Dominance of Metric Correlations in Two-Dimensional Neuronal Cultures Described through a Random Field Ising Model

Hernández-Navarro, Lluís; Orlandi, Javier G.; Cerruti, Benedetta; Vives, Eduard; Soriano, Jordi

doi:10.1103/physrevlett.118.208101

Cited by 27 publications

(31 citation statements)

References 29 publications

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“…S3). The similarity in the dynamics was due to the small size of the cultures in comparison to the axonal length and that favored a broad network interconnectivity, both between neurons and aggregates [19]. Only in the case of extreme aggregation coherent collective dynamics was replaced by fragmented, local activity [26].…”

Section: Simulation Of Spatial Networkmentioning

confidence: 99%

“…The increase of structural connectivity upon aggregation highlights that metric correlations may prove essential to understand the behavior of neuronal networks. Hernández-Navarro et al [19] recently showed that the role of spatial embedding in the structure and dynamics of networks can be quantified in terms of the degree of aggregation λ and the relative axonal length δ = a/L, where a is the average axonal length and L the diameter of the culture.…”

Section: Aggregation and Connectivitymentioning

confidence: 99%

“…Their accessibility and ease manipulation allow for a variety of preparations, from relatively simple homogeneous neuronal assemblies to intricate bioengineered designs [17]. Neuronal cultures are also ideally suited to study the role of spatial embedding and metric correlations in the formation, structure and dynamics of neuronal circuits [18], [19], [20]. Indeed, the analysis of spatial networks [21] has provided valuable insight in fields as diverse as transportation or epidemics [21], [22], [23], but it has been relatively poorly explored in the context of neuronal circuits.…”

Section: Introductionmentioning

confidence: 99%

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Neuronal Spatial Arrangement Shapes Effective Connectivity Traits of in vitro Cortical Networks

Tibau

Ludl

Rüdiger

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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We studied effective connectivity in rat cortical cultures with various degrees of spatial aggregation, ranging from homogeneous networks to highly aggregated ones. We considered small cultures 3 mm in diameter and that contained about 2000 neurons. Spatial inhomogeneity favored an increase of metric correlations and connectivity among neighboring neurons. Effective connectivity was determined from spontaneous activity recordings using calcium fluorescence imaging. We used generalized transfer entropy as tool to infer the effective connectivity. We carried out numerical simulations to build networks that mimicked the experimental ones and to test the reliability of the connectivity-inference algorithm. Effective connectivity traits were investigated during the development of the cultures over two weeks, and along the gradual blockade of excitatory connections through CNQX. We observed that the average effective connectivity rapidly increased during culture development. At DIV 15 the average excitatory in-degree was measured ask in E 50 for homogeneous and semi aggregated networks, andk in E 120 for aggregated ones, and with 20% inhibition. Aggregated cultures exhibited assortative traits and a high resilience to chemical damage, while the other cultures were dissassortative or neutral, and less resilient. Our work illustrates the role of metric correlations in spatially embedded networks in shaping connectivity and activity traits in living neuronal networks.

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Section: Simulation Of Spatial Networkmentioning

confidence: 99%

Section: Aggregation and Connectivitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neuronal Spatial Arrangement Shapes Effective Connectivity Traits of in vitro Cortical Networks

Tibau

Ludl

Rüdiger

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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“…[14][15][16] Recent studies showed that spatial embedding in these systems naturally feature metric correlations that shape in great measure the architectural and dynamic traits of the neuronal network. 14,[16][17][18] To the best of our knowledge, there exist no computational studies for damage that take into account the spatial embedding of biologically-realistic neuronal microcircuits. Thus, in the present study, we aim at lling this gap by introducing a detailed numerical exploration of the di erences between targeted attack and failure in spatially-embedded networks.…”

Section: Articlementioning

confidence: 99%

Impact of targeted attack on the spontaneous activity in spatial and biologically-inspired neuronal networks

Faci-Lázaro

Soriano

Gómez‐Gardeñes

2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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We study the structural and dynamical consequences of damage in spatial neuronal networks. Inspired by real in vitro networks, we construct directed networks embedded in a two-dimensional space and follow biological rules for designing the wiring of the system. As a result, synthetic cultures display strong metric correlations similar to those observed in real experiments. In its turn, neuronal dynamics is incorporated through the Izhikevich model adopting the parameters derived from observation in real cultures. We consider two scenarios for damage, targeted attacks on those neurons with the highest out-degree and random failures. By analyzing the evolution of both the giant connected component and the dynamical patterns of the neurons as nodes are removed, we observe that network activity halts for a removal of 50% of the nodes in targeted attacks, much lower than the 70% node removal required in the case of random failures. Notably, the decrease of neuronal activity is not gradual. Both damage scenarios portray "boosts" of activity just before full silencing that are not present in equivalent random (Erdös-Rényi) graphs. These boosts correspond to small, spatially compact subnetworks that are able to maintain high levels of activity. Since these subnetworks are absent in the equivalent random graphs, we hypothesize that metric correlations facilitate the existence of local circuits su ciently integrated to maintain activity, shaping an intrinsic mechanism for resilience.

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“…Note that connections cross the window also from neurons outside of the field of view, such that single connections cannot be distinguished visually in the sketch. (b) 1.4 × 1.4mm 2 subset of spatially-clustered (SC) topology generated by axonal-growth rules[5,88].…”

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confidence: 99%

Homeostatic Plasticity and External Input Shape Neural Network Dynamics

2018

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In vitro and in vivo spiking activity clearly differ. Whereas networks in vitro develop strong bursts separated by periods of very little spiking activity, in vivo cortical networks show continuous activity. This is puzzling considering that both networks presumably share similar single-neuron dynamics and plasticity rules. We propose that the defining difference between in vitro and in vivo dynamics is the strength of external input. In vitro, networks are virtually isolated, whereas in vivo every brain area receives continuous input. We analyze a model of spiking neurons in which the input strength, mediated by spike rate homeostasis, determines the characteristics of the dynamical state. In more detail, our analytical and numerical results on various network topologies show consistently that under increasing input, homeostatic plasticity generates distinct dynamic states, from bursting, to close-to-critical, reverberating and irregular states. This implies that the dynamic state of a neural network is not fixed but can readily adapt to the input strengths. Indeed, our results match experimental spike recordings in vitro and in vivo: the in vitro bursting behavior is consistent with a state generated by very low network input (< 0.1%), whereas in vivo activity suggests that on the order of 1% recorded spikes are input-driven, resulting in reverberating dynamics. Importantly, this predicts that one can abolish the ubiquitous bursts of in vitro preparations, and instead impose dynamics comparable to in vivo activity by exposing the system to weak long-term stimulation, thereby opening new paths to establish an in vivo-like assay in vitro for basic as well as neurological studies.

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Dominance of Metric Correlations in Two-Dimensional Neuronal Cultures Described through a Random Field Ising Model

Cited by 27 publications

References 29 publications

Neuronal Spatial Arrangement Shapes Effective Connectivity Traits of in vitro Cortical Networks

Neuronal Spatial Arrangement Shapes Effective Connectivity Traits of in vitro Cortical Networks

Impact of targeted attack on the spontaneous activity in spatial and biologically-inspired neuronal networks

Homeostatic Plasticity and External Input Shape Neural Network Dynamics

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