Bridging functional and anatomical neural connectivity through cluster synchronization.

The dynamics of the brain results from the complex interplay of several neural populations and is affected by both the individual dynamics of these areas and their connection structure. Hence, a fundamental challenge is to derive models of the brain that reproduce both structural and functional features measured experimentally. Our work combines neuroimaging data, such as dMRI, which provides information on the structure of the anatomical connectomes, and fMRI, which detects patterns of approximate synchronous activity between brain areas.

Pinning control of networks: Dimensionality reduction through simultaneous block-diagonalization of matrices.

In this paper, we study the network pinning control problem in the presence of two different types of coupling: (i) node-to-node coupling among the network nodes and (ii) input-to-node coupling from the source node to the "pinned nodes." Previous work has mainly focused on the case that (i) and (ii) are of the same type. We decouple the stability analysis of the target synchronous solution into subproblems of the lowest dimension by using the techniques of simultaneous block diagonalization of matrices.

One-way dependent clusters and stability of cluster synchronization in directed networks.

Cluster synchronization in networks of coupled oscillators is the subject of broad interest from the scientific community, with applications ranging from neural to social and animal networks and technological systems. Most of these networks are directed, with flows of information or energy that propagate unidirectionally from given nodes to other nodes. Nevertheless, most of the work on cluster synchronization has focused on undirected networks.

Delays induced cluster synchronization in chaotic networks.

We study networks of coupled oscillators and analyze the role of coupling delays in determining the emergence of cluster synchronization. Given a network topology and a particular arrangement of the coupling delays over the network connections, different patterns of cluster synchronization may emerge. We focus on a simple ring network of six bidirectionally coupled identical oscillators, for which with two different values of the delays, a total of eight cluster synchronization patterns may emerge, depending on the assignment of the delays to the ring connections.

Analyzing synchronized clusters in neuron networks.

The presence of synchronized clusters in neuron networks is a hallmark of information transmission and processing. Common approaches to study cluster synchronization in networks of coupled oscillators ground on simplifying assumptions, which often neglect key biological features of neuron networks. Here we propose a general framework to study presence and stability of synchronous clusters in more realistic models of neuron networks, characterized by the presence of delays, different kinds of neurons and synapses.

Design Principles for Central Pattern Generators With Preset Rhythms.

This article is concerned with the design of synthetic central pattern generators (CPGs). Biological CPGs are neural circuits that determine a variety of rhythmic activities, including locomotion, in animals. A synthetic CPG is a network of dynamical elements (here called cells) properly coupled by various synapses to emulate rhythms produced by a biological CPG.

Dimensional reduction in networks of non-Markovian spiking neurons: Equivalence of synaptic filtering and heterogeneous propagation delays.

Message passing between components of a distributed physical system is non-instantaneous and contributes to determine the time scales of the emerging collective dynamics. In biological neuron networks this is due in part to local synaptic filtering of exchanged spikes, and in part to the distribution of the axonal transmission delays. How differently these two kinds of communication protocols affect the network dynamics is still an open issue due to the difficulties in dealing with the non-Markovian nature of synaptic transmission.

Phase analysis method for burst onset prediction.

The response of bursting neurons to fluctuating inputs is usually hard to predict, due to their strong nonlinearity. For the same reason, decoding the injected stimulus from the activity of a bursting neuron is generally difficult. In this paper we propose a method describing (for neuron models) a mechanism of phase coding relating the burst onsets with the phase profile of the input current.

A mathematical model for the vessel recruitment in coronary microcirculation in the absence of active autoregulation.

This paper proposes a mathematical model for vessel recruitment in the microvascular coronary network. The model is based on microvascular network units (MVNUs), where we define a MVNU as a portion of the microvascular network comprising seven generations of identical, parallel-arranged vessels (upstream arteries, large and small arterioles, capillaries, small and large venules, and downstream veins). The model implements a new mechanism to describe the variation in the number of MVNU in response to sudden variations of the local input pressure.

Inferring network dynamics and neuron properties from population recordings.

Understanding the computational capabilities of the nervous system means to "identify" its emergent multiscale dynamics. For this purpose, we propose a novel model-driven identification procedure and apply it to sparsely connected populations of excitatory integrate-and-fire neurons with spike frequency adaptation (SFA). Our method does not characterize the system from its microscopic elements in a bottom-up fashion, and does not resort to any linearization.

Barriers to transport induced by periodic oscillations in a physical model of the human vitreous chamber.

Understanding mixing processes that occur in the human vitreous chamber is of fundamental importance due to the relevant clinical implications in drug delivery treatments of several eye conditions. In this article we rely on experimental observations (which demonstrated that dispersion coefficients largely dominate diffusive coefficients) on a physical model of the human eye to perform an analysis based on Lagrangian trajectories. In particular, we study barriers to transport in a particularly significant two-dimensional section of the eye model by using nonlinear dynamical systems theoretical and numerical tools.

Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.

Stochastic channel gating is the major source of intrinsic neuronal noise whose functional consequences at the microcircuit- and network-levels have been only partly explored. A systematic study of this channel noise in large ensembles of biophysically detailed model neurons calls for the availability of fast numerical methods. In fact, exact techniques employ the microscopic simulation of the random opening and closing of individual ion channels, usually based on Markov models, whose computational loads are prohibitive for next generation massive computer models of the brain.

The Hindmarsh-Rose neuron model: bifurcation analysis and piecewise-linear approximations.

This paper provides a global picture of the bifurcation scenario of the Hindmarsh-Rose model. A combination between simulations and numerical continuations is used to unfold the complex bifurcation structure. The bifurcation analysis is carried out by varying two bifurcation parameters and evidence is given that the structure that is found is universal and appears for all combinations of bifurcation parameters.

A modular supervised algorithm for vessel segmentation in red-free retinal images.

In this paper, a supervised algorithm for vessel segmentation in red-free images of the human retina is proposed. The algorithm is modular and made up of two fundamental blocks. The optimal values of two algorithm parameters are found out by maximizing proper measures of performances (MOPs) able to evaluate from a quantitative point of view the results provided by the proposed algorithm.

On the complexity of periodic and nonperiodic behaviors of a hysteresis-based electronic oscillator.

We investigate the families of periodic and nonperiodic behaviors admitted by a hysteresis-based circuit oscillator. The analysis is carried out by combining brute-force simulations with continuation methods. As a result of the analysis, it is shown that the existence of many different periodic solutions and of the chaotic behaviors associated with them is organized by few codimension-2 bifurcation points.