Action potential processing in a detailed Purkinje cell model reveals a critical role for axonal compartmentalization

Masoli, Stefano; Solinas, Sergio; D’Angelo, Egidio

doi:10.3389/fncel.2015.00047

Cited by 68 publications

(138 citation statements)

References 107 publications

(246 reference statements)

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“…One of the major features of this part of the brain is represented by the closed-loop circuit formed by (i) the Purkinje cells (PC) of the cerebellar cortex, (ii) the deep cerebellar nuclei (DCN) and (iii) the inferior olive (IO) (Hendelman & Marshall, 1980). Each of these neuronal populations is able to fire rhythmically independently from the respective synaptic inputs (Raman et al, 2000;Swensen & Bean, 2003;Masoli et al, 2015;Buchin et al, 2016). Specifically, the IO is characterised by subthreshold oscillations (5-10 Hz) sustained by electrical coupling (Llinas et al, 1974;Long et al, 2002;Leznik & Llin as, 2005).…”

Section: Introductionmentioning

confidence: 99%

Beta‐gamma burst stimulations of the inferior olive induce high‐frequency oscillations in the deep cerebellar nuclei

Cheron

Chéron

2018

Eur J of Neuroscience

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The cerebellum displays various sorts of rhythmic activities covering both low- and high-frequency oscillations. These cerebellar high-frequency oscillations were observed in the cerebellar cortex. Here, we hypothesised that not only is the cerebellar cortex a generator of high-frequency oscillations but also that the deep cerebellar nuclei may also play a similar role. Thus, we analysed local field potentials and single-unit activities in the deep cerebellar nuclei before, during and after electric stimulation in the inferior olive of awake mice. A high-frequency oscillation of 350 Hz triggered by the stimulation of the inferior olive, within the beta-gamma range, was observed in the deep cerebellar nuclei. The amplitude and frequency of the oscillation were independent of the frequency of stimulation. This oscillation emerged during the period of stimulation and persisted after the end of the stimulation. The oscillation coincided with the inhibition of deep cerebellar neurons. As the inhibition of the deep cerebellar nuclei is related to inhibitory inputs from Purkinje cells, we speculate that the oscillation represents the unmasking of the synchronous activation of another subtype of deep cerebellar neuronal subtype, devoid of GABA receptors and under the direct control of the climbing fibres from the inferior olive. Still, the mechanism sustaining this oscillation remains to be deciphered. Our study sheds new light on the role of the olivo-cerebellar loop as the final output control of the intercerebellar circuitry.

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Section: Introductionmentioning

confidence: 99%

Beta‐gamma burst stimulations of the inferior olive induce high‐frequency oscillations in the deep cerebellar nuclei

Cheron

Chéron

2018

Eur J of Neuroscience

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“…The PCs were distributed in a single sub-layer forming an almost planar grid between the 154 granular and molecular layers. The PC inter-soma distances over this plane were constrained by the 155 dendritic trees, which are flat and expand vertically on the parasagittal plane (about 150 µm radius x 156 30 µm width) without overlapping (Masoli et al, 2015). Since PC somata do not arrange in parallel 157 arrays but are somehow scattered, a noisy offset was introduced creating an average angular shift of 158 about 5° between adjacent PCs.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network

Casali

Marenzi

Medini

et al. 2019

Preprint

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16Reconstructing neuronal microcircuits through computational models is fundamental to 17 simulate local neuronal dynamics. Here a scaffold model of the cerebellum has been developed in 18 order to flexibly place neurons in space, connect them synaptically and endow neurons and synapses 19with biologically-grounded mechanisms. The scaffold model can keep neuronal morphology 20 separated from network connectivity, which can in turn be obtained from convergence/divergence 21 ratios and axonal/dendritic field 3D geometries. We first tested the scaffold on the cerebellar 22 microcircuit, which presents a challenging 3D organization, at the same time providing appropriate 23 datasets to validate emerging network behaviors. The scaffold was designed to integrate the 24 cerebellar cortex with deep cerebellar nuclei (DCN), including different neuronal types: Golgi cells, 25 granule cells, Purkinje cells, stellate cells, basket cells and DCN principal cells. Mossy fiber (mf) 26 inputs were conveyed through the glomeruli. An anisotropic volume (0.077 mm 3 ) of mouse 27 cerebellum was reconstructed, in which point-neuron models were tuned toward the specific 28 discharge properties of neurons and were connected by exponentially decaying excitatory and 29 inhibitory synapses. Simulations using pyNEST and pyNEURON showed the emergence of 30 organized spatio-temporal patterns of neuronal activity similar to those revealed experimentally in 31 response to background noise and burst stimulation of mossy fiber bundles. Different configurations 32 of granular and molecular layer connectivity consistently modified neuronal activation patterns, 33revealing the importance of structural constraints for cerebellar network functioning. The scaffold 34 provided thus an effective workflow accounting for the complex architecture of the cerebellar 35 network. In principle, the scaffold can incorporate cellular mechanisms at multiple levels of detail 36and be tuned to test different structural and functional hypotheses. A future implementation using 37 detailed 3D multi-compartment neuron models and dynamic synapses will be needed to investigate 38 the impact of single neuron properties on network computation. 39 40 41 Cerebellar scaffold model 2 This is a provisional file, not the final typeset article 42

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“…The model has a minimum number of state variables, the membrane potential and two currents, which can be associated to main biophysical subcellular mechanisms. Thanks to its mathematical structure, which is similar to GLIF and analytically solvable, E-GLIF can be optimized by traditional optimization algorithms (Pozzorini et al, 2015; Teeter et al, 2018) avoiding metaheuristic methods, like Genetic Algorithms, used for multi-compartment realistic neurons with high-dimensional parameter search space (Masoli et al, 2015). E-GLIF can reproduce a rich variety of electroresponsive properties: autorhythmicity, depolarization-induced bursting and post-inhibitory rebound bursting, specific input-output ( f-I stim ) relationships, spike-frequency adaptation, phase-reset, sub-threshold oscillations and resonance.…”

Section: Introductionmentioning

confidence: 99%

Complex dynamics in simplified neuronal models: reproducing Golgi cell electroresponsiveness

Geminiani¹,

Casellato²,

Locatelli³

et al. 2018

Preprint

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11Brain neurons exhibit complex electroresponsive properties -including intrinsic subthreshold 12 oscillations and pacemaking, resonance and phase-reset -which are thought to play a critical role 13 in controlling neural network dynamics. Although these properties emerge from detailed 14representations of molecular-level mechanisms in "realistic" models, they cannot usually be 15 generated by simplified neuronal models (although these may show spike-frequency adaptation 16 and bursting). We report here that this whole set of properties can be generated by the extended 17 generalized leaky integrate-and-fire (E-GLIF) neuron model. E-GLIF derives from the GLIF 18 model family and is therefore mono-compartmental, keeps the limited computational load typical 19 of a linear low-dimensional system, admits analytical solutions and can be tuned through gradient-20 descent algorithms. Importantly, E-GLIF is designed to maintain a correspondence between model 21 parameters and neuronal membrane mechanisms through a minimum set of equations. In order to 22 test its potential, E-GLIF was used to model a specific neuron showing rich and complex 23 electroresponsiveness, the cerebellar Golgi cell, and was validated against experimental 24 electrophysiological data recorded from Golgi cells in acute cerebellar slices. During simulations, 25 E-GLIF was activated by stimulus patterns, including current steps and synaptic inputs, identical 26 to those used for the experiments. The results demonstrate that E-GLIF can reproduce the whole 27 set of complex neuronal dynamics typical of these neurons -including intensity-frequency curves, 28 spike-frequency adaptation, depolarization-induced and post-inhibitory rebound bursting, 29 spontaneous subthreshold oscillations, resonance and phase-reset, -providing a new effective tool 30to investigate brain dynamics in large-scale simulations. 31Word count (body of the text): 8047. Figures: 9. Tables: 5 . 32 33 65 Gerstner, 2005). However, the nonlinearity entailed more difficulties in optimizing model 66 parameters and in computational efficiency. Therefore, recently, new linear adaptive point models 67have been developed (Generalized LIF, GLIF), with spike-triggered currents and moving threshold 68 as the source of adaptation (Mihalaş and Niebur, 2009) and with stochastic processes in firing 69 emission (Pozzorini et al., 2015; Rössert et al., 2016). The possibility to use a linear and 70 analytically solvable neuron model is fundamental when simulating large-scale SNNs, since 71 computational efficiency can be enhanced without severe loss in spike time accuracy and realism 72 (Hanuschkin et al., 2010). However, GLIF can hardly generate phenomena like subthreshold 73 oscillations, resonance and phase-reset, which are critical for large-scale network entrainment and 74 communication (Buzsáki, 2004; Buzsáki and Draghun, 2004). 75We propose here an extended GLIF (E-GLIF) model, which achieves a sound compromise 76 between model complexity, biological plausibility and computational efficienc...

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Action potential processing in a detailed Purkinje cell model reveals a critical role for axonal compartmentalization

Cited by 68 publications

References 107 publications

Beta‐gamma burst stimulations of the inferior olive induce high‐frequency oscillations in the deep cerebellar nuclei

Beta‐gamma burst stimulations of the inferior olive induce high‐frequency oscillations in the deep cerebellar nuclei

Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network

Complex dynamics in simplified neuronal models: reproducing Golgi cell electroresponsiveness

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