Mechanisms of Self-Sustained Oscillatory States in Hierarchical Modular Networks with Mixtures of Electrophysiological Cell Types

Tomov, Petar; Pena, Rodrigo Felipe de Oliveira; Roque, Antônio C.; Zaks, Michael A.

doi:10.3389/fncom.2016.00023

Cited by 18 publications

(55 citation statements)

References 52 publications

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“…We have shown elsewhere the importance of this disinhibitory effect to sustain the long-lived activity of the network in the oscillatory situation (Tomov et al, 2016).…”

Section: Preliminaries and The Deterministic Setupmentioning

confidence: 85%

“…In the distribution shown in Fig 5.2 G, the maximum is broad due to the time-scale separation of the variables: u is slower than v and its relaxation takes much longer. In (Tomov et al, 2016) we have shown the importance of the recovery variable for the breakdown of global high-activity epochs, which produces the up and down oscillatory pattern. The term "up" refers here to different states in which the network activity is above Figure 5.2: Up and down network oscillations in the noiseless case when g in > g ex .…”

Section: Preliminaries and The Deterministic Setupmentioning

confidence: 88%

“…Methods of mean-field analysis, dynamical systems analysis, and information theory were developed and applied. have been published elsewhere (Tomov, Pena, Zaks, & Roque, 2014;Tomov, Pena, Roque, & Zaks, 2016). Using interpretations based on single-neuron and network dynamical systems analyses, we explain why this activity starts and the reason for its eventual complete cessation.…”

Section: Goalsmentioning

confidence: 96%

“…where summation is performed over all time instants t i of preceding presynaptic spikes. We adopt the same parameters as in (Tomov et al, 2016): E ex = 0 mV, E in = −80 mV, τ ex = 5 ms and τ in = 6 ms.…”

Section: Neuron and Network Modelmentioning

confidence: 99%

“…Networks in which the nodes feature more complicated dynamics than LIF neurons and are able to reproduce intrinsic firing patterns of contrasting cortical neurons, e.g. based on the Izhikevich (Izhikevich, 2003(Izhikevich, , 2007 or the AdEx (Brette & Gerstner, 2005;Gerstner et al, 2014) (Tomov et al, 2014(Tomov et al, , 2016. This suggests that not only synaptic balance of excitation/inhibition but also heterogeneities in the neuronal composition of the network may have an impact on the dynamic pattern of the network.…”

Section: Introductionmentioning

confidence: 99%

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Emergência de flutuações de atividade em modelos de redes corticais com populações neurais heterogêneas

Pena¹

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Most theoretical studies of cortical network dynamics have concentrated on networks with non-modular architecture and homogeneous neuronal composition, usually described by unstructured networks with Erdős-Rényi topology (Erdős & Rényi, 1959) populated by leaky integrate-and-fire neurons (Gerstner, Kistler, Naud, & Paninski, 2014). The main contribution of this thesis was to extend these studies to networks with a more elaborate architecture, namely hierarchical modular, which captures elements of the organization of the cortical brain network (Meunier, Lambiotte, & Bullmore, 2010), composed of heterogeneous neuronal populations with distinct firing patterns, modeled by two-dimensional integrate-and-fire neurons (Gerstner et al., 2014). The network models studied here exhibit a rich repertoire of dynamic activity patterns, and this thesis describes, analyses and explains the underlying mechanisms of some of these patterns for the first time. 1.4 Organization of the thesis The structure of this thesis was inspired on the "traditional complex" arrangement (Paltridge, 2002), where general introduction and methods are followed by studies presented in different chapters with their own introduction, methods, results, and discussion sections. In the end, general conclusions are presented. A summary of the internal structure of each chapter follows: • In Chapter 2, we review the background and literature. We go through basic neuroscience modeling justifying the use of simplified models as the ones we will employ here. We introduce the reader to basic notions of phase-plane analysis which will be useful for the questions addressed in this thesis. • In Chapter 3 we present our general methods. There, we explain all the statistical measures used along the thesis. Although the common measures are placed here, in each chapter we also include a more specific methods section. • In Chapter 4, the first chapter with results, we investigate mechanisms behind the transient oscillatory activity observed in the models. Many of these results 1.5-Scientific publications derived from this thesis 1.5-Scientific publications derived from this thesis * j

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“…We have shown elsewhere the importance of this disinhibitory effect to sustain the long-lived activity of the network in the oscillatory situation (Tomov et al, 2016).…”

Section: Preliminaries and The Deterministic Setupmentioning

confidence: 85%

Section: Preliminaries and The Deterministic Setupmentioning

confidence: 88%

Section: Goalsmentioning

confidence: 96%

Section: Neuron and Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Emergência de flutuações de atividade em modelos de redes corticais com populações neurais heterogêneas

Pena¹

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Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise

2018

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Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. In the region of parameter space where inhibitory synaptic strength exceeds the excitatory synaptic strength and for moderate noise magnitudes networks feature intermittent switches between oscillatory and quiescent states with characteristics similar to those of synchronous and asynchronous cortical states, respectively. We explain these oscillatory and quiescent patterns by combining a phenomenological global description of the network state with local descriptions of individual neurons in their partial phase spaces. Our results point to a bridge from events at the molecular scale of synapses to the cellular scale of individual neurons to the collective scale of neuronal populations.Electronic supplementary materialThe online version of this article (10.1007/s10827-018-0688-6) contains supplementary material, which is available to authorized users.

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Deletion of Kv10.2 Causes Abnormal Dendritic Arborization and Epilepsy Susceptibility

et al. 2020

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Mechanisms of Self-Sustained Oscillatory States in Hierarchical Modular Networks with Mixtures of Electrophysiological Cell Types

Cited by 18 publications

References 52 publications

Emergência de flutuações de atividade em modelos de redes corticais com populações neurais heterogêneas

Emergência de flutuações de atividade em modelos de redes corticais com populações neurais heterogêneas

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise

Deletion of Kv10.2 Causes Abnormal Dendritic Arborization and Epilepsy Susceptibility

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