Anna Cattani

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Brain function operates through the coordinated activation of neuronal assemblies. Graph theory predicts that scale-free topologies, which include "hubs" (superconnected nodes), are an effective design to orchestrate synchronization. Whether hubs are present in neuronal assemblies and coordinate network activity remains unknown. Using network dynamics(More)
Light-powered molecular machines are conjectured to be essential constituents of future nanoscale devices. As a model for such systems, we have synthesized a polymer of bistable photosensitive azobenzenes. Individual polymers were investigated by single-molecule force spectroscopy in combination with optical excitation in total internal reflection. We were(More)
The recent development of multimedia communications across unreliable channels has brought the need for robust coding techniques, such that a partial loss of information does not necessarily imply the loss of the whole video sequence. The implementation of Multiple Description Coding (MDC) schemes based on the H.264/AVC coding standard provides an(More)
Mean-field approximations are a powerful tool for studying large neural networks. However, they do not describe well the behavior of networks composed of a small number of neurons. In this case, major differences between the mean-field approximation and the real behavior of the network can arise. Yet, many interesting problems in neuroscience involve the(More)
  • Anna Cattani
  • Mathematical biosciences and engineering : MBE
  • 2014
The aim of this work is to investigate the dynamics of a neural network, in which neurons, individually described by the FitzHugh-Nagumo model, are coupled by a generalized diffusive term. The formulation we are going to exploit is based on the general framework of graph theory. With the aim of defining the connection structure among the excitable elements,(More)
We consider an idealized network, formed by N neurons individually described by the FitzHugh-Nagumo equations and connected by electrical synapses. The limit for N → ∞ of the resulting discrete model is thoroughly investigated, with the aim of identifying a model for a continuum of neurons having an equivalent behaviour. Two strategies for passing to the(More)
Exactly solvable neural network models with asymmetric weights are rare, and exact solutions are available only in some mean-field approaches. In this article we find exact analytical solutions of an asymmetric spin-glass-like model of arbitrary size and we perform a complete study of its dynamical and statistical properties. The network has discrete-time(More)
The aim of the present paper is to efficiently describe the membrane potential dynamics of neural populations formed by species having a high density difference in specific brain areas. We propose a hybrid model whose main ingredients are a conductance-based model (ODE system) and its continuous counterpart (PDE system) obtained through a limit process in(More)