Literature DB >> 33566820

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

Yuanhong Tang1, Lingling An1, Ye Yuan1, Qingqi Pei2, Quan Wang1, Jian K Liu3.   

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.

Entities:  

Mesh:

Year:  2021        PMID: 33566820      PMCID: PMC7909957          DOI: 10.1371/journal.pcbi.1008670

Source DB:  PubMed          Journal:  PLoS Comput Biol        ISSN: 1553-734X            Impact factor:   4.475


  84 in total

1.  How spike generation mechanisms determine the neuronal response to fluctuating inputs.

Authors:  Nicolas Fourcaud-Trocmé; David Hansel; Carl van Vreeswijk; Nicolas Brunel
Journal:  J Neurosci       Date:  2003-12-17       Impact factor: 6.167

2.  Synchronization of golgi and granule cell firing in a detailed network model of the cerebellar granule cell layer.

Authors:  R Maex; E De Schutter
Journal:  J Neurophysiol       Date:  1998-11       Impact factor: 2.714

3.  Synaptic depression and the temporal response characteristics of V1 cells.

Authors:  F S Chance; S B Nelson; L F Abbott
Journal:  J Neurosci       Date:  1998-06-15       Impact factor: 6.167

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Authors:  Volker Steuber; Wolfgang Mittmann; Freek E Hoebeek; R Angus Silver; Chris I De Zeeuw; Michael Häusser; Erik De Schutter
Journal:  Neuron       Date:  2007-04-05       Impact factor: 17.173

5.  Neuronal gain modulability is determined by dendritic morphology: A computational optogenetic study.

Authors:  Sarah Jarvis; Konstantin Nikolic; Simon R Schultz
Journal:  PLoS Comput Biol       Date:  2018-03-09       Impact factor: 4.475

6.  Phase changes in neuronal postsynaptic spiking due to short term plasticity.

Authors:  Mark D McDonnell; Bruce P Graham
Journal:  PLoS Comput Biol       Date:  2017-09-22       Impact factor: 4.475

7.  Stellate cell computational modeling predicts signal filtering in the molecular layer circuit of cerebellum.

Authors:  Martina Francesca Rizza; Francesca Locatelli; Stefano Masoli; Diana Sánchez-Ponce; Alberto Muñoz; Francesca Prestori; Egidio D'Angelo
Journal:  Sci Rep       Date:  2021-02-16       Impact factor: 4.379

Review 8.  The cerebellar Golgi cell and spatiotemporal organization of granular layer activity.

Authors:  Egidio D'Angelo; Sergio Solinas; Jonathan Mapelli; Daniela Gandolfi; Lisa Mapelli; Francesca Prestori
Journal:  Front Neural Circuits       Date:  2013-05-17       Impact factor: 3.492

9.  Network structure within the cerebellar input layer enables lossless sparse encoding.

Authors:  Guy Billings; Eugenio Piasini; Andrea Lőrincz; Zoltan Nusser; R Angus Silver
Journal:  Neuron       Date:  2014-08-07       Impact factor: 17.173

10.  Climbing fiber synapses rapidly and transiently inhibit neighboring Purkinje cells via ephaptic coupling.

Authors:  Kyung-Seok Han; Christopher H Chen; Mehak M Khan; Chong Guo; Wade G Regehr
Journal:  Nat Neurosci       Date:  2020-09-07       Impact factor: 24.884

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  1 in total

1.  Dissecting cascade computational components in spiking neural networks.

Authors:  Shanshan Jia; Dajun Xing; Zhaofei Yu; Jian K Liu
Journal:  PLoS Comput Biol       Date:  2021-11-29       Impact factor: 4.475

  1 in total

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