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Computational modeling of multisensory processing using network of spiking neurons.

机译:使用尖峰神经元网络的多传感器处理的计算模型。

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摘要

Multisensory processing in the brain underlies a wide variety of perceptual phenomena, but little is known about the underlying mechanisms of how multisensory neurons are generated and how the neurons integrate sensory information from environmental events. This lack of knowledge is due to the difficulty of biological experiments to manipulate and test the characteristics of multisensory processing. By using a computational model of multisensory processing this research seeks to provide insight into the mechanisms of multisensory processing. From a computational perspective, modeling of brain functions involves not only the computational model itself but also the conceptual definition of the brain functions, the analysis of correspondence between the model and the brain, and the generation of new biologically plausible insights and hypotheses. In this research, the multisensory processing is conceptually defined as the effect of multisensory convergence on the generation of multisensory neurons and their integrated response products, i.e., multisensory integration. Thus, the computational model is the implementation of the multisensory convergence and the simulation of the neural processing acting upon the convergence. Next, the most important step in the modeling is analysis of how well the model represents the target, i.e., brain function. It is also related to validation of the model. One of the intuitive and powerful ways of validating the model is to apply methods standard to neuroscience for analyzing the results obtained from the model. In addition, methods such as statistical and graph-theoretical analyses are used to confirm the similarity between the model and the brain. This research takes both approaches to provide analyses from many different perspectives. Finally, the model and its simulations provide insight into multisensory processing, generating plausible hypotheses, which will need to be confirmed by real experimentation.
机译:大脑中的多感觉处理是各种各样的感知现象的基础,但是对于如何产生多感觉神经元以及神经元如何整合来自环境事件的感觉信息的潜在机制知之甚少。缺乏知识是由于生物学实验难以操纵和测试多感觉处理的特征。通过使用多感觉处理的计算模型,本研究旨在提供对多感觉处理机制的见解。从计算的角度来看,脑功能的建模不仅涉及计算模型本身,还涉及脑功能的概念定义,模型与大脑之间的对应关系分析以及新的生物学上合理的见解和假设的产生。在这项研究中,多感觉处理在概念上被定义为多感觉收敛对多感觉神经元及其集成响应产物(即多感觉集成)的生成的影响。因此,该计算模型是多传感器收敛的实现以及作用于该收敛的神经处理的仿真。接下来,建模中最重要的步骤是分析模型代表目标(即大脑功能)的程度。它也与模型的验证有关。验证模型的直观而强大的方法之一是将标准方法应用于神经科学,以分析从模型获得的结果。此外,还使用诸如统计分析和图论分析之类的方法来确认模型与大脑之间的相似性。这项研究采用两种方法从许多不同的角度进行分析。最后,该模型及其仿真提供了对多感觉处理的洞察力,从而产生了合理的假设,这需要通过实际实验加以确认。

著录项

  • 作者

    Lim, Hun Ki.;

  • 作者单位

    Virginia Commonwealth University.;

  • 授予单位 Virginia Commonwealth University.;
  • 学科 Biology Neuroscience.;Computer Science.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 121 p.
  • 总页数 121
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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