scholarly journals Self-organization of in vitro neuronal assemblies drives to complex network topology

2021 ◽  
Author(s):  
Priscila Corrêa Antonello ◽  
Thomas F Varley ◽  
John Beggs ◽  
Marimélia Porcionatto ◽  
Olaf Sporns ◽  
...  
Keyword(s):  
Complex Network ◽  
Network Topology ◽  
Effective Connectivity ◽  
Small World ◽  
Self Organization ◽  
Neuronal Cultures ◽  
Highly Active ◽  
Neuronal Interactions ◽  
Effective Network

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of electrophysiological signals recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the neuronal network topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular community topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of communities. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.

scholarly journals Directed effective connectivity and synaptic weights of in vitro neuronal cultures revealed from high-density multielectrode array recordings

2020 ◽  
Author(s):  
Chumin Sun ◽  
K.C. Lin ◽  
Yu-Ting Huang ◽  
Emily S.C. Ching ◽  
Pik-Yin Lai ◽  
...  
Keyword(s):  
Neuronal Networks ◽  
Spatial Scales ◽  
Effective Connectivity ◽  
Small World ◽  
Synaptic Strength ◽  
Multielectrode Array ◽  
Neuronal Cultures ◽  
Cross Covariance ◽  
Spatially Extended

AbstractStudying connectivity of neuronal cultures can provide insights for understanding brain networks but it is challenging to reveal neuronal connectivity from measurements. We apply a novel method that uses a theoretical relation between the time-lagged cross-covariance and the equal-time cross-covariance to reveal directed effective connectivity and synaptic weights of cortical neuron cultures at different days in vitro from multielectrode array recordings. Using a stochastic leaky-integrate-and-fire model, we show that the simulated spiking activity of the reconstructed networks can well capture the measured network bursts. The neuronal networks are found to be highly nonrandom with an over-representation of bidirectionally connections as compared to a random network of the same connection probability, with the fraction of inhibitory nodes comparable to the measured fractions of inhibitory neurons in various cortical regions in monkey, and have small-world topology with basic network measures comparable to those of the nematode C. elegans chemical synaptic network. Our analyses further reveal that (i) the excitatory and inhibitory incoming degrees have bimodal distributions the excitatory and inhibitory incoming degrees have bimodal distributions, which are that distributions that have been indicated to be optimal against both random failures and attacks in undirected networks; (ii) the distribution of the physical length of excitatory incoming links has two peaks indicating that excitatory signal is transmitted at two spatial scales, one localized to nearest nodes and the other spatially extended to nodes millimeters away, and the shortest links are mostly excitatory towards excitatory nodes and have larger synaptic weights on average; (iii) the average incoming and outgoing synaptic strength is non-Gaussian with long tails and, in particular, the distribution of outgoing synaptic strength of excitatory nodes with excitatory incoming synaptic strength is lognormal, similar to the measured excitatory postsynaptic potential in rat cortex.Author summaryTo understand how the brain processes signal and carries out its function, it is useful to know the connectivity of the underlying neuronal circuits. For large-scale neuronal networks, it is difficult to measure connectivity directly using electron microscopy techniques and methods that can estimate connectivity from electrophysiological recordings are thus highly desirable. Existing methods focus mainly on estimating functional connectivity, which is defined by statistical dependencies between neuronal activities but the relevant direct casual interactions are captured by effective connectivity. Here we apply a novel covariance-relation based method to estimate the directed effective connectivity and synaptic weights of cortical neuron cultures from recordings of multielectrode array of over 4000 electrodes taken at different days in vitro. The neuronal networks are found to be nonrandom, small-world, excitation/inhibition balanced as measured in monkey cortex, and with feeder hubs. Our analyses further suggest some form of specialisation of nodes in receiving excitatory and inhibitory signals and the transmission of excitatory signals at two spatial scales, one localized to nearest nodes and the other spatially extended to nodes millimeters away, and reveal that the distributions of the average incoming and outgoing synaptic strength are skewed with long tails.

Modeling and Simulation for Complex Network Based Military System-of-Systems (SoS)

2011 ◽  
Vol 145 ◽  
pp. 224-228 ◽  
Author(s):  
Xiao Song ◽  
Bing Cheng Liu ◽  
Guang Hong Gong
Keyword(s):  
Modeling And Simulation ◽  
Complex Network ◽  
Network Topology ◽  
Network Theory ◽  
Small World ◽  
System Of Systems ◽  
Statistical Parameters ◽  
Complex Network Theory ◽  
Scale Free ◽  
Topology Model

Military SoS increasingly shows its relation of complex network. According to complex network theory, we construct a SoS network topology model for network warfare simulation. Analyzing statistical parameters of the model, it is concluded that the topology model has small-world, high-aggregation and scale-free properties. Based on this model we mainly simulate and analyze vulnerability of the network. And this provides basis for analysis of the robustness and vulnerability of real battle SoS network.

scholarly journals Connective Core Structures in Cognitive Networks: the Role of Hubs

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 961 ◽  
Author(s):  
Carlos Arruda Arruda Baltazar ◽  
Maria Isabel Barros Guinle ◽  
Cora Jirschik Caron ◽  
Edson Amaro ◽  
Birajara Soares Machado
Keyword(s):  
Complex Network ◽  
Network Topology ◽  
Information Integration ◽  
Effective Connectivity ◽  
Cognitive Networks ◽  
Invariant Structure ◽  
Network Effectiveness ◽  
Efficient Information ◽  
Behavioral States

Complex network analysis applied to the resting brain has shown that sets of highly interconnected networks with coherent activity may support a default mode of brain function within a global workspace. Perceptual processing of environmental stimuli induces architectural changes in network topology with higher specialized modules. Evidence shows that during cognitive tasks, network topology is reconfigured and information is broadcast from modular processors to a connective core, promoting efficient information integration. In this paper, we explored how the brain adapts its effective connectivity within the connective core and across behavioral states. We used complex network metrics to identify hubs and proposed a method of classification based on the effective connectivity patterns of information flow. Finally, we interpreted the role of the connective core and each type of hub on the network effectiveness. We also calculated the complexity of electroencephalography microstate sequences across different tasks. We observed that divergent hubs contribute significantly to the network effectiveness and that part of this contribution persists across behavioral states, forming an invariant structure. Moreover, we found that a large quantity of multiple types of hubs may be associated with transitions of functional networks.

scholarly journals Small-World and Scale-Free Network Models for IoT Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Insoo Sohn
Keyword(s):  
Graph Theory ◽  
Complex Network ◽  
Network Topology ◽  
Network Models ◽  
Small World ◽  
Small World Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Massive Number ◽  
Complex Network Models

It is expected that Internet of Things (IoT) revolution will enable new solutions and business for consumers and entrepreneurs by connecting billions of physical world devices with varying capabilities. However, for successful realization of IoT, challenges such as heterogeneous connectivity, ubiquitous coverage, reduced network and device complexity, enhanced power savings, and enhanced resource management have to be solved. All these challenges are heavily impacted by the IoT network topology supported by massive number of connected devices. Small-world networks and scale-free networks are important complex network models with massive number of nodes and have been actively used to study the network topology of brain networks, social networks, and wireless networks. These models, also, have been applied to IoT networks to enhance synchronization, error tolerance, and more. However, due to interdisciplinary nature of the network science, with heavy emphasis on graph theory, it is not easy to study the various tools provided by complex network models. Therefore, in this paper, we attempt to introduce basic concepts of graph theory, including small-world networks and scale-free networks, and provide system models that can be easily implemented to be used as a powerful tool in solving various research problems related to IoT.

scholarly journals Super-selective reconstruction of causal and direct connectivity with application to in-vitro iPSC neuronal networks

2020 ◽  
Author(s):  
Francesca Puppo ◽  
Deborah Pré ◽  
Anne Bang ◽  
Gabriel A. Silva
Keyword(s):  
Neuronal Network ◽  
Neural Development ◽  
Drug Testing ◽  
Neuronal Networks ◽  
Effective Connectivity ◽  
Functional Characterization ◽  
Neuronal Cultures ◽  
Causal Connection ◽  
Model Free

AbstractDespite 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 iPSC-derived neuronal cultures by reconstructing their effective connectivity, thus facilitating future efforts to generate predictive models for neurological disorders, drug testing and neuronal network modeling.

scholarly journals Formation of neural networks with structural and functional features consistent with small-world network topology on surface-grafted polymer particles

2019 ◽  
Vol 6 (10) ◽  
pp. 191086 ◽  
Author(s):  
Vibeke Devold Valderhaug ◽  
Wilhelm Robert Glomm ◽  
Eugenia Mariana Sandru ◽  
Masahiro Yasuda ◽  
Axel Sandvig ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Topology ◽  
Small World ◽  
Network Activity ◽  
Small World Network ◽  
Grafted Polymer ◽  
Functional Features ◽  
The Brain

In vitro electrophysiological investigation of neural activity at a network level holds tremendous potential for elucidating underlying features of brain function (and dysfunction). In standard neural network modelling systems, however, the fundamental three-dimensional (3D) character of the brain is a largely disregarded feature. This widely applied neuroscientific strategy affects several aspects of the structure–function relationships of the resulting networks, altering network connectivity and topology, ultimately reducing the translatability of the results obtained. As these model systems increase in popularity, it becomes imperative that they capture, as accurately as possible, fundamental features of neural networks in the brain, such as small-worldness. In this report, we combine in vitro neural cell culture with a biologically compatible scaffolding substrate, surface-grafted polymer particles (PPs), to develop neural networks with 3D topology. Furthermore, we investigate their electrophysiological network activity through the use of 3D multielectrode arrays. The resulting neural network activity shows emergent behaviour consistent with maturing neural networks capable of performing computations, i.e. activity patterns suggestive of both information segregation (desynchronized single spikes and local bursts) and information integration (network spikes). Importantly, we demonstrate that the resulting PP-structured neural networks show both structural and functional features consistent with small-world network topology.

scholarly journals Functional strengthening through synaptic scaling upon connectivity disruption in neuronal cultures

2020 ◽  
Vol 4 (4) ◽  
pp. 1160-1180
Author(s):  
Estefanía Estévez-Priego ◽  
Sara Teller ◽  
Clara Granell ◽  
Alex Arenas ◽  
Jordi Soriano
Keyword(s):  
Effective Connectivity ◽  
Neuronal Cultures ◽  
Second Phase ◽  
Synaptic Scaling ◽  
Network Information ◽  
Activity Dependent ◽  
Processing Network ◽  
Dependent Mechanism

An elusive phenomenon in network neuroscience is the extent of neuronal activity remodeling upon damage. Here, we investigate the action of gradual synaptic blockade on the effective connectivity in cortical networks in vitro. We use two neuronal cultures configurations—one formed by about 130 neuronal aggregates and another one formed by about 600 individual neurons—and monitor their spontaneous activity upon progressive weakening of excitatory connectivity. We report that the effective connectivity in all cultures exhibits a first phase of transient strengthening followed by a second phase of steady deterioration. We quantify these phases by measuring GEFF, the global efficiency in processing network information. We term hyperefficiency the sudden strengthening of GEFF upon network deterioration, which increases by 20–50% depending on culture type. Relying on numerical simulations we reveal the role of synaptic scaling, an activity–dependent mechanism for synaptic plasticity, in counteracting the perturbative action, neatly reproducing the observed hyperefficiency. Our results demonstrate the importance of synaptic scaling as resilience mechanism.

scholarly journals Antioxidant properties and inhibition of angiotensin-converting enzyme by highly active peptides from wheat gluten

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wen-Ying Liu ◽  
Jiang-Tao Zhang ◽  
Takuya Miyakawa ◽  
Guo-Ming Li ◽  
Rui-Zeng Gu ◽  
...  
Keyword(s):  
Wheat Gluten ◽  
Antioxidant Properties ◽  
Converting Enzyme ◽  
Antioxidant Peptides ◽  
Active Peptides ◽  
Inhibitory Peptides ◽  
Highly Active ◽  
Ace Inhibitory Peptides ◽  
Ace Inhibitory

AbstractThis study aimed to focus on the high-value utilization of raw wheat gluten by determining the potent antioxidant peptides and angiotensin I-converting enzyme (ACE) inhibitory peptides from wheat gluten oligopeptides (WOP). WOP were analyzed for in vitro antioxidant activity and inhibition of ACE, and the identification of active peptides was performed by reversed-phase high-performance liquid chromatography and mass spectrometry. Quantitative analysis was performed for highly active peptides. Five potent antioxidant peptides, Leu-Tyr, Pro-Tyr, Tyr-Gln, Ala-Pro-Ser-Tyr and Arg-Gly-Gly-Tyr (6.07 ± 0.38, 7.28 ± 0.29, 11.18 ± 1.02, 5.93 ± 0.20 and 9.04 ± 0.47 mmol 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox) equivalent/g sample, respectively), and five potent ACE inhibitory peptides, Leu-Tyr, Leu-Val-Ser, Tyr-Gln, Ala-Pro-Ser-Tyr and Arg-Gly-Gly-Tyr (half maximal inhibitory concentration (IC50) values = 0.31 ± 0.02, 0.60 ± 0.03, 2.00 ± 0.13, 1.47 ± 0.08 and 1.48 ± 0.11 mmol/L, respectively), were observed. The contents of Leu-Tyr, Pro-Tyr, Tyr-Gln, Ala-Pro-Ser-Tyr, Arg-Gly-Gly-Tyr, and Leu-Val-Ser were 155.04 ± 8.36, 2.08 ± 0.12, 1.95 ± 0.06, 22.70 ± 1.35, 0.25 ± 0.01, and 53.01 ± 2.73 μg/g, respectively, in the WOP. Pro-Tyr, Tyr-Gln, Ala-Pro-Ser-Tyr, Arg-Gly-Gly-Tyr, and Leu-Val-Ser are novel antioxidative/ACE inhibitory peptides that have not been previously reported. The results suggest that WOP could potentially be applied in the food industry as a functional additive.

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