Abstract
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.
Original language | English |
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Article number | e1009163 |
Number of pages | 23 |
Journal | PLoS Computational Biology |
Volume | 17 |
Issue number | 6 |
DOIs | |
Publication status | Published - 28 Jun 2021 |
Bibliographical note
Funding:This work was supported by National Natural Science Foundation of China Grant 62072355 (AL), Shanxi Key Research and Development Programs of China Grant 2019ZDLGY13-07 (QW), Zhejiang Lab of China Grant 2019KC0AB03 and 2019KC0AD02 (JL), and Royal Society Newton Advanced Fellowship of UK Grant NAF-R1-191082 (JL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.