Understanding how the brain responds to sensory inputs from non-invasive brain recordings
like magnetoencephalography (MEG) can be particularly challenging:(i) the high …

Understanding how the brain responds to sensory inputs is challenging: brain recordings
are partial, noisy, and high dimensional; they vary across sessions and subjects and they …

We study the dynamical inactivity of the global network of identical oscillators in the
presence of mixed attractive and repulsive coupling. We consider that the oscillators are a …

A fundamental goal in network neuroscience is to understand how activity in one brain
region drives activity elsewhere, a process referred to as effective connectivity. Here we …

Dynamic causal models (DCMs) of electrophysiological data allow, in principle, for inference
on hidden, bulk synaptic function in neural circuits. The directed influences between the …

The role of counter-rotating oscillators in an ensemble of coexisting co-and counter-rotating
oscillators is examined by increasing the proportion of the latter. The phenomenon of aging …

The construction of synthetic complex-valued signals from real-valued observations is an
important part of many time series analysis techniques. The most widely used approach is …

This work focuses on a class of stochastic functional differential equations and neutral
stochastic differential functional equations. By using a new approach, some sufficient …

Deterioration or failure of even a fraction of the microscopic constituents of a large class of
networks leads to the loss of the macroscopic activity of the network as a whole. We deduce …

The construction of a complex-valued signal is an important step in many time series
analysis techniques. In this paper, we model the observable real-valued signal as the real …