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Ristič D, Gosak M. Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons. Front Comput Neurosci 2022; 16:885720. [PMID: 35521427 PMCID: PMC9062746 DOI: 10.3389/fncom.2022.885720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.
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Affiliation(s)
- David Ristič
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
| | - Marko Gosak
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Faculty of Medicine, University of Maribor, Maribor, Slovenia
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2
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Yu N, Jagdev G, Morgovsky M. Noise-induced network bursts and coherence in a calcium-mediated neural network. Heliyon 2021; 7:e08612. [PMID: 35024481 PMCID: PMC8723995 DOI: 10.1016/j.heliyon.2021.e08612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 11/17/2021] [Accepted: 12/13/2021] [Indexed: 11/26/2022] Open
Abstract
Noise-induced population bursting has been widely identified to play important roles in information processes. We construct a mathematical model for a random and sparse heterogeneous neural network where bursting can be induced from a resting state by a global stochastic stimulus. Importantly, the noise-induced bursting dynamics of this network are mediated by calcium conductance. We use two spectral measures to evaluate network coherence in the context of the network bursts, the spike trains of all neurons, and the individual bursts of all neurons. Our results show that the coherence of the network is optimized by an optimal level of the stochastic stimulus, which is known as coherence resonance (CR). We also demonstrate that the interplay of the calcium conductance and noise intensity can modify the degree of CR.
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Kim JH, Ryu JR, Lee B, Chae U, Son JW, Park BH, Cho IJ, Sun W. Interpreting the Entire Connectivity of Individual Neurons in Micropatterned Neural Culture With an Integrated Connectome Analyzer of a Neuronal Network (iCANN). Front Neuroanat 2021; 15:746057. [PMID: 34744642 PMCID: PMC8564400 DOI: 10.3389/fnana.2021.746057] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 09/23/2021] [Indexed: 11/13/2022] Open
Abstract
The function of a neural circuit can be determined by the following: (1) characteristics of individual neurons composing the circuit, (2) their distinct connection structure, and (3) their neural circuit activity. However, prior research on correlations between these three factors revealed many limitations. In particular, profiling and modeling of the connectivity of complex neural circuits at the cellular level are highly challenging. To reduce the burden of the analysis, we suggest a new approach with simplification of the neural connection in an array of honeycomb patterns on 2D, using a microcontact printing technique. Through a series of guided neuronal growths in defined honeycomb patterns, a simplified neuronal circuit was achieved. Our approach allowed us to obtain the whole network connectivity at cellular resolution using a combination of stochastic multicolor labeling via viral transfection. Therefore, we were able to identify several types of hub neurons with distinct connectivity features. We also compared the structural differences between different circuits using three-node motif analysis. This new model system, iCANN, is the first experimental model of neural computation at the cellular level, providing neuronal circuit structures for the study of the relationship between anatomical structure and function of the neuronal network.
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Affiliation(s)
- June Hoan Kim
- Department of Anatomy, Korea University College of Medicine, Seoul, South Korea
| | - Jae Ryun Ryu
- Department of Anatomy, Korea University College of Medicine, Seoul, South Korea
| | - Boram Lee
- Department of Anatomy, Korea University College of Medicine, Seoul, South Korea
| | - Uikyu Chae
- Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, South Korea
| | - Jong Wan Son
- Division of Quantum Phases and Devices, Department of Physics, Konkuk University, Seoul, South Korea
| | - Bae Ho Park
- Division of Quantum Phases and Devices, Department of Physics, Konkuk University, Seoul, South Korea
| | - Il-Joo Cho
- Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, South Korea
| | - Woong Sun
- Department of Anatomy, Korea University College of Medicine, Seoul, South Korea
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Zendrikov D, Paraskevov A. Emergent population activity in metric-free and metric networks of neurons with stochastic spontaneous spikes and dynamic synapses. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.11.073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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5
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Cao B, Wang R, Gu H, Li Y. Coherence resonance for neuronal bursting with spike undershoot. Cogn Neurodyn 2020; 15:77-90. [PMID: 33786081 DOI: 10.1007/s11571-020-09595-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 04/25/2020] [Accepted: 04/29/2020] [Indexed: 11/28/2022] Open
Abstract
Although the bursting patterns with spike undershoot are involved with the achievement of physiological or cognitive functions of brain with synaptic noise, noise induced-coherence resonance (CR) from resting state or subthreshold oscillations instead of bursting has been widely identified to play positive roles in information process. Instead, in the present paper, CR characterized by the increase firstly and then decease of peak value of power spectrum of spike trains is evoked from a bursting pattern with spike undershoot, which means that the minimal membrane potential within burst is lower than that of the subthreshold oscillations between bursts, while CR cannot be evoked from the bursting pattern without spike undershoot. With bifurcations and fast-slow variable dissection method, the bursting patterns with and without spike undershoot are classified into "Sub-Hopf/Fold" bursting and "Fold/Homoclinic" bursting, respectively. For the bursting with spike undershoot, the trajectory of the subthreshold oscillations is very close to that of the spikes within burst. Therefore, noise can induce more spikes from the subthreshold oscillations and modulate the bursting regularity, which leads to the appearance of CR. For the bursting pattern without spike undershoot, the trajectory of the quiescent state is not close to that of the spikes within burst, and noise cannot induce spikes from the quiescent state between bursts, which is cause for non-CR. The result provides a novel case of CR phenomenon and extends the scopes of CR concept, presents that noise can enhance rather than suppress information of the bursting patterns with spike undershoot, which are helpful for understanding the dynamics and the potential physiological or cognitive functions of the nerve fiber or brain neurons with such bursting patterns.
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Affiliation(s)
- Ben Cao
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, 200092 China
| | - Runxia Wang
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, 200092 China
| | - Huaguang Gu
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, 200092 China
| | - Yuye Li
- College of Mathematics and Computer Science, Chifeng University, Chifeng, 024000 China
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Kim JH, Lee HJ, Choi W, Lee KJ. Encoding information into autonomously bursting neural network with pairs of time-delayed pulses. Sci Rep 2019; 9:1394. [PMID: 30718675 PMCID: PMC6362090 DOI: 10.1038/s41598-018-37915-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 12/16/2018] [Indexed: 12/16/2022] Open
Abstract
Biological neural networks with many plastic synaptic connections can store external input information in the map of synaptic weights as a form of unsupervised learning. However, the same neural network often produces dramatic reverberating events in which many neurons fire almost simultaneously – a phenomenon coined as ‘population burst.’ The autonomous bursting activity is a consequence of the delicate balance between recurrent excitation and self-inhibition; as such, any periodic sequences of burst-generating stimuli delivered even at a low frequency (~1 Hz) can easily suppress the entire network connectivity. Here we demonstrate that ‘Δt paired-pulse stimulation’, can be a novel way for encoding spatially-distributed high-frequency (~10 Hz) information into such a system without causing a complete suppression. The encoded memory can be probed simply by delivering multiple probing pulses and then estimating the precision of the arrival times of the subsequent evoked recurrent bursts.
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Affiliation(s)
- June Hoan Kim
- Department of Physics, Korea University, Seoul, 02841, Korea
| | - Ho Jun Lee
- Department of Physics, Korea University, Seoul, 02841, Korea
| | - Wonshik Choi
- Department of Physics, Korea University, Seoul, 02841, Korea.,Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science, Seoul, 02841, Korea
| | - Kyoung J Lee
- Department of Physics, Korea University, Seoul, 02841, Korea.
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Vardi R, Goldental A, Sardi S, Sheinin A, Kanter I. Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity. Sci Rep 2016; 6:36228. [PMID: 27824075 PMCID: PMC5099952 DOI: 10.1038/srep36228] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 10/11/2016] [Indexed: 12/22/2022] Open
Abstract
The increasing number of recording electrodes enhances the capability of capturing the network’s cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that merges simultaneous multi-patch-clamp and multi-electrode array recordings of neural networks in-vitro, we show that the membrane potential of a single neuron is a reliable and super-sensitive probe for monitoring such cooperative activities and their detailed rhythms. Specifically, the membrane potential and the spiking activity of a single neuron are either highly correlated or highly anti-correlated with the time-dependent macroscopic activity of the entire network. This surprising observation also sheds light on the cooperative origin of neuronal burst in cultured networks. Our findings present an alternative flexible approach to the technique based on a massive tiling of networks by large-scale arrays of electrodes to monitor their activity.
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Affiliation(s)
- Roni Vardi
- Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel.,Gonda Interdisciplinary Brain Research Center and the Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel
| | - Amir Goldental
- Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel
| | - Shira Sardi
- Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel
| | - Anton Sheinin
- Department of Biochemistry and Molecular Biology, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Ido Kanter
- Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel.,Gonda Interdisciplinary Brain Research Center and the Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel
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Vitality of Neural Networks under Reoccurring Catastrophic Failures. Sci Rep 2016; 6:31674. [PMID: 27530974 PMCID: PMC4987694 DOI: 10.1038/srep31674] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 07/25/2016] [Indexed: 11/09/2022] Open
Abstract
Catastrophic failures are complete and sudden collapses in the activity of large networks such as economics, electrical power grids and computer networks, which typically require a manual recovery process. Here we experimentally show that excitatory neural networks are governed by a non-Poissonian reoccurrence of catastrophic failures, where their repetition time follows a multimodal distribution characterized by a few tenths of a second and tens of seconds timescales. The mechanism underlying the termination and reappearance of network activity is quantitatively shown here to be associated with nodal time-dependent features, neuronal plasticity, where hyperactive nodes damage the response capability of their neighbors. It presents a complementary mechanism for the emergence of Poissonian catastrophic failures from damage conductivity. The effect that hyperactive nodes degenerate their neighbors represents a type of local competition which is a common feature in the dynamics of real-world complex networks, whereas their spontaneous recoveries represent a vitality which enhances reliable functionality.
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