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Jia X, Shao W, Hu N, Shi J, Fan X, Chen C, Wang Y, Chen L, Qiao H, Li X. Learning populations with hubs govern the initiation and propagation of spontaneous bursts in neuronal networks after learning. Front Neurosci 2022; 16:854199. [PMID: 36061604 PMCID: PMC9433803 DOI: 10.3389/fnins.2022.854199] [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: 01/13/2022] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Abstract
Spontaneous bursts in neuronal networks with propagation involving a large number of synchronously firing neurons are considered to be a crucial feature of these networks both in vivo and in vitro. Recently, learning has been shown to improve the association and synchronization of spontaneous events in neuronal networks by promoting the firing of spontaneous bursts. However, little is known about the relationship between the learning phase and spontaneous bursts. By combining high-resolution measurement with a 4,096-channel complementary metal-oxide-semiconductor (CMOS) microelectrode array (MEA) and graph theory, we studied how the learning phase influenced the initiation of spontaneous bursts in cultured networks of rat cortical neurons in vitro. We found that a small number of selected populations carried most of the stimulus information and contributed to learning. Moreover, several new burst propagation patterns appeared in spontaneous firing after learning. Importantly, these "learning populations" had more hubs in the functional network that governed the initiation of spontaneous burst activity. These results suggest that changes in the functional structure of learning populations may be the key mechanism underlying increased bursts after learning. Our findings could increase understanding of the important role that synaptic plasticity plays in the regulation of spontaneous activity.
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Affiliation(s)
- Xiaoli Jia
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Wenwei Shao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Nan Hu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Jianxin Shi
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Xiu Fan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Chong Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Youwei Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Liqun Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Huanhuan Qiao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
| | - Xiaohong Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin, China
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2
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Puppo F, Pré D, Bang AG, Silva GA. Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks. Front Neurosci 2021; 15:647877. [PMID: 34335152 PMCID: PMC8323822 DOI: 10.3389/fnins.2021.647877] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 05/31/2021] [Indexed: 12/22/2022] Open
Abstract
Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective-direct and causal-connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect, and apparent links. Our method can be generally applied to the functional characterization of any in vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in vitro hiPSC-derived neuronal cultures.
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Affiliation(s)
- Francesca Puppo
- BioCircuits Institute and Center for Engineered Natural Intelligence, University of California, San Diego, La Jolla, CA, United States
| | - Deborah Pré
- Conrad Prebys Center for Chemical Genomics, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, United States
| | - Anne G. Bang
- Conrad Prebys Center for Chemical Genomics, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, United States
| | - Gabriel A. Silva
- BioCircuits Institute, Center for Engineered Natural Intelligence, Department of Bioengineering, Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
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3
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Masumori A, Sinapayen L, Maruyama N, Mita T, Bakkum D, Frey U, Takahashi H, Ikegami T. Neural Autopoiesis: Organizing Self-Boundaries by Stimulus Avoidance in Biological and Artificial Neural Networks. ARTIFICIAL LIFE 2020; 26:130-151. [PMID: 32027532 DOI: 10.1162/artl_a_00314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organism are not static but dynamically regulated by the system itself. To study the autonomous regulation of a self-boundary, we focus on neural homeodynamic responses to environmental changes using both biological and artificial neural networks. Previous studies showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) learn an action as they avoid stimulation from outside. In this article, as a result of our experiments using embodied cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: If the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable input. We also show such a behavior is reproduced by spiking neural networks with asymmetric STDP. We consider that these properties are to be regarded as autonomous regulation of self and nonself for the network, in which a controllable neuron is regarded as self, and an uncontrollable neuron is regarded as nonself. Finally, we introduce neural autopoiesis by proposing the principle of stimulus avoidance.
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Affiliation(s)
- Atsushi Masumori
- University of Tokyo, Department of General Systems Sciences, Graduate School of Arts and Sciences.
| | - Lana Sinapayen
- Sony Computer Science Laboratories
- Tokyo Institute of Technology, Earth-Life Science Institute.
| | - Norihiro Maruyama
- University of Tokyo, Department of General Systems Sciences, Graduate School of Arts and Sciences.
| | - Takeshi Mita
- University of Tokyo, Department of Mechano-Informatics, Graduate School of Information Science and Technology.
| | - Douglas Bakkum
- ETH Zurich, Department of Biosystems Science and Engineering.
| | | | - Hirokazu Takahashi
- University of Tokyo, Department of Mechano-Informatics, Graduate School of Information Science and Technology.
| | - Takashi Ikegami
- University of Tokyo, Department of General Systems Sciences, Graduate School of Arts and Sciences.
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Zhou Y, Qiu L, Wang H, Chen X. Induction of activity synchronization among primed hippocampal neurons out of random dynamics is key for trace memory formation and retrieval. FASEB J 2020; 34:3658-3676. [PMID: 31944374 PMCID: PMC7079015 DOI: 10.1096/fj.201902274r] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 12/02/2019] [Accepted: 12/15/2019] [Indexed: 01/07/2023]
Abstract
Memory is thought to be encoded by sparsely distributed neuronal ensembles in memory‐related regions. However, it is unclear how memory‐eligible neurons react during learning to encode trace fear memory and how they retrieve a memory. We implemented a fiber‐optic confocal fluorescence endomicroscope to directly visualize calcium dynamics of hippocampal CA1 neurons in freely behaving mice subjected to trace fear conditioning. Here we report that the overall activity levels of CA1 neurons showed a right‐skewed lognormal distribution, with a small portion of highly active neurons (termed Primed Neurons) filling the long‐tail. Repetitive training induced Primed Neurons to shift from random activity to well‐tuned synchronization. The emergence of activity synchronization coincided with the appearance of mouse freezing behaviors. In recall, a partial synchronization among the same subset of Primed Neurons was induced from random dynamics, which also coincided with mouse freezing behaviors. Additionally, training‐induced synchronization facilitated robust calcium entry into Primed Neurons. In contrast, most CA1 neurons did not respond to tone and foot shock throughout the training and recall cycles. In conclusion, Primed Neurons are preferably recruited to encode trace fear memory and induction of activity synchronization among Primed Neurons out of random dynamics is critical for trace memory formation and retrieval.
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Affiliation(s)
- Yuxin Zhou
- Department of Molecular, Cellular and Biomedical Sciences, University of New Hampshire, Durham, NH, USA
| | - Liyan Qiu
- Department of Molecular, Cellular and Biomedical Sciences, University of New Hampshire, Durham, NH, USA
| | - Haiying Wang
- Department of Statistics, University of Connecticut, Storrs, CT, USA
| | - Xuanmao Chen
- Department of Molecular, Cellular and Biomedical Sciences, University of New Hampshire, Durham, NH, USA
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5
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Emmenegger V, Obien MEJ, Franke F, Hierlemann A. Technologies to Study Action Potential Propagation With a Focus on HD-MEAs. Front Cell Neurosci 2019; 13:159. [PMID: 31118887 PMCID: PMC6504789 DOI: 10.3389/fncel.2019.00159] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 04/08/2019] [Indexed: 12/26/2022] Open
Abstract
Axons convey information in neuronal circuits via reliable conduction of action potentials (APs) from the axon initial segment (AIS) to the presynaptic terminals. Recent experimental findings increasingly evidence that the axonal function is not limited to the simple transmission of APs. Advances in subcellular-resolution recording techniques have shown that axons display activity-dependent modulation in spike shape and conduction velocity, which influence synaptic strength and latency. We briefly review here, how recent methodological developments facilitate the understanding of the axon physiology. We included the three most common methods, i.e., genetically encoded voltage imaging (GEVI), subcellular patch-clamp and high-density microelectrode arrays (HD-MEAs). We then describe the potential of using HD-MEAs in studying axonal physiology in more detail. Due to their robustness, amenability to high-throughput and high spatiotemporal resolution, HD-MEAs can provide a direct functional electrical readout of single cells and cellular ensembles at subcellular resolution. HD-MEAs can, therefore, be employed in investigating axonal pathologies, the effects of large-scale genomic interventions (e.g., with RNAi or CRISPR) or in compound screenings. A combination of extracellular microelectrode arrays (MEAs), intracellular microelectrodes and optical imaging may potentially reveal yet unexplored repertoires of axonal functions.
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Affiliation(s)
- Vishalini Emmenegger
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Marie Engelene J. Obien
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- MaxWell Biosystems AG, Basel, Switzerland
| | - Felix Franke
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
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6
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Gisquet-Verrier P, Riccio DC. Memory integration: An alternative to the consolidation/reconsolidation hypothesis. Prog Neurobiol 2018; 171:15-31. [PMID: 30343034 DOI: 10.1016/j.pneurobio.2018.10.002] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 09/11/2018] [Accepted: 10/13/2018] [Indexed: 10/28/2022]
Abstract
The original concept of consolidation considers that memory requires time to be fixed. Since 2000, a comparable protein-dependent re-stabilization phase, called reconsolidation, has been assumed to take place after memory retrieval. This consolidation/reconsolidation hypothesis, has dominated the literature for more than 50 years, despite compelling evidence that is inconsistent with it. In this review, we present an historical overview and explain how, despite serious criticisms, this hypothesis has persisted for decades and become accepted as a dogma. Based on both older and more recent evidence, we next propose the concept of memory integration which involves the linkage or embedding of new material into an already existing representation. We believe integration provides a viable explanation for retrograde amnesia in place of the consolidation/reconsolidation hypothesis. Integration can further be the basis for several major cases of memory alteration such as time dependent memory enhancement, interference, counter-conditioning, updating and other instances of memory malleability. In a final section we consider the implications this new concept may have for memory processes and its translational applications.
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Affiliation(s)
- Pascale Gisquet-Verrier
- Neuro-PSI, Université Paris-Sud, CNRS UMR9197, Université Paris-Saclay, Bât 446, Orsay, F-91405, France.
| | - David C Riccio
- Department of Psychological Sciences, Kent State University, Kent, OH, 44242, USA
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7
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Nieus T, D'Andrea V, Amin H, Di Marco S, Safaai H, Maccione A, Berdondini L, Panzeri S. State-dependent representation of stimulus-evoked activity in high-density recordings of neural cultures. Sci Rep 2018; 8:5578. [PMID: 29615719 PMCID: PMC5882875 DOI: 10.1038/s41598-018-23853-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 03/21/2018] [Indexed: 01/01/2023] Open
Abstract
Neuronal responses to external stimuli vary from trial to trial partly because they depend on continuous spontaneous variations of the state of neural circuits, reflected in variations of ongoing activity prior to stimulus presentation. Understanding how post-stimulus responses relate to the pre-stimulus spontaneous activity is thus important to understand how state dependence affects information processing and neural coding, and how state variations can be discounted to better decode single-trial neural responses. Here we exploited high-resolution CMOS electrode arrays to record simultaneously from thousands of electrodes in in-vitro cultures stimulated at specific sites. We used information-theoretic analyses to study how ongoing activity affects the information that neuronal responses carry about the location of the stimuli. We found that responses exhibited state dependence on the time between the last spontaneous burst and the stimulus presentation and that the dependence could be described with a linear model. Importantly, we found that a small number of selected neurons carry most of the stimulus information and contribute to the state-dependent information gain. This suggests that a major value of large-scale recording is that it individuates the small subset of neurons that carry most information and that benefit the most from knowledge of its state dependence.
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Affiliation(s)
- Thierry Nieus
- NetS3 Laboratory, Neuroscience and Brain Technologies Department, Istituto Italiano di Tecnologia, Genova, Italy. .,Department of Biomedical and Clinical Sciences "Luigi Sacco", Università di Milano, Milano, Italy.
| | - Valeria D'Andrea
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Hayder Amin
- NetS3 Laboratory, Neuroscience and Brain Technologies Department, Istituto Italiano di Tecnologia, Genova, Italy
| | - Stefano Di Marco
- NetS3 Laboratory, Neuroscience and Brain Technologies Department, Istituto Italiano di Tecnologia, Genova, Italy.,Scienze cliniche applicate e biotecnologiche, Università dell'Aquila, L'Aquila, Italy
| | - Houman Safaai
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy.,Department of Neurobiology, Harvard Medical School, 02115, Boston, Massachusetts, USA
| | - Alessandro Maccione
- NetS3 Laboratory, Neuroscience and Brain Technologies Department, Istituto Italiano di Tecnologia, Genova, Italy
| | - Luca Berdondini
- NetS3 Laboratory, Neuroscience and Brain Technologies Department, Istituto Italiano di Tecnologia, Genova, Italy
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy.
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8
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Clarkson BDS, Kahoud RJ, McCarthy CB, Howe CL. Inflammatory cytokine-induced changes in neural network activity measured by waveform analysis of high-content calcium imaging in murine cortical neurons. Sci Rep 2017; 7:9037. [PMID: 28831096 PMCID: PMC5567248 DOI: 10.1038/s41598-017-09182-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 07/20/2017] [Indexed: 01/07/2023] Open
Abstract
During acute neuroinflammation, increased levels of cytokines within the brain may contribute to synaptic reorganization that results in long-term changes in network hyperexcitability. Indeed, inflammatory cytokines are implicated in synaptic dysfunction in epilepsy and in an array of degenerative and autoimmune diseases of the central nervous system. Current tools for studying the impact of inflammatory factors on neural networks are either insufficiently fast and sensitive or require complicated and costly experimental rigs. Calcium imaging offers a reasonable surrogate for direct measurement of neuronal network activity, but traditional imaging paradigms are confounded by cellular heterogeneity and cannot readily distinguish between glial and neuronal calcium transients. While the establishment of pure neuron cultures is possible, the removal of glial cells ignores physiologically relevant cell-cell interactions that may be critical for circuit level disruptions induced by inflammatory factors. To overcome these issues, we provide techniques and algorithms for image processing and waveform feature extraction using automated analysis of spontaneous and evoked calcium transients in primary murine cortical neuron cultures transduced with an adeno-associated viral vector driving the GCaMP6f reporter behind a synapsin promoter. Using this system, we provide evidence of network perturbations induced by the inflammatory cytokines TNFα, IL1β, and IFNγ.
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Affiliation(s)
| | - Robert J Kahoud
- Department of Neurology, Mayo Clinic, Rochester, MN, USA 55905, USA
- Department of Pediatrics, Mayo Clinic, Rochester, MN, USA 55905, USA
| | | | - Charles L Howe
- Department of Neurology, Mayo Clinic, Rochester, MN, USA 55905, USA.
- Department of Neuroscience, Mayo Clinic, Rochester, MN, USA 55905, USA.
- Department of Immunology, Mayo Clinic, Rochester, MN, USA 55905, USA.
- Center for Multiple Sclerosis and Autoimmune Neurology, Mayo Clinic, Rochester, MN, USA 55905, USA.
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9
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Radulovic J, Jovasevic V, Meyer MA. Neurobiological mechanisms of state-dependent learning. Curr Opin Neurobiol 2017; 45:92-98. [PMID: 28558266 DOI: 10.1016/j.conb.2017.05.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Accepted: 05/14/2017] [Indexed: 01/06/2023]
Abstract
State-dependent learning (SDL) is a phenomenon relating to information storage and retrieval restricted to discrete states. While extensively studied using psychopharmacological approaches, SDL has not been subjected to rigorous neuroscientific study. Here we present an overview of approaches historically used to induce SDL, and highlight some of the known neurobiological mechanisms, in particular those related to inhibitory neurotransmission and its regulation by microRNAs (miR). We also propose novel cellular and circuit mechanisms as contributing factors. Lastly, we discuss the implications of advancing our knowledge on SDL, both for most fundamental processes of learning and memory as well as for development and maintenance of psychopathology.
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Affiliation(s)
- Jelena Radulovic
- Department of Psychiatry and Behavioral Sciences, The Asher Center for Study and Treatment of Depressive Disorders, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
| | - Vladimir Jovasevic
- Department of Psychiatry and Behavioral Sciences, The Asher Center for Study and Treatment of Depressive Disorders, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Mariah Aa Meyer
- Department of Psychiatry and Behavioral Sciences, The Asher Center for Study and Treatment of Depressive Disorders, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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10
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Paraskevov AV, Zendrikov DK. A spatially resolved network spike in model neuronal cultures reveals nucleation centers, circular traveling waves and drifting spiral waves. Phys Biol 2017; 14:026003. [PMID: 28333685 DOI: 10.1088/1478-3975/aa5fc3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
We show that in model neuronal cultures, where the probability of interneuronal connection formation decreases exponentially with increasing distance between the neurons, there exists a small number of spatial nucleation centers of a network spike, from where the synchronous spiking activity starts propagating in the network typically in the form of circular traveling waves. The number of nucleation centers and their spatial locations are unique and unchanged for a given realization of neuronal network but are different for different networks. In contrast, if the probability of interneuronal connection formation is independent of the distance between neurons, then the nucleation centers do not arise and the synchronization of spiking activity during a network spike occurs spatially uniform throughout the network. Therefore one can conclude that spatial proximity of connections between neurons is important for the formation of nucleation centers. It is also shown that fluctuations of the spatial density of neurons at their random homogeneous distribution typical for the experiments in vitro do not determine the locations of the nucleation centers. The simulation results are qualitatively consistent with the experimental observations.
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Affiliation(s)
- A V Paraskevov
- National Research Centre "Kurchatov Institute", 123182 Moscow, Russia. Moscow Institute of Physics and Technology (State University), 141700 Dolgoprudny, Russia
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11
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Obien MEJ, Gong W, Frey U, Bakkum DJ. CMOS-Based High-Density Microelectrode Arrays: Technology and Applications. SERIES IN BIOENGINEERING 2017. [DOI: 10.1007/978-981-10-3957-7_1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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12
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Yada Y, Mita T, Sanada A, Yano R, Kanzaki R, Bakkum DJ, Hierlemann A, Takahashi H. Development of neural population activity toward self-organized criticality. Neuroscience 2016; 343:55-65. [PMID: 27915209 DOI: 10.1016/j.neuroscience.2016.11.031] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Revised: 11/21/2016] [Accepted: 11/21/2016] [Indexed: 12/13/2022]
Abstract
Self-organized criticality (SoC), a spontaneous dynamic state established and maintained in networks of moderate complexity, is a universal characteristic of neural systems. Such systems produce cascades of spontaneous activity that are typically characterized by power-law distributions and rich, stable spatiotemporal patterns (i.e., neuronal avalanches). Since the dynamics of the critical state confer advantages in information processing within neuronal networks, it is of great interest to determine how criticality emerges during development. One possible mechanism is developmental, and includes axonal elongation during synaptogenesis and subsequent synaptic pruning in combination with the maturation of GABAergic inhibition (i.e., the integration then fragmentation process). Because experimental evidence for this mechanism remains inconclusive, we studied the developmental variation of neuronal avalanches in dissociated cortical neurons using high-density complementary metal-oxide semiconductor (CMOS) microelectrode arrays (MEAs). The spontaneous activities of nine cultures were monitored using CMOS MEAs from 4 to 30days in vitro (DIV) at single-cell spatial resolution. While cells were immature, cultures demonstrated random-like patterns of activity and an exponential avalanche size distribution; this distribution was followed by a bimodal distribution, and finally a power-law-like distribution. The bimodal distribution was associated with a large-scale avalanche with a homogeneous spatiotemporal pattern, while the subsequent power-law distribution was associated with diverse patterns. These results suggest that the SoC emerges through a two-step process: the integration process accompanying the characteristic large-scale avalanche and the fragmentation process associated with diverse middle-size avalanches.
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Affiliation(s)
- Yuichiro Yada
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan; Japan Society for the Promotion of Science (JSPS) Research Fellow, 5-3-1, Koji-machi, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Takeshi Mita
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Akihiro Sanada
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Ryuichi Yano
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Ryohei Kanzaki
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Douglas J Bakkum
- Department of Biosystems Science and Engineering, ETH, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Hirokazu Takahashi
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan.
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