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Yamakou ME, Desroches M, Rodrigues S. Synchronization in STDP-driven memristive neural networks with time-varying topology. J Biol Phys 2023; 49:483-507. [PMID: 37656327 PMCID: PMC10651826 DOI: 10.1007/s10867-023-09642-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/07/2023] [Indexed: 09/02/2023] Open
Abstract
Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by spike-timing-dependent plasticity (STDP) and temporal networks subject to homeostatic structural plasticity (HSP) rules remain unclear. Here, we bridge this gap by determining the configurations required to achieve high and stable degrees of complete synchronization (CS) and phase synchronization (PS) in time-varying small-world and random neural networks driven by STDP and HSP. In particular, we found that decreasing P (which enhances the strengthening effect of STDP on the average synaptic weight) and increasing F (which speeds up the swapping rate of synapses between neurons) always lead to higher and more stable degrees of CS and PS in small-world and random networks, provided that the network parameters such as the synaptic time delay [Formula: see text], the average degree [Formula: see text], and the rewiring probability [Formula: see text] have some appropriate values. When [Formula: see text], [Formula: see text], and [Formula: see text] are not fixed at these appropriate values, the degree and stability of CS and PS may increase or decrease when F increases, depending on the network topology. It is also found that the time delay [Formula: see text] can induce intermittent CS and PS whose occurrence is independent F. Our results could have applications in designing neuromorphic circuits for optimal information processing and transmission via synchronization phenomena.
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Affiliation(s)
- Marius E Yamakou
- Department of Data Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058, Erlangen, Germany.
- Max-Planck-Institut für Mathematik in den Naturwissenschaften, Inselstr. 22, 04103, Leipzig, Germany.
| | - Mathieu Desroches
- MathNeuro Project-Team, Inria Center at Université Côte d'Azur, 2004 route des Lucioles - BP 93, 06902, Cedex, Sophia Antipolis, France
| | - Serafim Rodrigues
- Mathematical, Computational and Experimental Neuroscience, Basque Center for Applied Mathematics, Alameda de Mazzaredo 14, 48009, Bilbao, Spain
- Ikerbasque, Basque Foundation for Science, Plaza Euskadi 5, 48009, Bilbao, Spain
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Wang Y, Shi X, Si B, Cheng B, Chen J. Synchronization and oscillation behaviors of excitatory and inhibitory populations with spike-timing-dependent plasticity. Cogn Neurodyn 2023; 17:715-727. [PMID: 37265649 PMCID: PMC10229527 DOI: 10.1007/s11571-022-09840-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 06/06/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
The effect of synaptic plasticity on the synchronization mechanism of the cerebral cortex has been a hot research topic over the past two decades. There are a great deal of literatures on excitatory pyramidal neurons, but the mechanism of interaction between the inhibitory interneurons is still under exploration. In this study, we consider a complex network consisting of excitatory (E) pyramidal neurons and inhibitory (I) interneurons interacting with chemical synapses through spike-timing-dependent plasticity (STDP). To study the effects of eSTDP and iSTDP on synchronization and oscillation behaviors emerged in an excitatory-inhibitory balanced network, we analyzed three different cases, a small-world network of purely excitatory neurons with eSTDP, a small-world network of purely inhibitory neurons with iSTDP and a small-world network with excitatory-inhibitory balanced neurons. By varying the number of inhibitory interneurons, and that of connected edges in a small-world network, and the coupling strength, these networks exhibit different synchronization and oscillation behaviors. We found that the eSTDP facilitates synchronization effectively, while iSTDP has no significant impact on it. In addition, eSTDP and iSTDP restrict the balance of the excitatory-inhibitory balanced neuronal network together and play a fundamental role in maintaining network stability and synchronization. They also can be used to guide the treatment and further research of neurodegenerative diseases.
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Affiliation(s)
- Yuan Wang
- Brain and Autonomous Intelligent Robots Lab, School of Systems Science, Beijing Normal University, Beijing, People’s Republic of China
| | - Xia Shi
- School of Science, Beijing University of Posts and Telecommunications, Beijing, People’s Republic of China
| | - Bailu Si
- Brain and Autonomous Intelligent Robots Lab, School of Systems Science, Beijing Normal University, Beijing, People’s Republic of China
| | - Bo Cheng
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, People’s Republic of China
| | - Junliang Chen
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, People’s Republic of China
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Wang Y, Liu S, Wang H, Zhao Y, Zhang XD. Neuron devices: emerging prospects in neural interfaces and recognition. MICROSYSTEMS & NANOENGINEERING 2022; 8:128. [PMID: 36507057 PMCID: PMC9726942 DOI: 10.1038/s41378-022-00453-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/28/2022] [Accepted: 08/30/2022] [Indexed: 06/17/2023]
Abstract
Neuron interface devices can be used to explore the relationships between neuron firing and synaptic transmission, as well as to diagnose and treat neurological disorders, such as epilepsy and Alzheimer's disease. It is crucial to exploit neuron devices with high sensitivity, high biocompatibility, multifunctional integration and high-speed data processing. During the past decades, researchers have made significant progress in neural electrodes, artificial sensory neuron devices, and neuromorphic optic neuron devices. The main part of the review is divided into two sections, providing an overview of recently developed neuron interface devices for recording electrophysiological signals, as well as applications in neuromodulation, simulating the human sensory system, and achieving memory and recognition. We mainly discussed the development, characteristics, functional mechanisms, and applications of neuron devices and elucidated several key points for clinical translation. The present review highlights the advances in neuron devices on brain-computer interfaces and neuroscience research.
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Affiliation(s)
- Yang Wang
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, China
| | - Shuangjie Liu
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, China
| | - Hao Wang
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, China
| | - Yue Zhao
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, China
| | - Xiao-Dong Zhang
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072 Tianjin, China
- Tianjin Key Laboratory of Low Dimensional Materials Physics and Preparing Technology, Institute of Advanced Materials Physics, School of Sciences, Tianjin University, 300350 Tianjin, China
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Reis AS, Brugnago EL, Viana RL, Batista AM, Iarosz KC, Caldas IL. Effects of feedback control in small-world neuronal networks interconnected according to a human connectivity map. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Xu S, Deng Y, Luo J, He E, Liu Y, Zhang K, Yang Y, Xu S, Sha L, Song Y, Xu Q, Cai X. High-Throughput PEDOT:PSS/PtNPs-Modified Microelectrode Array for Simultaneous Recording and Stimulation of Hippocampal Neuronal Networks in Gradual Learning Process. ACS APPLIED MATERIALS & INTERFACES 2022; 14:15736-15746. [PMID: 35294190 DOI: 10.1021/acsami.1c23170] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
When it comes to mechanisms of brain functions such as learning and memory mediated by neural networks, existing multichannel electrophysiological detection and regulation technology at the cellular level does not suffice. To address this challenge, a 128-channel microelectrode array (MEA) was fabricated for electrical stimulation (ES) training and electrophysiological recording of the hippocampal neurons in vitro. The PEDOT:PSS/PtNPs-coated microelectrodes dramatically promote the recording and electrical stimulation performance. The MEA exhibited low impedance (10.94 ± 0.49 kohm), small phase delay (-12.54 ± 0.51°), high charge storage capacity (14.84 ± 2.72 mC/cm2), and high maximum safe injection charge density (4.37 ± 0.22 mC/cm2), meeting the specific requirements for training neural networks in vitro. A series of ESs at various frequencies was applied to the neuronal cultures in vitro, seeking the optimum training mode that enables the neuron to display the most obvious plasticity, and 1 Hz ES was determined. The network learning process, including three consecutive trainings, affected the original random spontaneous activity. Along with that, the firing pattern gradually changed to burst and the correlation and synchrony of the neuronal activity in the network have progressively improved, increasing by 314% and 240%, respectively. The neurons remembered these changes for at least 4 h. Collectively, ES activates the learning and memory functions of neurons, which is manifested in transformations in the discharge pattern and the improvement of network correlation and synchrony. This study offers a high-performance MEA revealing the underlying learning and memory functions of the brain and therefore serves as a useful tool for the development of brain functions in the future.
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Affiliation(s)
- Shihong Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yu Deng
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China
| | - Jinping Luo
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Enhui He
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaoyao Liu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kui Zhang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yan Yang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shengwei Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Longze Sha
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China
- Neuroscience Center, Chinese Academy of Medical Sciences, Beijing 100005, China
| | - Yilin Song
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Xu
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China
- Neuroscience Center, Chinese Academy of Medical Sciences, Beijing 100005, China
| | - Xinxia Cai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Reis AS, Brugnago EL, Caldas IL, Batista AM, Iarosz KC, Ferrari FAS, Viana RL. Suppression of chaotic bursting synchronization in clustered scale-free networks by an external feedback signal. CHAOS (WOODBURY, N.Y.) 2021; 31:083128. [PMID: 34470231 DOI: 10.1063/5.0056672] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/02/2021] [Indexed: 06/13/2023]
Abstract
Oscillatory activities in the brain, detected by electroencephalograms, have identified synchronization patterns. These synchronized activities in neurons are related to cognitive processes. Additionally, experimental research studies on neuronal rhythms have shown synchronous oscillations in brain disorders. Mathematical modeling of networks has been used to mimic these neuronal synchronizations. Actually, networks with scale-free properties were identified in some regions of the cortex. In this work, to investigate these brain synchronizations, we focus on neuronal synchronization in a network with coupled scale-free networks. The networks are connected according to a topological organization in the structural cortical regions of the human brain. The neuronal dynamic is given by the Rulkov model, which is a two-dimensional iterated map. The Rulkov neuron can generate quiescence, tonic spiking, and bursting. Depending on the parameters, we identify synchronous behavior among the neurons in the clustered networks. In this work, we aim to suppress the neuronal burst synchronization by the application of an external perturbation as a function of the mean-field of membrane potential. We found that the method we used to suppress synchronization presents better results when compared to the time-delayed feedback method when applied to the same model of the neuronal network.
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Affiliation(s)
- Adriane S Reis
- Physics Institute, University of São Paulo, 05508-090 São Paulo, SP, Brazil
| | - Eduardo L Brugnago
- Physics Department, Federal University of Paraná, 81531-980 Curitiba, PR, Brazil
| | - Iberê L Caldas
- Physics Institute, University of São Paulo, 81531-980 São Paulo, SP, Brazil
| | - Antonio M Batista
- Department of Mathematics and Statistics, State University of Ponta Grossa, 84030-900 Ponta Grossa, PR, Brazil
| | - Kelly C Iarosz
- Faculty of Telêmaco Borba, 84266-010 Telêmaco Borba, PR, Brazil
| | - Fabiano A S Ferrari
- Institute of Engineering, Science and Technology, Federal University of the Valleys of Jequitinhonha and Mucuri, 39803-371 Janaúba, MG, Brazil
| | - Ricardo L Viana
- Physics Department, Federal University of Paraná, 81531-980 Curitiba, PR, Brazil
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