论文标题

神经元处理中的多层自适应网络

Multilayer adaptive networks in neuronal processing

论文作者

Hernández, Adrián, Amigó, José M.

论文摘要

连接组是映射大脑中所有神经连接的接线图。在细胞水平上,它提供了生物体的一部分或全部大脑内神经元和突触的图。近年来,通过网络科学和图理论在研究中取得了重大进展。该分析对于了解神经传递(快速突触传输)网络至关重要。但是,神经元将其他形式的通信作为神经调节,而不是传达激发或抑制,而是改变神经元和突触特性。这种额外的神经调节层条件并重新配置连接组。在本文中,我们建议多层自适应网络,其中不同的突触和神经化学层相互作用,是解释神经元处理的合适框架。然后,我们描述了一个简化的多层自适应网络模型,该模型解释了这些交互的超级层次,并分析了有趣的计算能力的出现。

The connectome is a wiring diagram mapping all the neural connections in the brain. At the cellular level, it provides a map of the neurons and synapses within a part or all of the brain of an organism. In recent years, significant advances have been made in the study of the connectome via network science and graph theory. This analysis is fundamental to understand neurotransmission (fast synaptic transmission) networks. However, neurons use other forms of communication as neuromodulation that, instead of conveying excitation or inhibition, change neuronal and synaptic properties. This additional neuromodulatory layers condition and reconfigure the connectome. In this paper, we propose that multilayer adaptive networks, in which different synaptic and neurochemical layers interact, are the appropriate framework to explain neuronal processing. Then, we describe a simplified multilayer adaptive network model that accounts for these extra-layers of interaction and analyse the emergence of interesting computational capabilities.

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