Literature DB >> 21230157

Combined effect of chemical and electrical synapses in Hindmarsh-Rose neural networks on synchronization and the rate of information.

M S Baptista1, F M Moukam Kakmeni, C Grebogi.   

Abstract

In this work we studied the combined action of chemical and electrical synapses in small networks of Hindmarsh-Rose (HR) neurons on the synchronous behavior and on the rate of information produced (per time unit) by the networks. We show that if the chemical synapse is excitatory, the larger the chemical synapse strength used the smaller the electrical synapse strength needed to achieve complete synchronization, and for moderate synaptic strengths one should expect to find desynchronous behavior. Otherwise, if the chemical synapse is inhibitory, the larger the chemical synapse strength used the larger the electrical synapse strength needed to achieve complete synchronization, and for moderate synaptic strengths one should expect to find synchronous behaviors. Finally, we show how to calculate semianalytically an upper bound for the rate of information produced per time unit (Kolmogorov-Sinai entropy) in larger networks. As an application, we show that this upper bound is linearly proportional to the number of neurons in a network whose neurons are highly connected.

Entities:  

Mesh:

Year:  2010        PMID: 21230157     DOI: 10.1103/PhysRevE.82.036203

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  9 in total

1.  Collective almost synchronisation in complex networks.

Authors:  Murilo S Baptista; Hai-Peng Ren; Johen C M Swarts; Rodrigo Carareto; Henk Nijmeijer; Celso Grebogi
Journal:  PLoS One       Date:  2012-11-08       Impact factor: 3.240

2.  Do Brain Networks Evolve by Maximizing Their Information Flow Capacity?

Authors:  Chris G Antonopoulos; Shambhavi Srivastava; Sandro E de S Pinto; Murilo S Baptista
Journal:  PLoS Comput Biol       Date:  2015-08-28       Impact factor: 4.475

3.  Chaotic, informational and synchronous behaviour of multiplex networks.

Authors:  M S Baptista; R M Szmoski; R F Pereira; S E de Souza Pinto
Journal:  Sci Rep       Date:  2016-03-04       Impact factor: 4.379

4.  Weak connections form an infinite number of patterns in the brain.

Authors:  Hai-Peng Ren; Chao Bai; Murilo S Baptista; Celso Grebogi
Journal:  Sci Rep       Date:  2017-04-21       Impact factor: 4.379

5.  Maintaining extensivity in evolutionary multiplex networks.

Authors:  Chris G Antonopoulos; Murilo S Baptista
Journal:  PLoS One       Date:  2017-04-12       Impact factor: 3.240

6.  Stochastic synchronization of neurons: the topologicalimpacts.

Authors:  Saurabh Kumar Sharma; Md Zubbair Malik; R K Brojen Singh
Journal:  Bioinformation       Date:  2018-12-09

7.  Rhythmic Oscillations of Excitatory Bursting Hodkin-Huxley Neuronal Network with Synaptic Learning.

Authors:  Qi Shi; Fang Han; Zhijie Wang; Caiyun Li
Journal:  Comput Intell Neurosci       Date:  2016-03-17

8.  Chimera-like States in Modular Neural Networks.

Authors:  Johanne Hizanidis; Nikos E Kouvaris; Gorka Zamora-López; Zamora-López Gorka; Albert Díaz-Guilera; Chris G Antonopoulos
Journal:  Sci Rep       Date:  2016-01-22       Impact factor: 4.379

9.  Autapses promote synchronization in neuronal networks.

Authors:  Huawei Fan; Yafeng Wang; Hengtong Wang; Ying-Cheng Lai; Xingang Wang
Journal:  Sci Rep       Date:  2018-01-12       Impact factor: 4.379

  9 in total

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