Jochems Arthur, Yoshida Motoharu
International Graduate School of Neuroscience, Ruhr-University Bochum, Bochum, Germany.
International Graduate School of Neuroscience, Ruhr-University Bochum, Bochum, Germany; Faculty of Psychology, Mercator Research Group-Structure of Memory, Ruhr-University Bochum, Bochum, Germany.
PLoS One. 2015 Apr 22;10(4):e0123799. doi: 10.1371/journal.pone.0123799. eCollection 2015.
Persistent firing is believed to support short-term information retention in the brain. Established hypotheses make use of the recurrent synaptic connectivity to support persistent firing. However, this mechanism is known to suffer from a lack of robustness. On the other hand, persistent firing can be supported by an intrinsic cellular mechanism in multiple brain areas. However, the consequences of having both the intrinsic and the synaptic mechanisms (a hybrid model) on persistent firing remain largely unknown. The goal of this study is to investigate whether a hybrid neural network model with these two mechanisms has advantages over a conventional recurrent network based model. Our computer simulations were based on in vitro recordings obtained from hippocampal CA3 pyramidal cells under cholinergic receptor activation. Calcium activated non-specific cationic (CAN) current supported persistent firing in the Hodgkin-Huxley style cellular models. Our results suggest that the hybrid model supports persistent firing within a physiological frequency range over a wide range of different parameters, eliminating parameter sensitivity issues generally recognized in network based persistent firing. In addition, persistent firing in the hybrid model is substantially more robust against distracting inputs, can coexist with theta frequency oscillations, and supports pattern completion.
持续发放被认为有助于大脑中的短期信息保留。已有的假说利用循环突触连接来支持持续发放。然而，已知这种机制缺乏稳健性。另一方面，多个脑区的内在细胞机制可以支持持续发放。然而，内在机制和突触机制（一种混合模型）共同作用对持续发放的影响在很大程度上仍不清楚。本研究的目的是探究具有这两种机制的混合神经网络模型是否比传统的基于循环网络的模型具有优势。我们的计算机模拟基于胆碱能受体激活下从海马CA3锥体细胞获得的体外记录。钙激活非特异性阳离子（CAN）电流在霍奇金-赫胥黎式细胞模型中支持持续发放。我们的结果表明，混合模型在广泛的不同参数范围内的生理频率范围内支持持续发放，消除了基于网络的持续发放中普遍存在的参数敏感性问题。此外，混合模型中的持续发放对干扰输入具有更强的稳健性，可以与θ频率振荡共存，并支持模式完成。
