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Function and Molecular Design of the Synapse

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Advances in Neural Computation, Machine Learning, and Cognitive Research II (NEUROINFORMATICS 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 799))

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Abstract

The majority of excitatory synapses in the mammalian forebrain and the hippocampus terminate onto dendritic spines. Even though their intricate molecular composition remains obscure in many aspects, available evidence suggests that these structures are highly specialized to support the short- and long-term plasticity crucial for flexible information processing. One concept that is extensively used to describe synaptic function is the Hebb’s postulate. However, the knowledge accumulated throughout following decades advocate for a broader scale in intercellular connection functions. Synaptic activity depends on interactions among sets of proteins (synaptic interactome) that assemble into complex supramolecular machines. Molecular biology, electrophysiology, and live-cell imaging studies have provided tantalizing glimpses into the inner workings of the synapse, yet fundamental questions regarding the functional organization of these “molecular nanomachines” remain to be answered. The presence of accessory receptors for secondary messengers in synapses along with receptors for primary mediator gave us the idea that these molecular constructs could be responsible for initial processing of the incoming signals. The purpose of this initial processing could be determined by analyzing and reconstructing this molecular informational machine that is essentially dendritic spine of hippocampal pyramidal neurons.

In this paper the data obtained during the implementation of RAS IV.35.2.6 and the RFBR 17-04-01440a projects are used.

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Correspondence to Anna L. Proskura .

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Proskura, A.L., Zapara, T.A. (2019). Function and Molecular Design of the Synapse. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research II. NEUROINFORMATICS 2018. Studies in Computational Intelligence, vol 799. Springer, Cham. https://doi.org/10.1007/978-3-030-01328-8_41

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