Complex temporal patterns processing by a neural mass model of a cortical column.

It is well known that neuronal networks are capable of transmitting complex spatiotemporal information in the form of precise sequences of neuronal discharges characterized by recurrent patterns. At the same time, the synchronized activity of large ensembles produces local field potentials that propagate through highly dynamic oscillatory waves, such that, at the whole brain scale, complex spatiotemporal dynamics of electroencephalographic (EEG) signals may be associated to sensorimotor decision making processes. Despite these experimental evidences, the link between highly temporally organized input patterns and EEG waves has not been studied in detail.

Differentiating resting brain states using ordinal symbolic analysis.

Symbolic methods of analysis are valuable tools for investigating complex time-dependent signals. In particular, the ordinal method defines sequences of symbols according to the ordering in which values appear in a time series. This method has been shown to yield useful information, even when applied to signals with large noise contamination.

Collective excitability in a mesoscopic neuronal model of epileptic activity.

At the mesoscopic scale, the brain can be understood as a collection of interacting neuronal oscillators, but the extent to which its sustained activity is due to coupling among brain areas is still unclear. Here we address this issue in a simplified situation by examining the effect of coupling between two cortical columns described via Jansen-Rit neural mass models. Our results show that coupling between the two neuronal populations gives rise to stochastic initiations of sustained collective activity, which can be interpreted as epileptic events.

Temporally correlated fluctuations drive epileptiform dynamics.

Macroscopic models of brain networks typically incorporate assumptions regarding the characteristics of afferent noise, which is used to represent input from distal brain regions or ongoing fluctuations in non-modelled parts of the brain. Such inputs are often modelled by Gaussian white noise which has a flat power spectrum. In contrast, macroscopic fluctuations in the brain typically follow a 1/f spectrum.

Crack Front Segmentation and Facet Coarsening in Mixed-Mode Fracture.

A planar crack generically segments into an array of "daughter cracks" shaped as tilted facets when loaded with both a tensile stress normal to the crack plane (mode I) and a shear stress parallel to the crack front (mode III). We investigate facet propagation and coarsening using in situ microscopy observations of fracture surfaces at different stages of quasistatic mixed-mode crack propagation and phase-field simulations. The results demonstrate that the bifurcation from propagating a planar to segmented crack front is strongly subcritical, reconciling previous theoretical predictions of linear stability analysis with experimental observations.

Synchronization-based computation through networks of coupled oscillators.

The mesoscopic activity of the brain is strongly dynamical, while at the same time exhibits remarkable computational capabilities. In order to examine how these two features coexist, here we show that the patterns of synchronized oscillations displayed by networks of neural mass models, representing cortical columns, can be used as substrates for Boolean-like computations. Our results reveal that the same neural mass network may process different combinations of dynamical inputs as different logical operations or combinations of them.

Cross-frequency transfer in a stochastically driven mesoscopic neuronal model.

The brain is known to operate in multiple coexisting frequency bands. Increasing experimental evidence suggests that interactions between those distinct bands play a crucial role in brain processes, but the dynamical mechanisms underlying this cross-frequency coupling are still under investigation. Two approaches have been proposed to address this issue.

Mesoscopic segregation of excitation and inhibition in a brain network model.

Neurons in the brain are known to operate under a careful balance of excitation and inhibition, which maintains neural microcircuits within the proper operational range. How this balance is played out at the mesoscopic level of neuronal populations is, however, less clear. In order to address this issue, here we use a coupled neural mass model to study computationally the dynamics of a network of cortical macrocolumns operating in a partially synchronized, irregular regime.

Probing scale interaction in brain dynamics through synchronization.

The mammalian brain operates in multiple spatial scales simultaneously, ranging from the microscopic scale of single neurons through the mesoscopic scale of cortical columns, to the macroscopic scale of brain areas. These levels of description are associated with distinct temporal scales, ranging from milliseconds in the case of neurons to tens of seconds in the case of brain areas. Here, we examine theoretically how these spatial and temporal scales interact in the functioning brain, by considering the coupled behaviour of two mesoscopic neural masses (NMs) that communicate with each other through a microscopic neuronal network (NN).

Information routing driven by background chatter in a signaling network.

Living systems are capable of processing multiple sources of information simultaneously. This is true even at the cellular level, where not only coexisting signals stimulate the cell, but also the presence of fluctuating conditions is significant. When information is received by a cell signaling network via one specific input, the existence of other stimuli can provide a background activity -or chatter- that may affect signal transmission through the network and, therefore, the response of the cell.

Helical crack-front instability in mixed-mode fracture.

Planar crack propagation under pure tension loading (mode I) is generally stable. However, it becomes universally unstable with the superposition of a shear stress parallel to the crack front (mode III). Under this mixed-mode (I + III) loading configuration, an initially flat parent crack segments into an array of daughter cracks that rotate towards a direction of maximum tensile stress.