Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways

Abstract

The dynamics of cerebellar neuronal networks is controlled by the underlying building blocks of neurons and synapses between them. For which, the computation of Purkinje cells (PCs), the only output cells of the cerebellar cortex, is implemented through various types of neural pathways interactively routing excitation and inhibition converged to PCs. Such tuning of excitation and inhibition, coming from the gating of specific pathways as well as short-term plasticity (STP) of the synapses, plays a dominant role in controlling the PC dynamics in terms of firing rate and spike timing. PCs receive cascade feedforward inputs from two major neural pathways: the first one is the feedforward excitatory pathway from granule cells (GCs) to PCs; the second one is the feedforward inhibition pathway from GCs, via molecular layer interneurons (MLIs), to PCs. The GC-PC pathway, together with short-term dynamics of excitatory synapses, has been a focus over past decades, whereas recent experimental evidence shows that MLIs also greatly contribute to controlling PC activity. Therefore, it is expected that the diversity of excitation gated by STP of GC-PC synapses, modulated by strong inhibition from MLI-PC synapses, can promote the computation performed by PCs. However, it remains unclear how these two neural pathways are interacted to modulate PC dynamics. Here using a computational model of PC network installed with these two neural pathways, we addressed this question to investigate the change of PC firing dynamics at the level of single cell and network. We show that the nonlinear characteristics of excitatory STP dynamics can significantly modulate PC spiking dynamics mediated by inhibition. The changes in PC firing rate, firing phase, and temporal spike pattern, are strongly modulated by these two factors in different ways. MLIs mainly contribute to variable delays in the postsynaptic action potentials of PCs while modulated by excitation STP. Notably, the diversity of synchronization and pause response in the PC network is governed not only by the balance of excitation and inhibition, but also by the synaptic STP, depending on input burst patterns. Especially, the pause response shown in the PC network can only emerge with the interaction of both pathways. Together with other recent findings, our results show that the interaction of feedforward pathways of excitation and inhibition, incorporated with synaptic short-term dynamics, can dramatically regulate the PC activities that consequently change the network dynamics of the cerebellar circuit.

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

“…As a result, GC oscillations are mediated by GoCs, consistent with other modeling work [ 19 ]. Furthermore, our findings of tuning GC oscillations by different formats of GoC inhibition suggest that downstream Purkinje cells, that receive inputs from GCs as the output of the granular layer, could show coherent oscillations [ 19 , 47 ]. Additional modulation sent out from Purkinje cells can further play a functional role in regulating synchronization of other brain areas [ 78 , 79 ].…”

“…Oscillations require precise synaptic integration to allow neurons to fire within a confined time window. However, the nonlinear characteristic of short-term plasticity (STP) disrupts precise synaptic integration, produces jitters to spike time, and depresses the synchrony of the network activity [47] (see S4 Fig) . Here we modeled the STP using a classic phenomenological model [40,41] (see Methods) and installed it on GoC-GC synapses showing both facilitation and depression with repeated incoming spikes (S5 Fig). Indeed, we found that network oscillations of GC responses were distorted by the STP of GoC-GC synapses in feedback inhibition networks (Fig 3).…”

“…Therefore, even for the same input, different combinations of excitation and inhibition can lead to a wide range of network oscillations. The cerebellum can maintain oscillation at different frequencies to complete different motor control, where both slow and fast GC oscillations have specific roles in cerebellar signal processing [17,47,54,55]. Additionally, such a combination plays a functional role in gating signals for information processing and activity selection for general network dynamics [56][57][58].…”

Dissecting cascade computational components in spiking neural networks

“…Nevertheless, the dynamics of the neural network is controlled by multiple layers and pathways [ 65 , 66 ]. In some neural systems, feedforward networks are more prominent.…”

Unraveling neural coding of dynamic natural visual scenes via convolutional recurrent neural networks

“…Temporal adaption is ubiquitous not only for the neural computation of sensory input, 43 but also for controlling and adjusting the dynamic range of single cells and neural populations in investigations of general neural dynamics. 70,71 Without limitation of the properties of visual stimulus, e.g., spatial invariation, our importance index, an important feature for modeling of the retinal encoding, used to evaluate the temporal filter enables us to incorporate neuronal adaption in response to complex and dynamic natural visual scenes. 72,73 Thus, our filter pruning approach could help reduce the effective number of parameters in other deep-learning models while processing the dynamic visual scenes, expanding their performance beyond that for static natural images.…”