Complex dynamics in simplified neuronal models: reproducing Golgi cell electroresponsiveness

Geminiani, Alice; Casellato, Claudia; Locatelli, Francesca; Prestori, Francesca; Pedrocchi, Alessandra; D’Angelo, Egidio

doi:10.1101/378315

Cited by 7 publications

(11 citation statements)

References 60 publications

(112 reference statements)

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“…We propose here an extended GLIF (E-GLIF) model, which achieves a sound compromise between model complexity, biological plausibility and computational efficiency (Figure 1) (Geminiani et al, 2018b). The model has only three state variables, the membrane potential and two currents, which can be associated to main biophysical subcellular mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness

Geminiani

Casellato

Locatelli

et al. 2018

Front. Neuroinform.

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Brain neurons exhibit complex electroresponsive properties – including intrinsic subthreshold oscillations and pacemaking, resonance and phase-reset – which are thought to play a critical role in controlling neural network dynamics. Although these properties emerge from detailed representations of molecular-level mechanisms in “realistic” models, they cannot usually be generated by simplified neuronal models (although these may show spike-frequency adaptation and bursting). We report here that this whole set of properties can be generated by the extended generalized leaky integrate-and-fire (E-GLIF) neuron model. E-GLIF derives from the GLIF model family and is therefore mono-compartmental, keeps the limited computational load typical of a linear low-dimensional system, admits analytical solutions and can be tuned through gradient-descent algorithms. Importantly, E-GLIF is designed to maintain a correspondence between model parameters and neuronal membrane mechanisms through a minimum set of equations. In order to test its potential, E-GLIF was used to model a specific neuron showing rich and complex electroresponsiveness, the cerebellar Golgi cell, and was validated against experimental electrophysiological data recorded from Golgi cells in acute cerebellar slices. During simulations, E-GLIF was activated by stimulus patterns, including current steps and synaptic inputs, identical to those used for the experiments. The results demonstrate that E-GLIF can reproduce the whole set of complex neuronal dynamics typical of these neurons – including intensity-frequency curves, spike-frequency adaptation, post-inhibitory rebound bursting, spontaneous subthreshold oscillations, resonance, and phase-reset – providing a new effective tool to investigate brain dynamics in large-scale simulations.

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Section: Introductionmentioning

confidence: 99%

Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness

Geminiani

Casellato

Locatelli

et al. 2018

Front. Neuroinform.

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“…Based on our results, the AdEx model has shown to be a computationally light approach for the close reproduction of the firing patterns reported from cerebellar GrCs. Recent articles in the literature have proposed modified GLIF point-neuron equations (the so-called Extended Generalized Leaky Integrate-and-Fire model) for the reproduction of experimental traces recorded in different cerebellar cells (Geminiani et al, 2018 ; Casali et al, 2019 ). That model allowed the direct application of some experimentally testable parameters together with other optimized ones.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the optimization process is fast, versatile, and able to capture relevant firing features. Contrary to the methodology proposed in Geminiani et al ( 2018 ) where the optimization algorithm fitted the recorded voltage traces, our approach aims to reproduce the firing characteristics (namely, the burst frequency, the firing rate, and the first-spike latency) of the biological neuron.…”

Section: Discussionmentioning

confidence: 99%

Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell

Marín

Sáez-Lara

Ros

et al. 2020

Front. Cell. Neurosci.

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Biologically relevant large-scale computational models currently represent one of the main methods in neuroscience for studying information processing primitives of brain areas. However, biologically realistic neuron models tend to be computationally heavy and thus prevent these models from being part of brain-area models including thousands or even millions of neurons. The cerebellar input layer represents a canonical example of large scale networks. In particular, the cerebellar granule cells, the most numerous cells in the whole mammalian brain, have been proposed as playing a pivotal role in the creation of somato-sensorial information representations. Enhanced burst frequency (spiking resonance) in the granule cells has been proposed as facilitating the input signal transmission at the theta-frequency band (4–12 Hz), but the functional role of this cell feature in the operation of the granular layer remains largely unclear. This study aims to develop a methodological pipeline for creating neuron models that maintain biological realism and computational efficiency whilst capturing essential aspects of single-neuron processing. Therefore, we selected a light computational neuron model template (the adaptive-exponential integrate-and-fire model), whose parameters were progressively refined using an automatic parameter tuning with evolutionary algorithms (EAs). The resulting point-neuron models are suitable for reproducing the main firing properties of a realistic granule cell from electrophysiological measurements, including the spiking resonance at the theta-frequency band, repetitive firing according to a specified intensity-frequency (I-F) curve and delayed firing under current-pulse stimulation. Interestingly, the proposed model also reproduced some other emergent properties (namely, silent at rest, rheobase and negligible adaptation under depolarizing currents) even though these properties were not set in the EA as a target in the fitness function (FF), proving that these features are compatible even in computationally simple models. The proposed methodology represents a valuable tool for adjusting AdEx models according to a FF defined in the spiking regime and based on biological data. These models are appropriate for future research of the functional implication of bursting resonance at the theta band in large-scale granular layer network models.

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“…others (E-GLIF) embedding non-linear firing properties (e.g. (Geminiani et al, 2018)) and 560 accounting for synaptic dendritic location by modifying the transmission weight depending on the 561 distance of synapses from the soma (Rössert et al, 2016). 562…”

Section: The Impact Of Molecular Layer Interneurons On Pc Activation mentioning

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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Complex dynamics in simplified neuronal models: reproducing Golgi cell electroresponsiveness

Cited by 7 publications

References 60 publications

Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness

Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness

Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell

Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network

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