Institute for Cognitive Neurodynamics, School of Information Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, People's Republic of China.
Cogn Neurodyn. 2010 Dec;4(4):359-66. doi: 10.1007/s11571-010-9129-6. Epub 2010 Aug 25.
Camperi and Wang (Comput Neurosci 5:383-405, 1998) presented a network model for working memory that combines intrinsic cellular bistability with the recurrent network architecture of the neocortex. While Fall and Rinzel (Comput Neurosci 20:97-107, 2006) replaced this intrinsic bistability with a biological mechanism-Ca(2+) release subsystem. In this study, we aim to further expand the above work. We integrate the traditional firing-rate network with Ca(2+) subsystem-induced bistability, amend the synaptic weights and suggest that Ca(2+) concentration only increase the efficacy of synaptic input but has nothing to do with the external input for the transient cue. We found that our network model maintained the persistent activity in response to a brief transient stimulus like that of the previous two models and the working memory performance was resistant to noise and distraction stimulus if Ca(2+) subsystem was tuned to be bistable.
卡姆佩里和王（Comput Neurosci 5:383-405, 1998）提出了一个工作记忆的网络模型，该模型将内在细胞双稳态与新皮层的递归网络结构相结合。而法尔和林泽尔（Comput Neurosci 20:97-107, 2006）则用一种生物机制——钙离子释放子系统来替代这种内在双稳态。在本研究中，我们旨在进一步扩展上述工作。我们将传统的发放率网络与钙离子子系统诱导的双稳态相结合，修正了突触权重，并提出钙离子浓度仅增加突触输入的效能，而与外部输入无关，对于短暂的提示。我们发现，我们的网络模型在响应短暂的瞬态刺激时保持持续的活动，就像前两个模型一样，如果钙离子子系统被调制成双稳态，那么工作记忆性能对噪声和干扰刺激具有抗干扰性。
