Sustained oscillations, irregular firing, and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types

Abstract

The cerebral cortex exhibits neural activity even in the absence of external stimuli. This self-sustained activity is characterized by irregular firing of individual neurons and population oscillations with a broad frequency range. Questions that arise in this context, are: What are the mechanisms responsible for the existence of neuronal spiking activity in the cortex without external input? Do these mechanisms depend on the structural organization of the cortical connections? Do they depend on intrinsic characteristics of the cortical neurons? To approach the answers to these questions, we have used computer simulations of cortical network models. Our networks have hierarchical modular architecture and are composed of combinations of neuron models that reproduce the firing behavior of the five main cortical electrophysiological cell classes: regular spiking (RS), chattering (CH), intrinsically bursting (IB), low threshold spiking (LTS), and fast spiking (FS). The population of excitatory neurons is built of RS cells (always present) and either CH or IB cells. Inhibitory neurons belong to the same class, either LTS or FS. Long-lived self-sustained activity states in our network simulations display irregular single neuron firing and oscillatory activity similar to experimentally measured ones. The duration of self-sustained activity strongly depends on the initial conditions, suggesting a transient chaotic regime. Extensive analysis of the self-sustained activity states showed that their lifetime expectancy increases with the number of network modules and is favored when the network is composed of excitatory neurons of the RS and CH classes combined with inhibitory neurons of the LTS class. These results indicate that the existence and properties of the self-sustained cortical activity states depend on both the topology of the network and the neuronal mixture that comprises the network.

Optimal Interplay between Synaptic Strengths and Network Structure Enhances Activity Fluctuations and Information Propagation in Hierarchical Modular Networks

“…Our main finding is that the hierarchical and modular topology creates an effect of slow fluctuations, similar to the one created by an increase of synaptic efficacy, which in turn shapes information propagation and processing through those modules. Previous studies have shown that hierarchical and modular networks are advantageous in the sense of activity sustainment [29] and can present critical behaviors [28] that are connected to optimal transmissions [48], here we see that modularity may also create optimal transmission. In particular, this does not necessarily happens due to high magnitude of information transfer, but may happen at a transition point in the level of hierarchical organization which endows a robust communication independently of synaptic strength.…”

“…An interesting question in computational neuroscience has been the investigation of different dynamics achieved by networks composed of spiking neurons [13,44,28,45] and in particular the ones that enhance information processing such as networks embedded in slow fluctuations [27,12,14]. Structural characteristics and how they interact with the dynamics are also of great interest [46,47] and, in this regard, a hierarchical and modular topology faithfully represents generic characteristics of a cortical network [18,20,29]. In this work, we have constructed large-scale networks populated by spiking neurons with increasing levels of hierarchy which we extracted information theory grounded measures.…”

Reimplementation of the Potjans-Diesmann cortical microcircuit model: from NEST to Brian

“…In conclusion, the Potjans-Diesmann model was successfully replicated in a different 10 platform than the one in which it was originally implemented. 11 2 Introduction 12Most theoretical studies of cortical activity are based on networks of randomly connected units [2,6,7,12] or 13 with architectures artificially built from random networks [10]. In spite of the usefulness of these models, in 14 order to understand the interplay between network structure and cortical dynamics it is essential to have 15 computational models which accurately represent the cortical network architecture.…”

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

“…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.…”

“…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.…”