biological neural system, spiking neural networks (SNN) continue to demonstrate great potential in the landscape of artificial intelligence, particularly in tasks such as recognition, inference, and learning. While SNN focuses on achieving high-level intelligence of individual creatures, Swarm Intelligence (SI) is another type of bio-inspired models that mimic the collective intelligence of biological swarms, i.e., bird flocks, fish school and ant colonies. SI algorithms provide efficient and practical solutions to many difficult optimization problems through multi-agent metaheuristic search. Bridging these two distinct subfields of artificial intelligence has the potential to harness collective behavior and learning ability of biological systems. In this work, we explore the feasibility of connecting these two models by implementing a generalized SI model on SNN. In the proposed computing paradigm, we use SNNs to represent agents in the swarm and encode problem solutions with the spike firing rate and with spike timing. The coupled neurons communicate and modulate each other's action potentials through event-driven spikes and synchronize their dynamics around the states of optimal solutions. We demonstrate that such an SI-SNN model is capable of efficiently solving optimization problems, such as parameter optimization of continuous functions and a ubiquitous combinatorial optimization problem, namely, the traveling salesman problem with near-optimal solutions. Furthermore, we demonstrate an efficient implementation of such neural dynamics on an emerging hardware platform, namely ferroelectric field-effect transistor (FeFET) based spiking neurons. Such an emerging in-silico neuron is composed of a compact 1T-1FeFET structure with both excitatory and inhibitory inputs. We show that the designed neuromorphic system can serve as an optimization solver with high-performance and high energy-efficiency.
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Affiliation(s)
- Yan Fang
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Zheng Wang
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Jorge Gomez
- Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Suman Datta
- Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Asif I Khan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Arijit Raychowdhury
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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Abnormal synchronization patterns in the electrical stimulation-contractile response coupling decrease with noise. Biosystems 2019; 180:63-70. [PMID: 30885687 DOI: 10.1016/j.biosystems.2019.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 03/07/2019] [Accepted: 03/12/2019] [Indexed: 11/24/2022]
Abstract
Synchronization theory predicts that if an oscillator interacts with a rhythmical external force, then it should react to a rhythmical force by adjusting its frequency. Furthermore, noise is present in nature, and it affects the nervous and cardiovascular systems. In this paper, we analyze the heart as an oscillator, where noisy periodic electrical stimulation can be regarded as an external forcing. This study aimed to investigate, from an experimental point of view, whether noise can induce synchronization of higher order in the mechanical heart response. A Langendorff heart preparation was used to obtain two variables of the mechanical response, intensity of contractile force and heart rate. The experiments show frequency locking in the electrical stimulation-contractile response coupling with and without noise induced. The role of noise in the response of effector organs invites further investigation.
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Lisitsyn D, Ernst UA. Causally Investigating Cortical Dynamics and Signal Processing by Targeting Natural System Attractors With Precisely Timed (Electrical) Stimulation. Front Comput Neurosci 2019; 13:7. [PMID: 30853906 PMCID: PMC6395860 DOI: 10.3389/fncom.2019.00007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 01/25/2019] [Indexed: 11/13/2022] Open
Abstract
Electrical stimulation is a promising tool for interacting with neuronal dynamics to identify neural mechanisms that underlie cognitive function. Since effects of a single short stimulation pulse typically vary greatly and depend on the current network state, many experimental paradigms have rather resorted to continuous or periodic stimulation in order to establish and maintain a desired effect. However, such an approach explicitly leads to forced and “unnatural” brain activity. Further, continuous stimulation can make it hard to parse the recorded activity and separate neural signal from stimulation artifacts. In this study we propose an alternate strategy: by monitoring a system in realtime, we use the existing preferred states or attractors of the network and apply short and precise pulses in order to switch between those states. When pushed into one of its attractors, one can use the natural tendency of the system to remain in such a state to prolong the effect of a stimulation pulse, opening a larger window of opportunity to observe the consequences on cognitive processing. To elaborate on this idea, we consider flexible information routing in the visual cortex as a prototypical example. When processing a stimulus, neural populations in the visual cortex have been found to engage in synchronized gamma activity. In this context, selective signal routing is achieved by changing the relative phase between oscillatory activity in sending and receiving populations (communication through coherence, CTC). In order to explore how perturbations interact with CTC, we investigate a network of interneuronal gamma (ING) oscillators composed of integrate-and-fire neurons exhibiting similar synchronization and signal routing phenomena. We develop a closed-loop stimulation paradigm based on the phase-response characteristics of the network and demonstrate its ability to establish desired synchronization states. By measuring information content throughout the model, we evaluate the effect of signal contamination caused by the stimulation in relation to the magnitude of the injected pulses and intrinsic noise in the system. Finally, we demonstrate that, up to a critical noise level, precisely timed perturbations can be used to artificially induce the effect of attention by selectively routing visual signals to higher cortical areas.
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Affiliation(s)
- Dmitriy Lisitsyn
- Computational Neuroscience Lab, Institute for Theoretical Physics, Department of Physics, University of Bremen, Bremen, Germany
| | - Udo A Ernst
- Computational Neuroscience Lab, Institute for Theoretical Physics, Department of Physics, University of Bremen, Bremen, Germany
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Protachevicz PR, Borges RR, Reis AS, Borges FS, Iarosz KC, Caldas IL, Lameu EL, Macau EEN, Viana RL, Sokolov IM, Ferrari FAS, Kurths J, Batista AM, Lo CY, He Y, Lin CP. Synchronous behaviour in network model based on human cortico-cortical connections. Physiol Meas 2018; 39:074006. [DOI: 10.1088/1361-6579/aace91] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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