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摘要翻译：
理解像人脑一样的生物神经网络的复杂性是本世纪的科学挑战之一。大脑的组织可以被描述在不同的层次上，从小的神经网络到整个大脑区域。现有的描述功能或有效连通性的方法都是基于通过检测相关性或信息流来分析不同神经单元活动之间的关系。这是理解阿尔茨海默病等神经疾病及其致病因素的关键一步。为了评估这些估计方法，有必要参考一个已知连通性的神经网络，这是自然生物神经网络通常未知的。因此，网络模拟，也在硅，是可用的。本文建立了大规模神经网络的In silico仿真模型，研究了不同拓扑结构对神经元信号产生模式的影响。目标是用一个现实的大规模模型为神经计算算法开发标准的评估方法，以实现不同研究的基准和可比性。
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英文标题：
《Simulation of Large Scale Neural Networks for Evaluation Applications》
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作者：
Stefano De Blasi
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最新提交年份：
2018
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Neurons and Cognition 神经元与认知
分类描述：Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触，皮层，神经元动力学，神经网络，感觉运动控制，行为，注意
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一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
Understanding the complexity of biological neural networks like the human brain is one of the scientific challenges of our century. The organization of the brain can be described at different levels, ranging from small neural networks to entire brain regions. Existing methods for the description of functionally or effective connectivity are based on the analysis of relations between the activities of different neural units by detecting correlations or information flow. This is a crucial step in understanding neural disorders like Alzheimers disease and their causative factors. To evaluate these estimation methods, it is necessary to refer to a neural network with known connectivity, which is typically unknown for natural biological neural networks. Therefore, network simulations, also in silico, are available. In this work, the in silico simulation of large scale neural networks is established and the influence of different topologies on the generated patterns of neuronal signals is investigated. The goal is to develop standard evaluation methods for neurocomputational algorithms with a realistic large scale model to enable benchmarking and comparability of different studies.
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PDF链接：
https://arxiv.org/pdf/1805.08626
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