Regulating synchronous oscillations of cerebellar granule cells by different types of inhibition

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.

Dissecting cascade computational components in spiking neural networks

“…A ubiquitous feature of neural circuits in the brain is that neurons are organized by layers or stages. Although there are dramatic feedback and/or recurrent connections between neurons [ 60 ], the information flow within recurrent neural networks could reinforce neurons to form a prevailing feedforward format of dynamics, utilizing synaptic plasticities [ 61 , 62 ]. One prominent example is the neural trajectory, in which different neurons fire at particular time points so that the overall dynamics of neural populations becomes a trajectory spanning over time, such as songbird neural dynamics [ 63 ], and space, such as memory dynamics of place cells [ 64 ].…”

Diverse role of NMDA receptors for dendritic integration of neural dynamics

“…For the inhibitory synaptic input, a model of GABA A synapse was applied with similar dynamics as the AMPA synapse, except that the parameters were adjusted to be inhibitory as in [ 65 ]. Detailed model parameters of AMPA, NMDA, and GABA A receptors are listed in Table 1 .…”

“…For neuronal somatic spiking responses, there are two fundamental firing modes as regular and bursting spikes [60][61][62]. In the case of PCs, regular or simple spikes are mostly triggered by parallel fibers, while bursting or complex spikes are induced by climbing fibers [63][64][65][66]. A single CF makes approximately 500 synaptic contacts with PC proximal dendrites, discarding at a low frequency [37,42].…”