Computational modelling: moonlighting on the neuroscience and medicine

P. Hassanzadeh
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引用次数: 13

Abstract

Computational modelling has emerged as a powerful tool to study the behaviour of complex systems. Computer simulation may lead to a better understanding of the function of biological systems and the pathophysiological mechanisms underlying various diseases. In neuroscience, modelling techniques have provided knowledge about the electrical properties of neurons, activity of ion channels, synaptic function, information processing, and signalling pathways. Using simulations and analysis in network models has resulted in greater understanding of the behaviour of neural networks and dynamics of synaptic connectivity. Moreover, the correlation between the neurobiological mechanisms and a cluster of physiological, cognitive, and behavioural phenomena may be explored by the computational modelling of the neuronal systems. In this context, a significant progress has been made in understanding of the neural network architectures including those with a high degree of connectivity between the units, information processing, performance of complex cognitive tasks, integration of brain signals, as well as the dynamic mechanisms and computations implemented in the brain for making goal-directed choices. Computational models are able to explore the interactions between the brain areas which are involved in predictive processes and high-level skills. In this review, the significance of computational modelling in the study of neural networks, decision-making procedure, nerve growth factor signalling, and endocannabinoid system along with its medical applications have been highlighted. Biomedical Reviews 2013; 24: 25-31.
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计算建模:神经科学和医学的兼职
计算模型已经成为研究复杂系统行为的有力工具。计算机模拟可以更好地理解生物系统的功能和各种疾病的病理生理机制。在神经科学中,建模技术提供了关于神经元电特性、离子通道活动、突触功能、信息处理和信号通路的知识。在网络模型中使用模拟和分析导致了对神经网络行为和突触连接动力学的更好理解。此外,神经生物学机制与一系列生理、认知和行为现象之间的相关性可以通过神经元系统的计算建模来探索。在此背景下,对神经网络架构的理解取得了重大进展,包括单元之间的高度连接、信息处理、复杂认知任务的执行、大脑信号的整合,以及在大脑中实现目标导向选择的动态机制和计算。计算模型能够探索涉及预测过程和高级技能的大脑区域之间的相互作用。本文综述了计算模型在神经网络、决策过程、神经生长因子信号和内源性大麻素系统研究中的重要意义及其医学应用。生物医学评论2013;24: 25-31。
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