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

Heterogeneous Responses to Changes in Inhibitory Synaptic Strength in Networks of Spiking Neurons

How does the dynamics of neurons in a network respond to changes in synaptic weights? Answer to this question would be important for a full understanding of synaptic plasticity. In this article, we report our numerical study of the effects of changes in inhibitory synaptic weights on the spontaneous activity of networks of spiking neurons with conductance-based synapses. Networks with biologically realistic features, which were reconstructed from multi-electrode array recordings taken in a cortical neuronal culture, and their modifications were used in the simulations. The magnitudes of the synaptic weights of all the inhibitory connections are decreased by a uniform amount subjecting to the condition that inhibitory connections would not be turned into excitatory ones. Our simulation results reveal that the responses of the neurons are heterogeneous: while the firing rate of some neurons increases as expected, the firing rate of other neurons decreases or remains unchanged. The same results show that heterogeneous responses also occur for an enhancement of inhibition. This heterogeneity in the responses of neurons to changes in inhibitory synaptic strength suggests that activity-induced modification of synaptic strength does not necessarily generate a positive feedback loop on the dynamics of neurons connected in a network. Our results could be used to understand the effects of bicuculline on spiking and bursting activities of neuronal cultures. Using reconstructed networks with biologically realistic features enables us to identify a long-tailed distribution of average synaptic weights for outgoing links as a crucial feature in giving rise to bursting in neuronal networks and in determining the overall response of the whole network to changes in synaptic strength. For networks whose average synaptic weights for outgoing links have a long-tailed distribution, bursting is observed and the average firing rate of the whole network increases upon inhibition suppression or decreases upon inhibition enhancement. For networks whose average synaptic weights for outgoing links are approximately normally distributed, bursting is not found and the average firing rate of the whole network remains approximately constant upon changes in inhibitory synaptic strength.

Active Role of Self-Sustained Neural Activity on Sensory Input Processing: A Minimal Theoretical Model

A growing body of work has demonstrated the importance of ongoing oscillatory neural activity in sensory processing and the generation of sensorimotor behaviors. It has been shown, for several different brain areas, that sensory-evoked neural oscillations are generated from the modulation by sensory inputs of inherent self-sustained neural activity (SSA). This letter contributes to that strand of research by introducing a methodology to investigate how much of the sensory-evoked oscillatory activity is generated by SSA and how much is generated by sensory inputs within the context of sensorimotor behavior in a computational model. We develop an abstract model consisting of a network of three Kuramoto oscillators controlling the behavior of a simulated agent performing a categorical perception task. The effects of sensory inputs and SSAs on sensory-evoked oscillations are quantified by the cross product of velocity vectors in the phase space of the network under different conditions (disconnected without input, connected without input, and connected with input). We found that while the agent is carrying out the task, sensory-evoked activity is predominantly generated by SSA (93.10%) with much less influence from sensory inputs (6.90%). Furthermore, the influence of sensory inputs can be reduced by 10.4% (from 6.90% to 6.18%) with a decay in the agent's performance of only 2%. A dynamical analysis shows how sensory-evoked oscillations are generated from a dynamic coupling between the level of sensitivity of the network and the intensity of the input signals. This work may suggest interesting directions for neurophysiological experiments investigating how self-sustained neural activity influences sensory input processing, and ultimately affects behavior.

Exponential distribution of lifetimes for transient bursting states in coupled noisy excitable systems

The phenomenon of transient bursting, caused by additive noise in a set of two coupled FitzHugh-Nagumo oscillators, is studied by direct numerical integration and by measurements in the analog electronic circuit. In the parameter region where the unique global attractor of the deterministic system is the state of rest, introduction of low or moderate intensity fluctuations into the voltage dynamics results in the onset of a transient bursting state: sequences of intermittent bursts (patches of spikes), followed by ultimate relaxation to the equilibrium. Like genuine deterministic bursting, this behavior has its origin in the slow-fast character of the underlying dynamics. Trajectories that in the deterministic variant would converge to the state of rest can, under the action of noise, escape the local basin of attraction of the equilibrium and experience a bursting episode, before being dynamically reinjected into the region around the equilibrium. Under frozen parameter values and fixed noise intensity, the number of bursts preceding the ultimate decay strongly varies for different realizations of the additive random signal. The average duration of the transient bursting stage, bounded for weak noise, diverges when the intensity of fluctuations is raised. For sufficiently large ensembles of realizations, the lifetimes of transient bursting states, both in simulations and in the analog circuit, obey the exponential distribution. We relate this distribution to the probability for a stochastic trajectory to temporarily escape from the local basin of attraction of the equilibrium.

State-Dependent Cortical Unit Activity Reflects Dynamic Brain State Transitions in Anesthesia

Understanding the effects of anesthesia on cortical neuronal spiking and information transfer could help illuminate the neuronal basis of the conscious state. Recent investigations suggest that the brain state identified by local field potential spectrum is not stationary but changes spontaneously at a fixed level of anesthetic concentration. How cortical unit activity changes with dynamically transitioning brain states under anesthesia is unclear. Extracellular unit activity was measured with 64-channel silicon microelectrode arrays in cortical layers 5/6 of the primary visual cortex of chronically instrumented, freely moving male rats (n = 7) during stepwise reduction of the anesthetic desflurane (6%, 4%, 2%, and 0%). Unsupervised machine learning applied to multiunit spike patterns revealed five distinct brain states. A novel desynchronized brain state with increased spike rate variability, sample entropy, and EMG activity occurred in 6% desflurane with 40.0% frequency. The other four brain states reflected graded levels of anesthesia. As anesthesia deepened the spike rate of neurons decreased regardless of their spike rate profile at baseline conscious state. Actively firing neurons with wide-spiking pattern showed increased bursting activity along with increased spike timing variability, unit-to-population correlation, and unit-to-unit transfer entropy, despite the overall decrease in transfer entropy. The narrow-spiking neurons showed similar changes but to a lesser degree. These results suggest that (1) anesthetic effect on spike rate is distinct from sleep, (2) synchronously fragmented spiking pattern is a signature of anesthetic-induced unconsciousness, and (3) the paradoxical, desynchronized brain state in deep anesthesia contends the generally presumed monotonic, dose-dependent anesthetic effect on the brain.SIGNIFICANCE STATEMENT Recent studies suggest that spontaneous changes in brain state occur under anesthesia. However, the spiking behavior of cortical neurons associated with such state changes has not been investigated. We found that local brain states defined by multiunit activity had a nonunitary relationship with the current anesthetic level. A paradoxical brain state displaying asynchronous firing pattern and high EMG activity was found unexpectedly in deep anesthesia. In contrast, the synchronous fragmentation of neuronal spiking appeared to be a robust signature of the state of anesthesia. The findings challenge the assumption of monotonic, anesthetic dose-dependent behavior of cortical neuron populations. They enhance the interpretation of neuroscientific data obtained under anesthesia and the understanding of the neuronal basis of anesthetic-induced state of unconsciousness.

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

The abnormal function of the voltage-gated potassium channel Kv10.2 can induce epilepsy. However, the physiological function of Kv10.2 in the central nervous system remains unclear. In this study, we found that Kv10.2 knockout (KO) increased the complexity of neurons in the CA3 subarea of hippocampus. Kv10.2 KO led to enlarged somata, elongated dendritic length, and increased the number of dendritic tips in cultured rat hippocampus neurons. Kv10.2 KO also increased Synapsin I and PSD95 protein density in cultured rat hippocampal neurons. Whole cell patch-clamp recordings of brain slices in the CA3 subarea of hippocampus revealed that Kv10.2 KO increased the amplitude of spontaneous excitatory postsynaptic currents (sEPSC) and miniature excitatory postsynaptic currents (mEPSC), depolarized the resting membrane potential and increased the action potential firing, reduced the rheobase and increased the input resistance, which results in enhanced neuronal excitability. Furthermore, we made electroencephalogram (EEG) recordings of brain activity in freely moving rats before and after inducing seizures by pentylenetetrazole (PTZ) injection. Kv10.2 KO rats dramatically increased the EEG amplitude during epilepsy. Behavioral observation after seizure induction revealed that Kv10.2 KO rats demonstrated shortened onset latency, prolonged duration, and increased seizure severity when compared with wild type rats. Therefore, this study provides a new link between Kv10.2 and neuronal morphology and higher intrinsic excitability.

The Pelvic Girdle Pain deadlock: 2. Topics that, so far, have remained out of focus

INTRODUCTION

In our preceding paper, we concluded that Pelvic Girdle Pain (PGP) should be taken seriously. Still, we do not know its causes. Literature reviews on treatment fail to reveal a consistent pattern, and there are patients who do not respond well to treatment. We designated the lack of progress in research and in the clinic as 'deadlock', and proposed a 'deconstruction' of PGP, that is to say, taking PGP apart into its relevant dimensions.

PURPOSE

We examine the proposition that PGP may emerge as local inflammation. Inflammation would be a new dimension to be taken into account, between biomechanics and psychology. To explore the consequences of this idea, we present four different topics that, so far, have remained out of focus. One: The importance of microtrauma. Two: Ways to counteract chronification. Three: The importance of sickness behaviour when systemic inflammation turns into neuroinflammation of the brain. And Four: The mainly emotional and cognitive nature of chronic pain, and how aberrant neuroinflammation may render chronic pain intractable. For intractable pain, sleep and stress management are promising treatment options.

IMPLICATIONS

The authors hope that the present paper helps to stimulate the flexible creativity that is required to deal with the biological and psychological impact of PGP. Measuring inflammatory mediators in PGP should be a research priority. It should be understood that the boundaries between biology and psychology are becoming blurred. Clinicians must frequently monitor pain, disability, and mood, and be ready to switch treatment whenever the patient does not improve.

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

In network models of spiking neurons, the joint impact of network structure and synaptic parameters on activity propagation is still an open problem. Here, we use an information-theoretical approach to investigate activity propagation in spiking networks with a hierarchical modular topology. We observe that optimized pairwise information propagation emerges due to the increase of either (i) the global synaptic strength parameter or (ii) the number of modules in the network, while the network size remains constant. At the population level, information propagation of activity among adjacent modules is enhanced as the number of modules increases until a maximum value is reached and then decreases, showing that there is an optimal interplay between synaptic strength and modularity for population information flow. This is in contrast to information propagation evaluated among pairs of neurons, which attains maximum value at the maximum values of these two parameter ranges. By examining the network behavior under the increase of synaptic strength and the number of modules, we find that these increases are associated with two different effects: (i) the increase of autocorrelations among individual neurons and (ii) the increase of cross-correlations among pairs of neurons. The second effect is associated with better information propagation in the network. Our results suggest roles that link topological features and synaptic strength levels to the transmission of information in cortical networks.

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

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.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks.

New Insights on Temporal Lobe Epilepsy Based on Plasticity-Related Network Changes and High-Order Statistics

Epilepsy is a disorder of the brain characterized by the predisposition to generate recurrent unprovoked seizures, which involves reshaping of neuronal circuitries based on intense neuronal activity. In this review, we first detailed the regulation of plasticity-associated genes, such as ARC, GAP-43, PSD-95, synapsin, and synaptophysin. Indeed, reshaping of neuronal connectivity after the primary, acute epileptogenesis event increases the excitability of the temporal lobe. Herein, we also discussed the heterogeneity of neuronal populations regarding the number of synaptic connections, which in the theoretical field is commonly referred as degree. Employing integrate-and-fire neuronal model, we determined that in addition to increased synaptic strength, degree correlations might play essential and unsuspected roles in the control of network activity. Indeed, assortativity, which can be described as a condition where high-degree correlations are observed, increases the excitability of neural networks. In this review, we summarized recent topics in the field, and data were discussed according to newly developed or unusual tools, as provided by mathematical graph analysis and high-order statistics. With this, we were able to present new foundations for the pathological activity observed in temporal lobe epilepsy.