Perspective: network-guided pattern formation of neural dynamics

The understanding of neural activity patterns is fundamentally linked to an understanding of how the brain's network architecture shapes dynamical processes. Established approaches rely mostly on deviations of a given network from certain classes of random graphs. Hypotheses about the supposed role of prominent topological features (for instance, the roles of modularity, network motifs, or hierarchical network organization) are derived from these deviations. An alternative strategy could be to study deviations of network architectures from regular graphs (rings, lattices) and consider the implications of such deviations for self-organized dynamic patterns on the network. Following this strategy, we draw on the theory of spatiotemporal pattern formation and propose a novel perspective for analyzing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states. In particular, we discuss the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the notion of network-guided pattern formation with numerical simulations and outline how it can facilitate the understanding of neural dynamics

INCF Lithuanian Workshop on Neuroscience and Information Technology

The aim of this workshop was to give a current overview of neuroscience and informatics research in Lithuania, and to discuss the strategies for forming the Lithuanian Neuroinformatics Node and becoming a member of INCF. The workshop was organized by Dr. Aušra Saudargiene (Department of Informatics, Vytautas Magnus University, Kaunas, and Faculty of Natural Sciences, Vilnius University, Lithuania) and INCF.
The workshop was attended by 15 invited speakers, among them 4 guests and 11 Lithuanian neuroscientists, and over 20 participants. The workshop was organized into three main sessions: overview of the INCF activities including the Swedish and UK nodes of INCF; presentations on Neuroscience research carried out in Lithuania; discussion about the strategies for forming an INCF national node, and the benefits of having such a node in Lithuania (Appendix A: Program; Appendix B: Abstracts)

Brain oscillations in a random neural network

[EN] It is well-known that rhythmic patterns of neural activity appear both in the normal and abnormal function of the brain. Apart from the standard bands of electric oscillations found in the electroencephalogram (EEG): from alpha (8-12 Hz) to delta waves (1-4 Hz), synchronized firing of neural populations characterize some complex cognitive functions such as memory, attention and consciousness. In the case of electrocardiogram (ECG) it is usually recognized that oscillations can be understood as the limit cycle of an underlying non-linear process in heart dynamics. However, the situation is not so clear for EEG and the origin and purpose of neural oscillations are still the subject of a heated debate. Our model is a version of the standard SIRS model from epidemiology in which susceptible, infected and recovered sites represent quiescent, firing and refractory neurons, respectively. Here we show that, in a SIRS random network epidemic model for neural activity, self-sustained oscillations appear in a restricted parameter region of the transition probabilities. This could explain the role of synchronized oscillations as a discriminant process for internal or external stimuli in brain dynamics. (C) 2011 Elsevier Ltd. All rights reserved.This work was supported by grant from the Universidad Politecnica de Valencia PAID-06-09 ref: 2588 and FIS Research Grant PI10/01433 from the Instituto de Salud Carlos III.Acedo Rodríguez, L.; Moraño Fernández, JA. (2013). Brain oscillations in a random neural network. Mathematical and Computer Modelling. 57(7-8):1768-1772. https://doi.org/10.1016/j.mcm.2011.11.02817681772577-

Multiscale Computations on Neural Networks: From the Individual Neuron Interactions to the Macroscopic-Level Analysis

We show how the Equation-Free approach for multi-scale computations can be exploited to systematically study the dynamics of neural interactions on a random regular connected graph under a pairwise representation perspective. Using an individual-based microscopic simulator as a black box coarse-grained timestepper and with the aid of simulated annealing we compute the coarse-grained equilibrium bifurcation diagram and analyze the stability of the stationary states sidestepping the necessity of obtaining explicit closures at the macroscopic level. We also exploit the scheme to perform a rare-events analysis by estimating an effective Fokker-Planck describing the evolving probability density function of the corresponding coarse-grained observables

Implementing vertex dynamics models of cell populations in biology within a consistent computational framework

The dynamic behaviour of epithelial cell sheets plays a central role during development, growth, disease and wound healing. These processes occur as a result of cell adhesion, migration, division, differentiation and death, and involve multiple processes acting at the cellular and molecular level. Computational models offer a useful means by which to investigate and test hypotheses about these processes, and have played a key role in the study of cell–cell interactions. However, the necessarily complex nature of such models means that it is difficult to make accurate comparison between different models, since it is often impossible to distinguish between differences in behaviour that are due to the underlying model assumptions, and those due to differences in the in silico implementation of the model. In this work, an approach is described for the implementation of vertex dynamics models, a discrete approach that represents each cell by a polygon (or polyhedron) whose vertices may move in response to forces. The implementation is undertaken in a consistent manner within a single open source computational framework, Chaste, which comprises fully tested, industrial-grade software that has been developed using an agile approach. This framework allows one to easily change assumptions regarding force generation and cell rearrangement processes within these models. The versatility and generality of this framework is illustrated using a number of biological examples. In each case we provide full details of all technical aspects of our model implementations, and in some cases provide extensions to make the models more generally applicable

Brain Dynamics across levels of Organization

After presenting evidence that the electrical activity recorded from the brain surface can reflect metastable state transitions of neuronal configurations at the mesoscopic level, I will suggest that their patterns may correspond to the distinctive spatio-temporal activity in the Dynamic Core (DC) and the Global Neuronal Workspace (GNW), respectively, in the models of the Edelman group on the one hand, and of Dehaene-Changeux, on the other. In both cases, the recursively reentrant activity flow in intra-cortical and cortical-subcortical neuron loops plays an essential and distinct role. Reasons will be given for viewing the temporal characteristics of this activity flow as signature of Self-Organized Criticality (SOC), notably in reference to the dynamics of neuronal avalanches. This point of view enables the use of statistical Physics approaches for exploring phase transitions, scaling and universality properties of DC and GNW, with relevance to the macroscopic electrical activity in EEG and EMG

A large-scale simulation of the piriform cortex by a cell automaton-based network model

An event-driven framework is used to construct physiologically motivated large-scale model of the piriform cortex containing in the order of 105 neuron-like computing units. This approach is based on a hierarchically defined highly abstract neuron model consisting of finite-state machines. It provides computational efficiency while incorporating components which have identifiable counterparts in the neurophysiological domain. The network model incorporates four neuron types, and glutamatergic excitatory and inhibitory synapses. The spatio-temporal patterns of cortical activity and the temporal and spectral characteristics of simulated electroencephalograms (EEGs) are studied. In line with previous experimental and compartmental work, 1) shock stimuli elicit EEG profiles with either isolated peaks or damped oscillations, the response type being determined by the intensity of the stimuli, and 2) temporally unpatterned input generates EEG oscillations supported by model-wide waves of excitation. <br/