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Astrocyte- neuron interaction as a mechanism responsible for generation of neural synchrony: a study based on modeling and experiments

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Abstract

Neural synchronization is considered as an important mechanism for information processing. In addition, based on recent neurophysiologic findings, it is believed that astrocytes regulate the synaptic transmission of neuronal networks. Therefore, the present study focused on determining the functional contribution of astrocytes in neuronal synchrony using both computer simulations and extracellular field potential recordings. For computer simulations, as a first step, a minimal network model is constructed by connecting two Morris-Lecar neuronal models. In this minimal model, astrocyte-neuron interactions are considered in a functional-based procedure. Next, the minimal network is extended and a biologically plausible neuronal population model is developed which considers functional outcome of astrocyte-neuron interactions too. The employed structure is based on the physiological and anatomical network properties of the hippocampal CA1 area. Utilizing these two different levels of modeling, it is demonstrated that astrocytes are able to change the threshold value of transition from synchronous to asynchronous behavior among neurons. In this way, variations in the interaction between astrocytes and neurons lead to the emergence of synchronous/asynchronous patterns in neural responses. Furthermore, population spikes are recorded from CA1 pyramidal neurons in rat hippocampal slices to validate the modeling results. It demonstrates that astrocytes play a primary role in neuronal firing synchronicity and synaptic coordination. These results may offer a new insight into understanding the mechanism by which astrocytes contribute to stabilizing neural activities.

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Correspondence to Mahmood Amiri.

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Amiri, M., Hosseinmardi, N., Bahrami, F. et al. Astrocyte- neuron interaction as a mechanism responsible for generation of neural synchrony: a study based on modeling and experiments. J Comput Neurosci 34, 489–504 (2013). https://doi.org/10.1007/s10827-012-0432-6

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