School of Computer Science and Technology, Xidian University, Xi'an, China.
Guangzhou institute of technology, Xidian University, Guangzhou, China.
PLoS Comput Biol. 2021 Jun 28;17(6):e1009163. doi: 10.1371/journal.pcbi.1009163. eCollection 2021 Jun.
Synchronous oscillations in neural populations are considered being controlled by inhibitory neurons. In the granular layer of the cerebellum, two major types of cells are excitatory granular cells (GCs) and inhibitory Golgi cells (GoCs). GC spatiotemporal dynamics, as the output of the granular layer, is highly regulated by GoCs. However, there are various types of inhibition implemented by GoCs. With inputs from mossy fibers, GCs and GoCs are reciprocally connected to exhibit different network motifs of synaptic connections. From the view of GCs, feedforward inhibition is expressed as the direct input from GoCs excited by mossy fibers, whereas feedback inhibition is from GoCs via GCs themselves. In addition, there are abundant gap junctions between GoCs showing another form of inhibition. It remains unclear how these diverse copies of inhibition regulate neural population oscillation changes. Leveraging a computational model of the granular layer network, we addressed this question to examine the emergence and modulation of network oscillation using different types of inhibition. We show that at the network level, feedback inhibition is crucial to generate neural oscillation. When short-term plasticity was equipped on GoC-GC synapses, oscillations were largely diminished. Robust oscillations can only appear with additional gap junctions. Moreover, there was a substantial level of cross-frequency coupling in oscillation dynamics. Such a coupling was adjusted and strengthened by GoCs through feedback inhibition. Taken together, our results suggest that the cooperation of distinct types of GoC inhibition plays an essential role in regulating synchronous oscillations of the GC population. With GCs as the sole output of the granular network, their oscillation dynamics could potentially enhance the computational capability of downstream neurons.
神经元群体中的同步振荡被认为是由抑制性神经元控制的。在小脑颗粒层中，两种主要类型的细胞是兴奋性颗粒细胞（GCs）和抑制性高尔基细胞（GoCs）。GC 的时空动力学作为颗粒层的输出，受到 GoCs 的高度调节。然而，GoCs 实施了各种类型的抑制。通过苔藓纤维的输入，GCs 和 GoCs 相互连接，表现出不同的突触连接网络模式。从 GC 的角度来看，前馈抑制表现为直接来自被苔藓纤维兴奋的 GoCs 的输入，而反馈抑制则来自通过 GC 本身的 GoCs。此外，GoCs 之间存在丰富的缝隙连接，表现出另一种抑制形式。目前尚不清楚这些不同类型的抑制如何调节神经元群体振荡的变化。利用颗粒层网络的计算模型，我们解决了这个问题，研究了使用不同类型的抑制来产生和调制网络振荡。我们表明，在网络水平上，反馈抑制对于产生神经振荡至关重要。当 GoC-GC 突触上配备了短期可塑性时，振荡大大减少。只有当存在额外的缝隙连接时，才能出现稳健的振荡。此外，在振荡动力学中存在相当大的交叉频域耦合。这种耦合通过反馈抑制被 GoCs 调整和增强。总之，我们的结果表明，不同类型的 GoC 抑制的合作在调节 GC 群体的同步振荡中起着至关重要的作用。由于 GC 是颗粒网络的唯一输出，它们的振荡动力学可能会增强下游神经元的计算能力。
