Multiscale modeling of neuronal dynamics in hippocampus CA1

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Federico Tesler, Roberta Maria Lorenzi, Adam Ponzi, Claudia Casellato, Fulvia Palesi, Daniela Gandolfi, Claudia A. M. Gandini Wheeler Kingshott, Jonathan Mapelli, Egidio D'Angelo, Michele Migliore, Alain Destexhe
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引用次数: 0

Abstract

The development of biologically realistic models of brain microcircuits and regions constitutes currently a very relevant topic in computational neuroscience. One of the main challenges of such 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. In this paper we introduce a multiscale modeling 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 modeling 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. 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. Then, we analyze the implementation of synaptic plasticity within our framework, a key aspect to study the role of hippocampus in learning and memory consolidation, and we demonstrate the capability of our framework to incorporate the variations at synaptic level. Finally, we present an example of the implementation of our model to study a stimulus propagation at the macro-scale level, and we show that the results of our framework can capture the dynamics obtained in the corresponding spiking network model of the whole CA1 area.
海马 CA1 神经元动态的多尺度建模
大脑微电路和脑区生物现实模型的开发是当前计算神经科学领域一个非常重要的课题。此类模型面临的主要挑战之一是如何在不同尺度之间穿行,从微观尺度(细胞)到中观尺度(微电路)和宏观尺度(区域或全脑水平),同时还要保持对计算资源需求的限制。本文介绍了海马 CA1 的多尺度建模框架,海马 CA1 是大脑的一个区域,在学习、记忆巩固和导航等功能中发挥着关键作用。我们的建模框架从单细胞水平到宏观尺度,并利用本文介绍的 CA1 的新型均场模型来弥合微观和宏观尺度之间的差距。我们通过分析系统对海马体中观察到的主要大脑节律的响应,并将结果与 CA1 的相应尖峰网络模型进行比较,来测试和验证该模型。然后,我们分析了突触可塑性在我们的框架中的实现,这是研究海马在学习和记忆巩固中的作用的一个关键方面。最后,我们举例说明了如何利用我们的模型来研究刺激在宏观尺度上的传播,结果表明我们的框架可以捕捉到整个 CA1 区域相应的尖峰网络模型所获得的动态变化。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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