College of Information Science and Technology, Donghua University, Shanghai, 201620 China.
Glorious Sun School of Business and Management, Donghua University, Shanghai, 200051 China.
Cogn Neurodyn. 2014 Aug;8(4):335-44. doi: 10.1007/s11571-014-9290-4. Epub 2014 May 8.
A great number of biological experiments show that gamma oscillation occurs in many brain areas after the presentation of stimulus. The neural systems in these brain areas are highly heterogeneous. Specifically, the neurons and synapses in these neural systems are diversified; the external inputs and parameters of these neurons and synapses are heterogeneous. How the gamma oscillation generated in such highly heterogeneous networks remains a challenging problem. Aiming at this problem, a highly heterogeneous complex network model that takes account of many aspects of real neural circuits was constructed. The network model consists of excitatory neurons and fast spiking interneurons, has three types of synapses (GABAA, AMPA, and NMDA), and has highly heterogeneous external drive currents. We found a new regime for robust gamma oscillation, i.e. the oscillation in inhibitory neurons is rather accurate but the oscillation in excitatory neurons is weak, in such highly heterogeneous neural networks. We also found that the mechanism of the oscillation is a mixture of interneuron gamma (ING) and pyramidal-interneuron gamma (PING). We explained the mixture ING and PING mechanism in a consistent-way by a compound post-synaptic current, which has a slowly rising-excitatory stage and a sharp decreasing-inhibitory stage.
大量的生物实验表明，刺激呈现后许多脑区都会出现γ 振荡。这些脑区的神经系统高度异质，具体来说，这些神经系统中的神经元和突触多样化，这些神经元和突触的外部输入和参数也存在异质性。在如此高度异质的网络中，γ 振荡是如何产生的仍然是一个具有挑战性的问题。针对这一问题，构建了一个考虑到真实神经回路诸多方面的高度异质复杂网络模型。该网络模型由兴奋性神经元和快速放电中间神经元组成，具有三种类型的突触（GABAA、AMPA 和 NMDA），并具有高度异质的外部驱动电流。我们在如此高度异质的神经网络中发现了一种新的稳健γ 振荡模式，即抑制性神经元的振荡相当准确，而兴奋性神经元的振荡较弱。我们还发现，振荡的机制是中间神经元γ（ING）和锥体神经元-中间神经元γ（PING）的混合。我们通过一个具有缓慢上升的兴奋性阶段和急剧下降的抑制性阶段的复合突触后电流，以一致的方式解释了混合 ING 和 PING 机制。
