Spatiotemporal dynamics in large-scale cortical networks

Investigating links between nervous system function and behavior requires monitoring neuronal activity at a range of spatial and temporal scales. Here, we summarize recent progress in applying two distinct but complementary approaches to the study of network dynamics in the neocortex. Mesoscopic calcium imaging allows simultaneous monitoring of activity across most of the cortex at moderate spatiotemporal resolution. Electrophysiological recordings provide extremely high temporal resolution of neural signals at multiple targeted locations. A number of recent studies have used these tools to reveal novel patterns of activity across distributed cortical subnetworks. This growing body of work strongly supports the hypothesis that the dynamic coordination of spatially distinct regions is a fundamental aspect of cortical function that supports cognition and behavior.     

Population networks: a large-scale framework for modelling cortical neural networks

Artificial neural networks are usually built on rather few elements such as activation functions, learning rules, and the network topology. When modelling the more complex properties of realistic networks, however, a number of higher-level structural principles become important. In this paper we present a theoretical framework for modelling cortical networks at a high level of abstraction. Based on the notion of a population of neurons, this framework can accommodate the common features of cortical architecture, such as lamination, multiple areas and topographic maps, input segregation, and local variations of the frequency of different cell types (e.g., cytochrome oxidase blobs). The framework is meant primarily for the simulation of activation dynamics; it can also be used to model the neural environment of single cells in a multiscale approach. Received: 9 January 1996 / Accepted in revised form: 24 July 1996     

Oscillatory neural networks in the rabbit hippocampus

A model is described to account for damped oscillatory activity of two interacting neural populations, pyramidal cells and interneurons. This network in the hippocampus is treated as a lumped system with tine delays between elements. The physiological mechanism underlying the oscillatory activity appears to involve neural population interaction and cannot be described in terms of a network composed of but two neurons, a single pyramidal cell and a single interneuron. An unusual aspect of the model is the explicit incorporation of an ongoing background input to raise the mean level of activity of the pyramidal cell population. This model has evolved from a series of studies previously performed on cats. To test the model experiments were performed on rabbits. The data showing oscillatory activity following fornix stimulation in the rabbit indicate that the model can be applied not only to the cat but also to the rabbit. In additions, for commissural stimulation oscillatory potentials of neural populations and individual pyramidal cells were evoked as predicted by the model.     

Oscillatory cortical network involved in auditory verbal hallucinations in schizophrenia

Background

Auditory verbal hallucinations (AVH), a prominent symptom of schizophrenia, are often highly distressing for patients. Better understanding of the pathogenesis of hallucinations could increase therapeutic options. Magnetoencephalography (MEG) provides direct measures of neuronal activity and has an excellent temporal resolution, offering a unique opportunity to study AVH pathophysiology.

Methods

Twelve patients (10 paranoid schizophrenia, 2 psychosis not otherwise specified) indicated the presence of AVH by button-press while lying in a MEG scanner. As a control condition, patients performed a self-paced button-press task. AVH-state and non-AVH state were contrasted in a region-of-interest (ROI) approach. In addition, the two seconds before AVH onset were contrasted with the two seconds after AVH onset to elucidate a possible triggering mechanism.

Results

AVH correlated with a decrease in beta-band power in the left temporal cortex. A decrease in alpha-band power was observed in the right inferior frontal gyrus. AVH onset was related to a decrease in theta-band power in the right hippocampus.

Conclusions

These results suggest that AVH are triggered by a short aberration in the theta band in a memory-related structure, followed by activity in language areas accompanying the experience of AVH itself.     

Oscillatory activity in the neural networks of spiking elements

We study the dynamics of activity in the neural networks of enhanced integrate-and-fire elements (with random noise, refractory periods, signal propagation delay, decay of postsynaptic potential, etc.). We consider the networks composed of two interactive populations of excitatory and inhibitory neurons with all-to-all or random sparse connections. It is shown by computer simulations that the regime of regular oscillations is very stable in a broad range of parameter values. In particular, oscillations are possible even in the case of very sparse and randomly distributed inhibitory connections and high background activity. We describe two scenarios of how oscillations may appear which are similar to Andronov-Hopf and saddle-node-on-limit-cycle bifurcations in dynamical systems. The role of oscillatory dynamics for information encoding and processing is discussed.     

Ecology predicts large-scale patterns of phylogenetic diversification in birds

One of the most striking patterns in evolutionary biology is that clades may differ greatly in the number of species they contain. Numerous hypotheses have been put forward to explain this phenomenon, and several have been tested using phylogenetic methods. Remarkably, however, all such tests performed to date have been characterized by modest explanatory power, which has generated an interest in explanations stressing the importance of random processes. Here we make use of phylogenetic methods to test whether ecological variables, typically ignored in previous models, may explain phylogenetic tree imbalance in birds. We show that diversification rate possesses an intermediate phylogenetic signal across families. Using phylogenetic comparative methods, we then build a multipredictor model that explains more than 50% of the variation in diversification rate among clades. High annual dispersal is identified as the strongest predictor of high rates of diversification. In addition, high diversification rate is strongly associated with feeding generalization. In all but one instance, these key findings remain qualitatively unchanged when we use an alternative phylogeny and methodology and when small clades, containing five species or less, are excluded. Taken together, these results suggest that large-scale patterns in avian diversification can be explained by variation in intrinsic biology.     

The brainweb: phase synchronization and large-scale integration

The emergence of a unified cognitive moment relies on the coordination of scattered mosaics of functionally specialized brain regions. Here we review the mechanisms of large-scale integration that counterbalance the distributed anatomical and functional organization of brain activity to enable the emergence of coherent behaviour and cognition. Although the mechanisms involved in large-scale integration are still largely unknown, we argue that the most plausible candidate is the formation of dynamic links mediated by synchrony over multiple frequency bands.     

Oral shear stress predicts flavour perception in viscous solutions

The perception of sweetness and flavour were studied in viscous solutions containing 50 g/l sucrose, 100 p.p.m. iso-amyl acetate and varying concentrations of three hydrocolloid thickeners (guar gum, lambda-carrageenan and hydroxypropylmethyl cellulose). Zero-shear viscosity of the samples ranged from 1 to 5000 mPas. Perception of both sweetness and aroma was suppressed at thickener concentrations above c* (coil overlap concentration, the point at which there is an abrupt increase in solution viscosity as thickener concentration is increased). Sensory data for the three hydrocolloids was only loosely correlated with their concentration relative to c* (c/c* ratio), particularly above c*. However, when perceptual data were plotted against the Kokini oral shear stress (tau), calculated from rheological measurements, data for the three hydrocolloids aligned to form a master-curve, enabling the prediction of flavour intensity in such systems. The fact that oral shear stress can be used to model sweetness and aroma perception supports the hypothesis that somatosensory tactile stimuli can interact with taste and aroma signals to modulate their perception.     

Event-based exponential synchronization of complex networks

In this paper, we consider exponential synchronization of complex networks. The information diffusions between nodes are driven by properly defined events. By employing the M-matrix theory, algebraic graph theory and the Lyapunov method, two kinds of distributed event-triggering laws are designed, which avoid continuous communications between nodes. Then, several criteria that ensure the event-based exponential synchronization are presented, and the exponential convergence rates are obtained as well. Furthermore, we prove that Zeno behavior of the event-triggering laws can be excluded before synchronization being achieved, that is, the lower bounds of inter-event times are strictly positive. Finally, a simulation example is provided to illustrate the effectiveness of theoretical analysis.     

Stochastic synchronization of genetic oscillator networks

Background  

The study of synchronization among genetic oscillators is essential for the understanding of the rhythmic phenomena of living organisms at both molecular and cellular levels. Genetic networks are intrinsically noisy due to natural random intra- and inter-cellular fluctuations. Therefore, it is important to study the effects of noise perturbation on the synchronous dynamics of genetic oscillators. From the synthetic biology viewpoint, it is also important to implement biological systems that minimizing the negative influence of the perturbations.     

Measuring synchronization in neuronal networks for biosensor applications

Cultures of neurons can be grown on microelectrode arrays (MEAs), so that their spike and burst activity can be monitored. These activity patterns are quite sensitive to changes in the environment, such as chemical exposure, and hence the cultures can be used as biosensors. One key issue in analyzing the data from neuronal networks is how to quantify the level of synchronization among different units, which represent different neurons in the network. In this paper, we propose a synchronization metric, based on the statistical distribution of unit-to-unit correlation coefficients. We show that this synchronization metric changes significantly when the networks are exposed to bicuculline, strychnine, or 2,3-dioxo-6-nitro-l,2,3,4-tetrahydrobenzoquinoxaline-7-sulphonamide (NBQX). For that reason, this metric can be used to characterize pharmacologically induced changes in a network, either for research or for biosensor applications.     

Bioinformatics of large-scale protein interaction networks

We survey recent techniques for construction and prediction of large-scale protein interaction networks, focusing on computational processing steps. Special emphasis is placed on critical assessment of data completeness and reliability of the various approaches. Once built, protein interaction networks can be used for functional annotation or to generate higher-level biological hypotheses on pathways.     

The functional significance of synchronization of cortical potentials in humans

The dependence between the level and the topography of spatial synchronization of cortical potentials and the manifestation of motor reactions to light stimuli was studied in humans. To evaluate the spatial synchronization of potentials the correlation coefficients () were calculated between three points of the motor area and each of three occipital points of the cortex. The calculation was made using a computer with direct input of the potentials. At a definite level of the machine switched on a light stimulus and a mechanism recording the motor reaction. The experiments have shown that the higher the number of pair derivations giving a high value of , the more often movements are caused. Moreover it became apparent that the pattern of correlation ratios between the derivations depends on the motor reactions. In the case of movements a significant constancy of the ratios between individual derivations was found. It is shown that the spatial synchronization of potentials in the human cerebral cortex has a functional significance. However, this synchronization does not reflect the realization of movements but only the conditions required for them.Institute of Higher Nervous Activity and Neurophysiology, Academy of Sciences of the USSR, Moscow. Translated from Neirofiziologiya, Vol. 2, No. 2, pp. 166–172, March–April, 1970.     

Towards online multiresolution community detection in large-scale networks

The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks.