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首页> 外文期刊>Journal of Comparative Physiology, A. Sensory, Neural, and Behavioral Physiology >Computational principles underlying recognition of acoustic signals in grasshoppers and crickets
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Computational principles underlying recognition of acoustic signals in grasshoppers and crickets

机译：蚱grass和的声信号识别基础的计算原理

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摘要

Grasshoppers and crickets independently evolved hearing organs and acoustic communication. They differ considerably in the organization of their auditory pathways, and the complexity of their songs, which are essential for mate attraction. Recent approaches aimed at describing the behavioral preference functions of females in both taxa by a simple modeling framework. The basic structure of the model consists of three processing steps: (1) feature extraction with a bank of 'LN models'aEuro"each containing a linear filter followed by a nonlinearity, (2) temporal integration, and (3) linear combination. The specific properties of the filters and nonlinearities were determined using a genetic learning algorithm trained on a large set of different song features and the corresponding behavioral response scores. The model showed an excellent prediction of the behavioral responses to the tested songs. Most remarkably, in both taxa the genetic algorithm found Gabor-like functions as the optimal filter shapes. By slight modifications of Gabor filters several types of preference functions could be modeled, which are observed in different cricket species. Furthermore, this model was able to explain several so far enigmatic results in grasshoppers. The computational approach offered a remarkably simple framework that can account for phenotypically rather different preference functions across several taxa.
机译：蚱and和independently独立地发展了听觉器官和声音交流。他们在听觉途径的组织和歌曲的复杂性方面有很大的不同，这对于吸引伴侣至关重要。最近的方法旨在通过简单的建模框架来描述两种分类中女性的行为偏好功能。该模型的基本结构包括三个处理步骤：（1）使用“ LN模型”“ aEuro”库进行特征提取，每个模型都包含一个线性滤波器，随后是非线性的;（2）时间积分，以及（3）线性组合。滤波器的特定属性和非线性特性是通过在大量不同歌曲特征和相应的行为反应得分上训练的遗传学习算法确定的，该模型显示了对测试歌曲的行为反应的出色预测。遗传算法都找到了类似Gabor的函数作为最优滤波器形状，通过对Gabor滤波器进行稍加修改，可以建模几种类型的偏好函数，这些函数在不同的板球物种中都可以观察到。蚱grass的神秘结果。计算方法提供了一个非常简单的框架，可以解释表型上的差异ent跨多个分类单元的偏好功能。

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