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Qian W, Papadopoulos L, Lu Z, Kroma-Wiley KA, Pasqualetti F, Bassett DS. Path-dependent dynamics induced by rewiring networks of inertial oscillators. Phys Rev E 2022; 105:024304. [PMID: 35291167 DOI: 10.1103/physreve.105.024304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 10/14/2021] [Indexed: 06/14/2023]
Abstract
In networks of coupled oscillators, it is of interest to understand how interaction topology affects synchronization. Many studies have gained key insights into this question by studying the classic Kuramoto oscillator model on static networks. However, new questions arise when the network structure is time varying or when the oscillator system is multistable, the latter of which can occur when an inertial term is added to the Kuramoto model. While the consequences of evolving topology and multistability on collective behavior have been examined separately, real-world systems such as gene regulatory networks and the brain may exhibit these properties simultaneously. It is thus relevant to ask how time-varying network connectivity impacts synchronization in systems that can exhibit multistability. To address this question, we study how the dynamics of coupled Kuramoto oscillators with inertia are affected when the topology of the underlying network changes in time. We show that hysteretic synchronization behavior in networks of coupled inertial oscillators can be driven by changes in connection topology alone. Moreover, we find that certain fixed-density rewiring schemes induce significant changes to the level of global synchrony that remain even after the network returns to its initial configuration, and we show that these changes are robust to a wide range of network perturbations. Our findings highlight that the specific progression of network topology over time, in addition to its initial or final static structure, can play a considerable role in modulating the collective behavior of systems evolving on complex networks.
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Affiliation(s)
- William Qian
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Lia Papadopoulos
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Zhixin Lu
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Keith A Kroma-Wiley
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, California 92521, USA
| | - Dani S Bassett
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Mechanical Engineering, University of California, Riverside, California 92521, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Santa Fe Institute, Santa Fe, New Mexico 87501, USA
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Avalos-Gaytán V, Almendral JA, Leyva I, Battiston F, Nicosia V, Latora V, Boccaletti S. Emergent explosive synchronization in adaptive complex networks. Phys Rev E 2018; 97:042301. [PMID: 29758636 DOI: 10.1103/physreve.97.042301] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Indexed: 06/08/2023]
Abstract
Adaptation plays a fundamental role in shaping the structure of a complex network and improving its functional fitting. Even when increasing the level of synchronization in a biological system is considered as the main driving force for adaptation, there is evidence of negative effects induced by excessive synchronization. This indicates that coherence alone cannot be enough to explain all the structural features observed in many real-world networks. In this work, we propose an adaptive network model where the dynamical evolution of the node states toward synchronization is coupled with an evolution of the link weights based on an anti-Hebbian adaptive rule, which accounts for the presence of inhibitory effects in the system. We found that the emergent networks spontaneously develop the structural conditions to sustain explosive synchronization. Our results can enlighten the shaping mechanisms at the heart of the structural and dynamical organization of some relevant biological systems, namely, brain networks, for which the emergence of explosive synchronization has been observed.
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Affiliation(s)
- Vanesa Avalos-Gaytán
- Research Center in Applied Mathematics, Universidad Autónoma de Coahuila, Saltillo, Coahuila 25115, Mexico
| | - Juan A Almendral
- Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain and Complex Systems Group & GISC, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
| | - I Leyva
- Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain and Complex Systems Group & GISC, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
| | - F Battiston
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - V Nicosia
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - V Latora
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - S Boccaletti
- CNR-Institute of Complex Systems, Via Madonna del Piano, 10, 50019 Sesto Fiorentino, Florence, Italy and Embassy of Italy in Israel, Trade Tower, 25 Hamered St., 68125 Tel Aviv, Israel
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3
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Yuan WJ, Zhou JF, Sendiña-Nadal I, Boccaletti S, Wang Z. Adaptive control of dynamical synchronization on evolving networks with noise disturbances. Phys Rev E 2018; 97:022211. [PMID: 29548131 DOI: 10.1103/physreve.97.022211] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Indexed: 11/07/2022]
Abstract
In real-world networked systems, the underlying structure is often affected by external and internal unforeseen factors, making its evolution typically inaccessible. An adaptive strategy was introduced for maintaining synchronization on unpredictably evolving networks [Sorrentino and Ott, Phys. Rev. Lett. 100, 114101 (2008)PRLTAO0031-900710.1103/PhysRevLett.100.114101], which yet does not consider the noise disturbances widely existing in networks' environments. We provide here strategies to control dynamical synchronization on slowly and unpredictably evolving networks subjected to noise disturbances which are observed at the node and at the communication channel level. With our strategy, the nodes' coupling strength is adaptively adjusted with the aim of controlling synchronization, and according only to their received signal and noise disturbances. We first provide a theoretical analysis of the control scheme by introducing an error potential function to seek for the minimization of the synchronization error. Then, we show numerical experiments which verify our theoretical results. In particular, it is found that our adaptive strategy is effective even for the case in which the dynamics of the uncontrolled network would be explosive (i.e., the states of all the nodes would diverge to infinity).
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Affiliation(s)
- Wu-Jie Yuan
- College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China.,College of Information, Huaibei Normal University, Huaibei 235000, China
| | - Jian-Fang Zhou
- College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China
| | - Irene Sendiña-Nadal
- Complex Systems Group & GISC, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain.,Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Stefano Boccaletti
- CNR-Institute of Complex Systems, Via Madonna del Piano, 10, 50019 Sesto Fiorentino, Florence, Italy
| | - Zhen Wang
- School of Mechanical Engineering and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China
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4
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Neural electrical activity and neural network growth. Neural Netw 2018; 101:15-24. [PMID: 29475142 DOI: 10.1016/j.neunet.2018.02.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 01/31/2018] [Accepted: 02/01/2018] [Indexed: 01/19/2023]
Abstract
The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization.
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Papadopoulos L, Kim JZ, Kurths J, Bassett DS. Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators. CHAOS (WOODBURY, N.Y.) 2017; 27:073115. [PMID: 28764402 PMCID: PMC5552408 DOI: 10.1063/1.4994819] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 07/07/2017] [Indexed: 05/06/2023]
Abstract
Synchronization of non-identical oscillators coupled through complex networks is an important example of collective behavior, and it is interesting to ask how the structural organization of network interactions influences this process. Several studies have explored and uncovered optimal topologies for synchronization by making purposeful alterations to a network. On the other hand, the connectivity patterns of many natural systems are often not static, but are rather modulated over time according to their dynamics. However, this co-evolution and the extent to which the dynamics of the individual units can shape the organization of the network itself are less well understood. Here, we study initially randomly connected but locally adaptive networks of Kuramoto oscillators. In particular, the system employs a co-evolutionary rewiring strategy that depends only on the instantaneous, pairwise phase differences of neighboring oscillators, and that conserves the total number of edges, allowing the effects of local reorganization to be isolated. We find that a simple rule-which preserves connections between more out-of-phase oscillators while rewiring connections between more in-phase oscillators-can cause initially disordered networks to organize into more structured topologies that support enhanced synchronization dynamics. We examine how this process unfolds over time, finding a dependence on the intrinsic frequencies of the oscillators, the global coupling, and the network density, in terms of how the adaptive mechanism reorganizes the network and influences the dynamics. Importantly, for large enough coupling and after sufficient adaptation, the resulting networks exhibit interesting characteristics, including degree-frequency and frequency-neighbor frequency correlations. These properties have previously been associated with optimal synchronization or explosive transitions in which the networks were constructed using global information. On the contrary, by considering a time-dependent interplay between structure and dynamics, this work offers a mechanism through which emergent phenomena and organization can arise in complex systems utilizing local rules.
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Affiliation(s)
- Lia Papadopoulos
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Jason Z Kim
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research - Telegraphenberg A 31, 14473 Potsdam, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
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6
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Synchronous slowing down in coupled logistic maps via random network topology. Sci Rep 2016; 6:23448. [PMID: 27021897 PMCID: PMC4810433 DOI: 10.1038/srep23448] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 03/04/2016] [Indexed: 11/10/2022] Open
Abstract
The speed and paths of synchronization play a key role in the function of a system, which has not received enough attention up to now. In this work, we study the synchronization process of coupled logistic maps that reveals the common features of low-dimensional dissipative systems. A slowing down of synchronization process is observed, which is a novel phenomenon. The result shows that there are two typical kinds of transient process before the system reaches complete synchronization, which is demonstrated by both the coupled multiple-period maps and the coupled multiple-band chaotic maps. When the coupling is weak, the evolution of the system is governed mainly by the local dynamic, i.e., the node states are attracted by the stable orbits or chaotic attractors of the single map and evolve toward the synchronized orbit in a less coherent way. When the coupling is strong, the node states evolve in a high coherent way toward the stable orbit on the synchronized manifold, where the collective dynamics dominates the evolution. In a mediate coupling strength, the interplay between the two paths is responsible for the slowing down. The existence of different synchronization paths is also proven by the finite-time Lyapunov exponent and its distribution.
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7
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Fang P, Hou Y, Nan Y. Synchronization of two homodromy rotors installed on a double vibro-body in a coupling vibration system. PLoS One 2015; 10:e0126069. [PMID: 25993472 PMCID: PMC4437654 DOI: 10.1371/journal.pone.0126069] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Accepted: 03/28/2015] [Indexed: 11/18/2022] Open
Abstract
A new mechanism is proposed to implement synchronization of the two unbalanced rotors in a vibration system, which consists of a double vibro-body, two induction motors and spring foundations. The coupling relationship between the vibro-bodies is ascertained with the Laplace transformation method for the dynamics equation of the system obtained with the Lagrange's equation. An analytical approach, the average method of modified small parameters, is employed to study the synchronization characteristics between the two unbalanced rotors, which is converted into that of existence and the stability of zero solutions for the non-dimensional differential equations of the angular velocity disturbance parameters. By assuming the disturbance parameters that infinitely approach to zero, the synchronization condition for the two rotors is obtained. It indicated that the absolute value of the residual torque between the two motors should be equal to or less than the maximum of their coupling torques. Meanwhile, the stability criterion of synchronization is derived with the Routh-Hurwitz method, and the region of the stable phase difference is confirmed. At last, computer simulations are preformed to verify the correctness of the approximate solution of the theoretical computation for the stable phase difference between the two unbalanced rotors, and the results of theoretical computation is in accordance with that of computer simulations. To sum up, only the parameters of the vibration system satisfy the synchronization condition and the stability criterion of the synchronization, the two unbalanced rotors can implement the synchronization operation.
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Affiliation(s)
- Pan Fang
- School of Mechanical Engineering, Southwest Petroleum University, Chengdu, China
| | - Yongjun Hou
- School of Mechanical Engineering, Southwest Petroleum University, Chengdu, China
- * E-mail:
| | - Yanghai Nan
- Department of Mechanical Engineering and Robotics, Universite Libre de Bruxelles, Brussel, Belgium
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8
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Huang K, Wang T, Cheng Y, Zheng X. Effect of heterogeneous investments on the evolution of cooperation in spatial public goods game. PLoS One 2015; 10:e0120317. [PMID: 25781345 PMCID: PMC4363493 DOI: 10.1371/journal.pone.0120317] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Accepted: 02/09/2015] [Indexed: 11/26/2022] Open
Abstract
Understanding the emergence of cooperation in spatial public goods game remains a grand challenge across disciplines. In most previous studies, it is assumed that the investments of all the cooperators are identical, and often equal to 1. However, it is worth mentioning that players are diverse and heterogeneous when choosing actions in the rapidly developing modern society and researchers have shown more interest to the heterogeneity of players recently. For modeling the heterogeneous players without loss of generality, it is assumed in this work that the investment of a cooperator is a random variable with uniform distribution, the mean value of which is equal to 1. The results of extensive numerical simulations convincingly indicate that heterogeneous investments can promote cooperation. Specifically, a large value of the variance of the random variable can decrease the two critical values for the result of behavioral evolution effectively. Moreover, the larger the variance is, the better the promotion effect will be. In addition, this article has discussed the impact of heterogeneous investments when the coevolution of both strategy and investment is taken into account. Comparing the promotion effect of coevolution of strategy and investment with that of strategy imitation only, we can conclude that the coevolution of strategy and investment decreases the asymptotic fraction of cooperators by weakening the heterogeneity of investments, which further demonstrates that heterogeneous investments can promote cooperation in spatial public goods game.
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Affiliation(s)
- Keke Huang
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Tao Wang
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Yuan Cheng
- School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Xiaoping Zheng
- Department of Automation, Tsinghua University, Beijing, 100084, China
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9
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Du Y, Gao C, Chen X, Hu Y, Sadiq R, Deng Y. A new closeness centrality measure via effective distance in complex networks. CHAOS (WOODBURY, N.Y.) 2015; 25:033112. [PMID: 25833434 DOI: 10.1063/1.4916215] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Closeness centrality (CC) measure, as a well-known global measure, is widely applied in many complex networks. However, the classical CC presents many problems for flow networks since these networks are directed and weighted. To address these issues, we propose an effective distance based closeness centrality (EDCC), which uses effective distance to replace conventional geographic distance and binary distance obtained by Dijkstra's shortest path algorithm. The proposed EDCC considers not only the global structure of the network but also the local information of nodes. And it can be well applied in directed or undirected, weighted or unweighted networks. Susceptible-Infected model is utilized to evaluate the performance by using the spreading rate and the number of infected nodes. Numerical examples simulated on four real networks are given to show the effectiveness of the proposed EDCC.
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Affiliation(s)
- Yuxian Du
- School of Computer and Information Science, Southwest University, Chongqing 400715, China
| | - Cai Gao
- School of Computer and Information Science, Southwest University, Chongqing 400715, China
| | - Xin Chen
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, 92 Weijin Road, Tianjin 300072, China
| | - Yong Hu
- Institute of Business Intelligence and Knowledge Discovery, Guangdong University of Foreign Studies, Guangzhou 510006, China
| | - Rehan Sadiq
- School of Engineering, University of British Columbia Okanagan, 3333 University Way, Kelowna, British Columbia V1V 1V7, Canada
| | - Yong Deng
- School of Computer and Information Science, Southwest University, Chongqing 400715, China
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10
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Timme N, Ito S, Myroshnychenko M, Yeh FC, Hiolski E, Hottowy P, Beggs JM. Multiplex networks of cortical and hippocampal neurons revealed at different timescales. PLoS One 2014; 9:e115764. [PMID: 25536059 PMCID: PMC4275261 DOI: 10.1371/journal.pone.0115764] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Accepted: 11/03/2014] [Indexed: 12/31/2022] Open
Abstract
Recent studies have emphasized the importance of multiplex networks--interdependent networks with shared nodes and different types of connections--in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy--an information theoretic quantity that can be used to measure linear and nonlinear interactions--to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons ("hubs") were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first systematic study of temporally dependent multiplex networks among individual neurons.
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Affiliation(s)
- Nicholas Timme
- Department of Physics, Indiana University, Bloomington, Indiana, 47405, United States of America
| | - Shinya Ito
- Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, California, 95064, United States of America
| | - Maxym Myroshnychenko
- Program in Neuroscience, Indiana University, Bloomington, Indiana, 47405, United States of America
| | - Fang-Chin Yeh
- Department of Physics, Indiana University, Bloomington, Indiana, 47405, United States of America
| | - Emma Hiolski
- Department of Microbiology & Environmental Toxicology, University of California Santa Cruz, Santa Cruz, California, 95064, United States of America
| | - Pawel Hottowy
- Physics and Applied Computer Science, AGH University of Science and Technology, 30–059, Krakow, Poland
| | - John M. Beggs
- Department of Physics, Indiana University, Bloomington, Indiana, 47405, United States of America
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11
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Quattrociocchi W, Caldarelli G, Scala A. Self-healing networks: redundancy and structure. PLoS One 2014; 9:e87986. [PMID: 24533065 PMCID: PMC3922772 DOI: 10.1371/journal.pone.0087986] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2013] [Accepted: 01/01/2014] [Indexed: 11/18/2022] Open
Abstract
We introduce the concept of self-healing in the field of complex networks modelling; in particular, self-healing capabilities are implemented through distributed communication protocols that exploit redundant links to recover the connectivity of the system. We then analyze the effect of the level of redundancy on the resilience to multiple failures; in particular, we measure the fraction of nodes still served for increasing levels of network damages. Finally, we study the effects of redundancy under different connectivity patterns—from planar grids, to small-world, up to scale-free networks—on healing performances. Small-world topologies show that introducing some long-range connections in planar grids greatly enhances the resilience to multiple failures with performances comparable to the case of the most resilient (and least realistic) scale-free structures. Obvious applications of self-healing are in the important field of infrastructural networks like gas, power, water, oil distribution systems.
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Affiliation(s)
- Walter Quattrociocchi
- Laboratory for the modeling of biological and socio-technical systems, Northeastern University, Boston, Massachusetts, United States of America
- LIMS the London Institute of Mathematical Sciences, Mayfair, London, United Kingdom
- IMT Alti Studi Lucca, Lucca, Italy
| | - Guido Caldarelli
- LIMS the London Institute of Mathematical Sciences, Mayfair, London, United Kingdom
- IMT Alti Studi Lucca, Lucca, Italy
- ISC-CNR Uos “Sapienza”, Roma, Italy
| | - Antonio Scala
- LIMS the London Institute of Mathematical Sciences, Mayfair, London, United Kingdom
- IMT Alti Studi Lucca, Lucca, Italy
- ISC-CNR Uos “Sapienza”, Roma, Italy
- * E-mail:
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12
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Yuan WJ, Zhou JF, Zhou C. Network evolution induced by asynchronous stimuli through spike-timing-dependent plasticity. PLoS One 2013; 8:e84644. [PMID: 24391971 PMCID: PMC3877323 DOI: 10.1371/journal.pone.0084644] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Accepted: 11/25/2013] [Indexed: 11/21/2022] Open
Abstract
In sensory neural system, external asynchronous stimuli play an important role in perceptual learning, associative memory and map development. However, the organization of structure and dynamics of neural networks induced by external asynchronous stimuli are not well understood. Spike-timing-dependent plasticity (STDP) is a typical synaptic plasticity that has been extensively found in the sensory systems and that has received much theoretical attention. This synaptic plasticity is highly sensitive to correlations between pre- and postsynaptic firings. Thus, STDP is expected to play an important role in response to external asynchronous stimuli, which can induce segregative pre- and postsynaptic firings. In this paper, we study the impact of external asynchronous stimuli on the organization of structure and dynamics of neural networks through STDP. We construct a two-dimensional spatial neural network model with local connectivity and sparseness, and use external currents to stimulate alternately on different spatial layers. The adopted external currents imposed alternately on spatial layers can be here regarded as external asynchronous stimuli. Through extensive numerical simulations, we focus on the effects of stimulus number and inter-stimulus timing on synaptic connecting weights and the property of propagation dynamics in the resulting network structure. Interestingly, the resulting feedforward structure induced by stimulus-dependent asynchronous firings and its propagation dynamics reflect both the underlying property of STDP. The results imply a possible important role of STDP in generating feedforward structure and collective propagation activity required for experience-dependent map plasticity in developing in vivo sensory pathways and cortices. The relevance of the results to cue-triggered recall of learned temporal sequences, an important cognitive function, is briefly discussed as well. Furthermore, this finding suggests a potential application for examining STDP by measuring neural population activity in a cultured neural network.
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Affiliation(s)
- Wu-Jie Yuan
- College of Physics and Electronic Information, Huaibei Normal University, Huaibei, China
- Department of Physics, Centre for Nonlinear Studies and the Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- * E-mail: (WJY); (CZ)
| | - Jian-Fang Zhou
- College of Physics and Electronic Information, Huaibei Normal University, Huaibei, China
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and the Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Beijing Computational Science Research Center, Beijing, China
- Research Centre, HKBU Institute of Research and Continuing Education, Virtual University Park Building, South Area Hi-tech Industrial Park, Shenzhen, China
- * E-mail: (WJY); (CZ)
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