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A New Nonlinear Sparse Component Analysis for a Biologically Plausible Model of Neurons

机译：神经元生物学上可行的模型的新的非线性稀疏成分分析

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It is known that brain can create a sparse representation of the environment in both sensory and mnemonic forms (Olshausen & Field, 2004). Such sparse representation can be combined in downstream areas to create rich multisensory responses to support various cognitive and motor functions. Determining the components present in neuronal responses in a given region is key to deciphering its functional role and connection with upstream areas. One approach for parsing out various sources of information in a single neuron is by using linear blind source separation (BSS) techniques. However, applying linear techniques to neuronal spiking activity is likely to be suboptimal due to inherent and unknown nonlinearity of neuronal responses to inputs. This letter proposes a nonlinear sparse component analysis (SCA) method to separate jointly sparse inputs to neurons with post summation nonlinearity, or SCA for post-nonlinear neurons (SCAPL). Specifically, a linear clustering approach followed by principal curve regression (PCR) and a nonlinear curve fitting are used to separate sources. Analysis using simulated data shows that SCAPL accuracy outperforms ones obtained by linear SCA, as well as other separating methods, including linear independent and principal component analyses. In SCAPL, the number of derived sparse components is not limited by the number of neurons, unlike most BSS methods. Furthermore, this method allows for a broad range of post-summation nonlinearities that could differ among neurons. The sensitivity of our method to noise, joint sparseness, degree, and shape of nonlinearity and mixing ill conditions is discussed and compared to existing methods. Our results show that the proposed method can successfully separate input components in a population of neurons provided that they are temporally sparse to some degree. Application of SCAPL should facilitate comparison of functional roles across regions by parsing various elements present in a region.
机译：众所周知，大脑可以以感官和记忆形式创建稀疏的环境表示（Olshausen＆Field，2004）。可以在下游区域组合这种稀疏表示，以创建丰富的多感官响应，以支持各种认知和运动功能。确定给定区域中神经元反应中存在的成分是破译其功能作用和与上游区域的联系的关键。解析单个神经元中各种信息源的一种方法是使用线性盲源分离（BSS）技术。然而，由于神经元对输入的响应的固有和未知的非线性，将线性技术应用于神经元尖峰活动可能不是最优的。这封信提出了一种非线性稀疏成分分析（SCA）方法，以将具有后求和非线性的联合稀疏输入分离到神经元，或将SCA用于非线性后神经元（SCAPL）。具体而言，使用线性聚类方法，然后进行主曲线回归（PCR）和非线性曲线拟合来分离源。使用模拟数据进行的分析表明，SCAPL的精度优于线性SCA以及其他分离方法（包括线性独立和主成分分析）所获得的精度。在SCAPL中，与大多数BSS方法不同，派生的稀疏分量的数量不受神经元数量的限制。此外，此方法还允许广泛的求和后非线性，这些非线性可能在神经元之间有所不同。讨论了我们的方法对噪声，联合稀疏度，非线性度和形状以及混合病态的敏感性，并与现有方法进行了比较。我们的结果表明，所提出的方法可以成功地分离神经元群体中的输入成分，前提是它们在某种程度上在时间上是稀疏的。 SCAPL的应用应该通过解析区域中存在的各种元素来促进跨区域功能角色的比较。

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  • 来源
    《Neural computation》 |2019年第9期|1853-1873|共21页
  • 作者

    Heidarieh S. M.; Jahed M.; Ghazizadeh A.;

  • 作者单位

    Sharif Univ Technol Sch Elect Engn Tehran Iran;

    Sharif Univ Technol Sch Elect Engn Tehran Iran|Sharif Univ Technol Brain Res Ctr Biointelligence Res Unit Tehran Iran;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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