A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits

Gandolfi, Daniela; Mapelli, Jonathan; Solinas, Sergio; Schepper, Robin De; Geminiani, Alice; Casellato, Claudia; D’Angelo, Egidio; Migliore, Michele

doi:10.1038/s41598-022-18024-y

Cited by 7 publications

(22 citation statements)

References 76 publications

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“…We have assumed that the wide range of combinations in the mf-GrC-GoC circuit is actually limited to a restricted range (two to three mfs and two to three GoCs), leading to the simulation of just a small number of circuit architectures. We plan to further extend the functional analysis of these circuits to a more inclusive range of configurations, which could also include the pharmacological exploration of conditions that are otherwise not testable, allowing for the instantiation of a reliable benchmark for advanced translational investigations [ 71 , 72 ].…”

Section: Discussionmentioning

confidence: 99%

Long-Term Synaptic Plasticity Tunes the Gain of Information Channels through the Cerebellum Granular Layer

et al. 2022

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A central hypothesis on brain functioning is that long-term potentiation (LTP) and depression (LTD) regulate the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granule cells have been shown to control the gain of signals transmitted through the mossy fiber pathway by exploiting synaptic inhibition in the glomeruli. However, the way LTP and LTD control signal transformation at the single-cell level in the space, time and frequency domains remains unclear. Here, the impact of LTP and LTD on incoming activity patterns was analyzed by combining patch-clamp recordings in acute cerebellar slices and mathematical modeling. LTP reduced the delay, increased the gain and broadened the frequency bandwidth of mossy fiber burst transmission, while LTD caused opposite changes. These properties, by exploiting NMDA subthreshold integration, emerged from microscopic changes in spike generation in individual granule cells such that LTP anticipated the emission of spikes and increased their number and precision, while LTD sorted the opposite effects. Thus, akin with the expansion recoding process theoretically attributed to the cerebellum granular layer, LTP and LTD could implement selective filtering lines channeling information toward the molecular and Purkinje cell layers for further processing.

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Section: Discussionmentioning

confidence: 99%

Long-Term Synaptic Plasticity Tunes the Gain of Information Channels through the Cerebellum Granular Layer

et al. 2022

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“…In this section we will present the results of simulations of a network representing a slice of hippocampal CA1 area. To this end we will adopt a recently developed method to incorporate realistic morpho-anatomical connectivities based on the geometrical probability volumes associated with pre- and postsynaptic neurites [2]. The method has been benchmarked for the mouse hippocampus CA1 area, and the results show that this approach is able to generate full-scale brain networks that are in good agreement with experimental findings.…”

Section: Resultsmentioning

confidence: 99%

“…To develop the mean-field of the CA1 microcircuit we make use of a recently developed formalism that follows a bottom-up approach starting from the single-cell level, which allows to build a mean-field model that incorporates different cell types with specific intrinsic firing properties, and their synaptic interactions mediated by different receptor types [18][19][20]. In addition we develop a macroscale simulation of CA1 using the mean-field models as building blocks and incorporating extended specific connectivity structure based on a recently developed data-driven method [2] (see Fig. 1 for a diagram of the multiscale framework).…”

Section: Introductionmentioning

confidence: 99%

“…The image of CA1 is adapted from Ref. [2]. The color code represent different neurons in 4 layers of CA1: red: Superficial Pyramidal Cells (SP); yellow: Deep Pyramidal Cells (SO); blue: Stratum Oriens Inhibitory neurons (SO); green: Stratum Pyramidalis Inhibitory neurons (SP); black: Stratum Radiatum Inhibitory neurons (SR); magenta: Stratum Lacunosum Inhibitory neurons (SLM).…”

Section: Introductionmentioning

confidence: 99%

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Multiscale modelling of neuronal dynamics in hippocampus CA1

Tesler,

Lorenzi,

Ponzi

et al. 2024

Preprint

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The development of biologically realistic models of brain microcircuits and regions is currently a very relevant topic in computational neuroscience. From basic research to clinical applications, there is an increasing demand for accurate models that incorporate local cellular and network specificities, able to capture a broad range of dynamics and functions associated with given brain regions. One of the main challenges of these models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. One novel approach to this problem is the use of mean-field models of neuronal activity to build large-scale simulations. This provides an effective solution to the passage between scales with relatively low computational demands, which is achieved by a drastic reduction in the dimensionality of the system. In this paper we introduce a multiscale modelling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modelling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. To develop the mean-field model we make use of a recently introduced formalism based on a bottom-up approach that is easily applicable to different neuronal models and cell types. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. In addition, we show an example of the implementation of our model to study a stimulus propagation at the macro-scale, and we compare the results obtained from our model with the corresponding spiking network model of the whole CA1 area.

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“…An interesting work addressing the importance of realistic modelling of neurons placement is [ 29 ]. This work considers the hippocampus.…”

Section: Resultsmentioning

confidence: 99%

Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System

et al. 2022

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The reproduction of the brain ’sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neurons are linked together to build a network, in order to reproduce complex behaviors. In the literature, different network-building rules and models have been described, targeting realistic distributions and connections of the neurons. In particular, the Granular layEr Simulator (GES) performs the granular layer network reconstruction considering biologically realistic rules to connect the neurons. Moreover, it simulates the network considering the Hodgkin–Huxley model. The work proposed in this paper adopts the network reconstruction model of GES and proposes a simulation module based on Leaky Integrate and Fire (LIF) model. This simulator targets the reproduction of the activity of large scale networks, exploiting the GPU technology to reduce the processing times. Experimental results show that a multi-GPU system reduces the simulation of a network with more than 1.8 million neurons from approximately 54 to 13 h.

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A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits

Cited by 7 publications

References 76 publications

Long-Term Synaptic Plasticity Tunes the Gain of Information Channels through the Cerebellum Granular Layer

Long-Term Synaptic Plasticity Tunes the Gain of Information Channels through the Cerebellum Granular Layer

Multiscale modelling of neuronal dynamics in hippocampus CA1

Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System

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