137 research outputs found
Onset of Phase Synchronization in Neurons Conneted via Chemical Synapses
We study the onset of synchronous states in realistic chaotic neurons coupled
by mutually inhibitory chemical synapses. For the realistic parameters, namely
the synaptic strength and the intrinsic current, this synapse introduces
non-coherences in the neuronal dynamics, yet allowing for chaotic phase
synchronization in a large range of parameters. As we increase the synaptic
strength, the neurons undergo to a periodic state, and no chaotic complete
synchronization is found.Comment: to appear in Int. J. Bif. Chao
Do brain networks evolve by maximizing their information flow capacity?
We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization
Neuronal synchrony: peculiarity and generality
Synchronization in neuronal systems is a new and intriguing application of dynamical systems theory. Why are neuronal systems different as a subject for synchronization? (1) Neurons in themselves are multidimensional nonlinear systems that are able to exhibit a wide variety of different activity patterns. Their âdynamical repertoireâ includes regular or chaotic spiking, regular or chaotic bursting, multistability, and complex transient regimes. (2) Usually, neuronal oscillations are the result of the cooperative activity of many synaptically connected neurons (a neuronal circuit). Thus, it is necessary to consider synchronization between different neuronal circuits as well. (3) The synapses that implement the coupling between neurons are also dynamical elements and their intrinsic dynamics influences the process of synchronization or entrainment significantly. In this review we will focus on four new problems: (i) the synchronization in minimal neuronal networks with plastic synapses (synchronization with activity dependent coupling), (ii) synchronization of bursts that are generated by a group of nonsymmetrically coupled inhibitory neurons (heteroclinic synchronization), (iii) the coordination of activities of two coupled neuronal networks (partial synchronization of small composite structures), and (iv) coarse grained synchronization in larger systems (synchronization on a mesoscopic scale
Optimal network topologies for information transmission in active networks
This work clarifies the relation between network circuit (topology) and
behavior (information transmission and synchronization) in active networks,
e.g. neural networks. As an application, we show how to determine a network
topology that is optimal for information transmission. By optimal, we mean that
the network is able to transmit a large amount of information, it possesses a
large number of communication channels, and it is robust under large variations
of the network coupling configuration. This theoretical approach is general and
does not depend on the particular dynamic of the elements forming the network,
since the network topology can be determined by finding a Laplacian matrix (the
matrix that describes the connections and the coupling strengths among the
elements) whose eigenvalues satisfy some special conditions. To illustrate our
ideas and theoretical approaches, we use neural networks of electrically
connected chaotic Hindmarsh-Rose neurons.Comment: 20 pages, 12 figure
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