Role of chemical synapses in coupled neurons with noise

Balenzuela, Pablo; García‐Ojalvo, Jordi

doi:10.1103/physreve.72.021901

Cited by 61 publications

(29 citation statements)

References 18 publications

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“…This array-enhanced coherence was reported only in the case of linear (diffusive) electrical coupling, mediated by gap junctions between the neurons. The coherence of an array of ML neurons coupled via chemical synapses was studied with changing the noise level in [168]. Results showed that chemical synapses were more efficient than gap junctions in enhancing coherence at optimal noise (known as array-enhanced CR).…”

Section: Noise-induced Resonance and Spatiotemporal Patterns In Neuromentioning

confidence: 98%

Dynamics of firing patterns, synchronization and resonances in neuronal electrical activities: experiments and analysis

Yang

et al. 2008

Acta Mech Sin

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Recent advances in the experimental and theoretical study of dynamics of neuronal electrical firing activities are reviewed. Firstly, some experimental phenomena of neuronal irregular firing patterns, especially chaotic and stochastic firing patterns, are presented, and practical nonlinear time analysis methods are introduced to distinguish deterministic and stochastic mechanism in time series. Secondly, the dynamics of electrical firing activities in a single neuron is concerned, namely, fast-slow dynamics analysis for classification and mechanism of various bursting patterns, one-or two-parameter bifurcation analysis for transitions of firing patterns, and stochastic dynamics of firing activities (stochastic and coherence resonances, integer multiple and other firing patterns induced by noise, etc.).types of synchronization of coupled neurons with electrical and chemical synapses are discussed. As noise and time delay are inevitable in nervous systems, it is found that noise and time delay may induce or enhance synchronization and change firing patterns of coupled neurons. Noise-induced resonance and spatiotemporal patterns in coupled neuronal networks are also demonstrated. Finally, some prospects are presented for future research. In consequence, the idea and methods of nonlinear dynamics are of great significance in exploration of dynamic processes and physiological functions of nervous systems.

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Section: Noise-induced Resonance and Spatiotemporal Patterns In Neuromentioning

confidence: 98%

Dynamics of firing patterns, synchronization and resonances in neuronal electrical activities: experiments and analysis

Yang

et al. 2008

Acta Mech Sin

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“…For step 1, a total of 1,000 time series datasets were generated for each synapse type by systematically varying the coupling strength k in the range [1e-4, 0.5] mS/cm 2 , D of both neurons in the range [0, 80], and I dc of both neurons in the range [40,85] (Balenzuela and García-Ojalvo 2005). Only unidirectional couplings were considered.…”

Section: Decision Tree Constructionmentioning

confidence: 99%

Detecting effective connectivity in networks of coupled neuronal oscillators

et al. 2011

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The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.

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“…The coherence of an array of Morris-Lecar neurons coupled via chemical synapses was studied with the noise level changed (Balenzuela and Garcl´a-Ojalvo 2005). Results showed that chemical synapses were more efficient than gap junctions in enhancing coherence at optimal noise (known as arrayenhanced coherence resonance).…”

Section: Introductionmentioning

confidence: 99%

Firing synchronization and temporal order in noisy neuronal networks

Shi

Wang

2008

Cogn Neurodyn

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Noise-induced complete synchronization and frequency synchronization in coupled spiking and bursting neurons are studied firstly. The effects of noise and coupling are discussed. It is found that bursting neurons are easier to achieve firing synchronization than spiking ones, which means that bursting activities are more important for information transfer in neuronal networks. Secondly, the effects of noise on firing synchronization in a noisy map neuronal network are presented. Noise-induced synchronization and temporal order are investigated by means of the firing rate function and the order index. Firing synchronization and temporal order of excitatory neurons can be greatly enhanced by subthreshold stimuli with resonance frequency. Finally, it is concluded that random perturbations play an important role in firing activities and temporal order in neuronal networks.

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Role of chemical synapses in coupled neurons with noise

Cited by 61 publications

References 18 publications

Dynamics of firing patterns, synchronization and resonances in neuronal electrical activities: experiments and analysis

Dynamics of firing patterns, synchronization and resonances in neuronal electrical activities: experiments and analysis

Detecting effective connectivity in networks of coupled neuronal oscillators

Firing synchronization and temporal order in noisy neuronal networks

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