Extension of NEURON simulator for simulation of reaction-diffusion in neurons

模拟生物工程技术在医学神经科学中的发展一直受到限制 计算神经科学的重点是人类的更高能力，例如玩游戏的能力 不下就下。这导致了这样一种观念，即通过求助于 计算机科学的概念：人工神经网络、信息论、贝叶斯推理、 模式识别等。这些工具是否足以理解人类 新皮质功能，它们显然不足以理解神经系统 在神经学和精神病学病理中出现的功能障碍。大脑疾病，就像其他疾病一样 器官系统，是生物组织的疾病，最终必须考虑能量学和 氧合、血压和大脑压力，以及毒素和毒素的化学反应和扩散 制药公司。这种全器官模拟将需要与其他模拟器连接。通过这样的方式 联系，以及通过内部扩展，我们一直在扩展广泛使用的神经元 处理神经组织中反应扩散的模拟器，以更好地了解大脑 信号、神经退行性毒性级联反应和药物效应。在本资助期内，我们 将通过4个目标进一步增强神经元的NRxD模块：1.改进突触建模技术 通过将突触前体积、突触间隙和突触后体积一起考虑来处理亲离子 突触、代谢性突触、缝隙连接和复杂的组合突触；以及 重摄取、扩散、电扩散、神经递质、第二信使和逆行 以协调的方式传递神经递质。2.允许更快速、更广泛地勘探 通过提高模拟速度和允许完全保存状态变量 改进了模拟初始化和从高性能计算故障中恢复。3. 开发基于Python和基于套接字的应用程序编程接口(API)以简化开发 与其他模拟器的链接，包括用于电扩散和各种类型的随机 模拟。4.继续进行包裹传播和教育，以获得更多的计算性和 NRxD试验性用户。我们将继续提供一年两次的教程，并赞助 在计算神经科学和其他会议上的其他研讨会。我们将在线整合 带有文档的教程，以提供程序员参考和生物参考 在线手册。我们将开发一套与这些相关的视频教程，最终将 链接在一起，形成一个在线课程。总体而言，我们预计我们的新模拟 无论是在研究方面，还是在未来，神经技术都将得到越来越多的使用和利用 个性化医疗的临床应用。

Development of simulation bioengineering techniques in medical neuroscience has been limited due to computational neuroscience's focus on the higher capacities of humans, such as the ability to play chess or go. That has led to the notion that the brain can best be understood by recourse to the concepts of computer science: artificial neural networks, information theory, Bayesian inference, pattern recognition, etc. Whether or not these tools are sufficient for understanding human neocortical function, they are clearly not sufficient for understanding the nervous system dysfunction seen in neurological and psychiatric pathology. Brain disease, like diseases of other organ systems, is disease of biological tissue, and must eventually consider energetics and oxygenation, blood and brain pressures, as well as chemical reactions and diffusion of toxins and pharmaceuticals. Such full-organ simulation will require linkages to other simulators. Through such linkages, as well as through internal extensions, we have been extending the widely-used NEURON simulator to handle reaction-diffusion in neural tissue in order to better understand brain signaling, neurodegenerative toxic cascades, and drug effects. For the current funding period we will further augment NEURON's NRxD module through 4 Aims: 1. Improve synaptic modeling techniques by considering presynaptic volume, cleft and postsynaptic volume together to handle ionotropic synapses, metabotropic synapses, gap junctions and complex combination synapses; along with reuptake, diffusion, electrodiffusion, neurotransmitters, second messengers and retrograde neurotransmitters in a coordinated way. 2. Allow more rapid and more extensive exploration of parameter space by improving simulation speed and by allowing full state variable saving for improved simulation initialization and recovery from high-performance computing failures. 3. Develop both Python-based and socket-based application programming interfaces (APIs) for easier linkage with other simulators, including for electrodiffusion and for various types of stochastic simulation. 4. Continue package dissemination and education in order to get more computational and experimental NRxD users. We will continue to offer twice-yearly tutorials, and to sponsor additional workshops at Computational Neuroscience and at other meetings. We will integrate online tutorials with the documentation to provide both Programmer's Reference and Biological Reference online manuals. We will develop a set of video tutorials associated with these that will eventually be linked together to make an online course. Overall, we expect that our novel simulation neurotechnology will be of increasing use and utilization both for research use, and for future clinical adoption in personalized medicine.