Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Biochem Biophys Res Commun. 2012 Oct 26;427(3):466-72. doi: 10.1016/j.bbrc.2012.08.081. Epub 2012 Aug 24.
Synaptic plasticity is believed to play an important role in hippocampal learning and memory. The precise and relative timing of pre- and postsynaptic activity has been shown to determine the sign and amplitude of hippocampal synaptic plasticity through spike timing-dependent plasticity (STDP). While most studies on STDP have mainly focused on excitatory synapses, neural networks are composed not only of excitatory synapses, but also of inhibitory synapses. Interneurons are known to make inhibitory synaptic connections with hippocampal CA1 pyramidal neurons through feedforward and feedback inhibitory networks. However, the roles of different inhibitory network structures on STDP remain unknown. Using a simplified hippocampal network model with a deterministic Ca(2+) dynamics-dependent STDP model, we show that feedforward and feedback inhibitory networks differentially modulate STDP. Moreover, inhibitory synaptic weight and synaptic location influenced the STDP profile. Taken together, our results provide a computational role of inhibitory network in STDP and in memory processing of hippocampal circuits.
突触可塑性被认为在海马体学习和记忆中发挥重要作用。通过尖峰时间依赖可塑性(STDP),已经证明了前后突触活动的精确和相对时间决定了海马体突触可塑性的符号和幅度。虽然大多数关于 STDP 的研究主要集中在兴奋性突触上,但神经网络不仅由兴奋性突触组成,还由抑制性突触组成。已知中间神经元通过前馈和反馈抑制网络与海马 CA1 锥体神经元形成抑制性突触连接。然而,不同抑制性网络结构对 STDP 的作用仍不清楚。使用具有确定性 Ca(2+)动力学依赖性 STDP 模型的简化海马体网络模型,我们表明前馈和反馈抑制网络对 STDP 有不同的调节作用。此外,抑制性突触权重和突触位置影响 STDP 曲线。总的来说,我们的结果提供了抑制性网络在 STDP 以及海马体电路记忆处理中的计算作用。