Computational models of neurotransmission at cerebellar synapses unveil the impact on network computation

Masoli, Stefano; Rizza, Martina Francesca; Tognolina, Marialuisa; Prestori, Francesca; D’Angelo, Egidio

doi:10.3389/fncom.2022.1006989

Cited by 5 publications

(2 citation statements)

References 270 publications

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“…The membrane mechanisms for both AMPA and GABA receptors were the same as in 28 with a peak synaptic conductance of 2600pS each. The synapses contain a presynaptic vesicle cycle and neurotransmitter release mechanism, a 2D diffusion process and Markov chain molecular models to simulated the postsynaptic receptor 64 .…”

Section: Methodsmentioning

confidence: 99%

Human outperform mouse Purkinje cells in dendritic complexity and computational capacity

Masoli

Sánchez-Ponce

Vrieler

et al. 2023

Preprint

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Purkinje cells (PC) of the cerebellum are amongst the largest neurons of the brain and have been extensively investigated in rodents. However, their morphological and physiological properties in humans are still poorly understood. Here, we have taken advantage of high-resolution morphological reconstructions and of unique electrophysiological recordings of human PCsex vivoto generate computational models and estimate computational capacity. An inter-species comparison showed that human PCs had similar fractal structure but were bigger than mouse PCs. Consequently, given a similar spine density (2/μm), human PCs hosted about 5 times more dendritic spines. Moreover, human had higher dendritic complexity than mouse PCs and usually emitted 2-3 main dendritic trunks instead than 1. Intrinsic electroresponsiveness was similar in the two species but model simulations revealed that the dendrites generated ~6.5 times (n=51 vs. n=8) more combinations of independent input patterns in human than mouse PCs leading to an exponential 2nincrease in Shannon information. Thus, while during evolution human PCs maintained similar patterns of spike discharge as in rodents, they developed more complex dendrites enhancing computational capacity up to the limit of 10 billion times.

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Section: Methodsmentioning

confidence: 99%

Human outperform mouse Purkinje cells in dendritic complexity and computational capacity

Masoli

Sánchez-Ponce

Vrieler

et al. 2023

Preprint

Self Cite

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“…Although they have not been purposely implemented for mimicking in vitro dynamics, they have been successfully exploited, e.g., to study neuronal network modulation and delineate potential mechanisms underlying activity patterns [22]. Traditionally, these models include the mathematical descriptions of the single-neuron activity, ranging from simple phenomenological characterization of neuronal spiking [23] to more complex, conductance-based simulations of ion fluxes between the intra and the extracellular space [24], as well as of the cell-cell connections to resemble the neuronal network architecture [25]. Some studies also incorporate more sophisticated models, e.g., including astrocytes via tripartite synapses [26].…”

Section: Introductionmentioning

confidence: 99%

Digitoids: a novel computational platform for mimicking oxygen-dependent firing dynamics inin vitroneuronal networks

Fabbri,

Ahluwalia,

Magliaro

2023

Preprint

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In vitromodels of neural tissues are crucial to gain new insights on the pathophysiology of the brain. However, cell cultures are associated with many drawbacks and difficulties, e.g., technical complexities, ethical problems and high cost. Computational model-based solutions could represent an important tool to support the study of neuron function. In this work we present a novel computational platform where digital neuronal networks, i.e., Digitoids, can be developed with different size and layouts. The Digitoids rely on a novel firing model where the dependence on oxygen concentration is introduced, since it is a crucial limiting factor in cell cultures. To validate the performance of the platform, Digitoids were developed with the same morphological arrangement as observed in neuron monolayersin vitro. The comparison between the functional output of the Digitoids and the experimental data are not statistically different. The platform delivers a flexible digital tool that can easily be adapted for mimickingin vitromodels with increasing complexity and can be exploited to optimize the laboratory experiments involving neuron cultures.Author SummaryThe use of cell models within laboratories is crucial to gain new understandings of the functioning of neuronal assemblies. However, culturing cells requires highly skilled personnel, a huge quantity of disposable materials and is thus associated with elevated costs. To overcome these limitations, the use of computer-based systems that reproduce the electrical behaviour of neurons can be employed. Sincein vitromodels are vessel-free, we present a novel computational model of neuron electrophysiology, where we introduce the dependence on local oxygen concentration Indeed, oxygen affects the metabolism and the function of cultured neurons. Thanks to our model and platform, we are able to reproduce the morphology of the cultured networks of neurons and build the so-calledDigitoids. We then simulate theDigitoidsand obtain their electrophysiological output. We compared theDigitoids’output with experimental data from neurons cultured on microelectrode arrays. We did not find any differences between the electrophysiological output of theDigitoidsand the experimental data, therefore we conclude that our novel oxygen-dependent model can be used to develop more physiologically relevant tools for simulating the activity of cultured neurons.

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Human Purkinje cells outperform mouse Purkinje cells in dendritic complexity and computational capacity

Masoli,

Sanchez-Ponce,

Vrieler

et al. 2024

Commun Biol

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View full text Add to dashboard Cite

Purkinje cells in the cerebellum are among the largest neurons in the brain and have been extensively investigated in rodents. However, their morphological and physiological properties remain poorly understood in humans. In this study, we utilized high-resolution morphological reconstructions and unique electrophysiological recordings of human Purkinje cells ex vivo to generate computational models and estimate computational capacity. An inter-species comparison showed that human Purkinje cell had similar fractal structures but were larger than those of mouse Purkinje cells. Consequently, given a similar spine density (2/μm), human Purkinje cell hosted approximately 7.5 times more dendritic spines than those of mice. Moreover, human Purkinje cells had a higher dendritic complexity than mouse Purkinje cells and usually emitted 2–3 main dendritic trunks instead of one. Intrinsic electro-responsiveness was similar between the two species, but model simulations revealed that the dendrites could process ~6.5 times (n = 51 vs. n = 8) more input patterns in human Purkinje cells than in mouse Purkinje cells. Thus, while human Purkinje cells maintained spike discharge properties similar to those of rodents during evolution, they developed more complex dendrites, enhancing computational capacity.

show abstract

Computational models of neurotransmission at cerebellar synapses unveil the impact on network computation

Cited by 5 publications

References 270 publications

Human outperform mouse Purkinje cells in dendritic complexity and computational capacity

Human outperform mouse Purkinje cells in dendritic complexity and computational capacity

Digitoids: a novel computational platform for mimicking oxygen-dependent firing dynamics inin vitroneuronal networks

Human Purkinje cells outperform mouse Purkinje cells in dendritic complexity and computational capacity

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