Speaker
Toni Guillamon

Description The phase of an oscillator such as a spiking neuron is one of the main indicators of the effects of external stimuli on the (membrane potential) dynamics, and a key information to study synchrony in biological oscillators. Experimentally, the phase advancement is mostly computed through phase response curves (PRCs) obtained from recordings of the time variations in reaching the next peak of the membrane potential; successful methods have been used to predict it by means of theoretical PRCs evaluated on the attractor (limit cycle). However, stimulation in transient states may induce phase advancements that differ from the predictions given in the asymptotic state. By computing the isochrons (curves of constant phase) in a vicinity of the limit cycle, we are able to accurately generalize the PRCs to the transient states and, as well, to provide a methodology to compute the phase advancement under any type of stimulus (weak or strong, instantaneous or long-lasting). We use this methodology to illustrate how classical (non-transient) PRCs do not reflect important differences among cells such as bistability. We will also present theoretical examples to illustrate the goodness of the generalized PRCs, especially in cases of "weak" attractors or high-frequency stimuli. Finally, we will remark how the knowledge of second-order PRCs, together with a geometrical interpretation of the isochrons, can help to use the contribution of successive return times to refine experimentally computed PRCs.

Speaker
Gorka Zamora-Lopez

Description A striking question in neuroscience is how the brain is capable to segregate information (process information at different places) and to simultaneously integrate information to produce a coherent, multi-sensorial, perception of reality. All neural and brain networks investigated so far share common modular and hierarchical features that are believed to aid the brain this puzzle. Motivated by this problem we investigate the capacity of different classes of hierarchical and modular networks to host complex dynamical regimes, that is, when nodes are neither fully independent nor fully synchronized. We characterize this behaviour by introducing a measure of functional complexity.

Speaker
Maxi San MIguel

Description I will address the general issue of how collective social phenomena can be studied from a physicist´s perspective on interactions and on the use of models and data. I will consider social consensus as a paradigmatic problem, and I will discuss what can be learnt about this problem from the simple voter model that accounts for the mechanism of social imitation. I will also discuss what properties of the social network of interactions are relevant and what are irrelevant to reach consensus. Finally I will show how a metapopulation voter model properly describes the statistical regularities and spatial correlations found in US presidential electoral data. The model uses as input parameters census data on spatially distributed population and its mobility.

Speaker
Tiago Simas

Description Background: Semi-metric analysis studies the topological features of weighted mathematical relations and has been employed in many areas of complex networks, such as extracting knowledge from documents, predicting social behavior and so on. In this work semi-metric analysis was applied to the human brain connectome and specifically to assess the sensitivity and specificity of the technique in distinguishing adolescents with Autism Spectrum Condition (ASC) and Major Depressive Disorders (MDD) from their typically developing peers. Methods: Blood-oxygenation level dependent sensitive functional MRI data were acquired without the presence of an active task (i.e. passive rest). Weighted relations (networks) were derived from time-series correlations between brain regions. Subsequently, semi-metric measures were obtained that characterise the specialization and dispersion of communicability across the brain, as well as the strength of specialized or dispersed co-activity. Case-control differences were tested in terms of global and local semi-metricity. The inter-hemispheric relationship of semi-metricity was also examined. Results: This topological analysis of the human connectome identified: (1) relative to typically developing adolescents, participants with MDD and ASC had opposing directions of global specialization (increased in MDD) and dispersion (increased in ASC); (2) local regions of the brain where autism and depression differ from controls; (3) hemisphere lateralization effects, and finally; (4) give some evidence in support of findings that autism and depression are in opposite extremes of psychiatric disorders.