Cerebellar Golgi cell models predict dendritic processing and mechanisms of synaptic plasticity

Abstract

The Golgi cells are the main inhibitory interneurons of the cerebellar granular layer. Although recent works have highlighted the complexity of their dendritic organization and synaptic inputs, the mechanisms through which these neurons integrate complex input patterns remained unknown. Here we have used 8 detailed morphological reconstructions to develop multicompartmental models of Golgi cells, in which Na, Ca, and K channels were distributed along dendrites, soma, axonal initial segment and axon. The models faithfully reproduced a rich pattern of electrophysiological and pharmacological properties and predicted the operating mechanisms of these neurons. Basal dendrites turned out to be more tightly electrically coupled to the axon initial segment than apical dendrites. During synaptic transmission, parallel fibers caused slow Ca-dependent depolarizations in apical dendrites that boosted the axon initial segment encoder and Na-spike backpropagation into basal dendrites, while inhibitory synapses effectively shunted backpropagating currents. This oriented dendritic processing set up a coincidence detector controlling voltage-dependent NMDA receptor unblock in basal dendrites, which, by regulating local calcium influx, may provide the basis for spike-timing dependent plasticity anticipated by theory.

Scaffold modelling captures the structure-function-dynamics relationship in brain microcircuits

“…Network simulations were carried out using detailed neuronal and synaptic models written in NEURON for GrC (Masoli et al, 2020b), GoC (Masoli et al, 2020a), PC, , SC and BC (Rizza et al, 2021). Local microcircuit responses to input patterns were tracked back to individual neurons and used to follow signal propagation with unprecedented resolution.…”

“…• GoC synapses (Masoli et al, 2020a). pf-GoC: AMPA; aa-GoC: AMPA and NMDA; mf -GoC: AMPA and NMDA; GoC-GoC: GABAalpha1, gap junctions (Szoboszlay et al, 2016;Vervaeke et al, 2012).…”

“…A neurotransmitter impulse was followed by a slow diffusion wave generating both a transient and a sustained component of the postsynaptic response, as observed experimentally. Parameters describing probability of release, diffusion, ionic receptor mechanisms, vesicle cycling parameters, time constant of recovery, electrical conduction were derived from original papers reporting GrC synapses (Diwakar et al, 2009;Masoli et al, 2020b), GoC synapses (Masoli et al, 2020a), GoC gap junctions (Szoboszlay et al, 2016), PC synapses , SC synapses (Rizza et al, 2021).…”

“…A random input at low frequency (4 Hz Poisson) on all mfs (Rancz et al, 2007) was used to simulate the cerebellar network in resting state in vivo. Since anatomical data about the connectivity of cerebellar neurons are incomplete, but their resting discharge frequency is known, we finetuned the number of connections against target parameter values of resting discharge in GrCs (Wilms and Häusser, 2015), GoCs, (Forti et al, 2006;Masoli et al, 2020a;Solinas et al, 2007), PCs (Arancillo et al, 2015), SCs and BCs (Barmack and Yakhnitsa, 2008;Jirenhed et al, 2013;Kim and Augustine, 2020).The turning point was to calibrate GoC-GoC inhibition, which influenced resting-state activity of the entire network. Since the synaptic conductance (~ 3200 pS) and the number of interconnected GoCs (about 15) are known (Hull and Regehr, 2012), we tuned the number of GoC-GoC synapses until basal discharge frequency was normalized.…”

“…With BSB, we have generated the first computational model of the cerebellar microcircuit including a layered volume with variegate neuron types characterized by different density, morphology and orientation, which were wired through a connectome defined by the anisotropy of dendritic and axonal processes through principled rules. The insertion of single cell multicompartmental models and dynamic synapses (Masoli et al, 2020a(Masoli et al, , 2020bRizza et al, 2021) was key to simulate network dynamics in response to naturalistic inputs (Ramakrishnan et al, 2016;Rancz et al, 2007;Roggeri et al, 2008). This work provides a new structural and functional ground-truth for the cerebellar circuit and implies the effectiveness of the BSB multi-scale modelling strategy to different brain areas.…”