CRCNS: Ethological theories: optimal auditory processing

描述（由申请人提供）：使用作为一个出发点的假设，即感觉系统已经进化到行为相关或自然刺激执行最佳转换，我们将使用系统分析方法和信息理论的原则，以发展听觉处理的理论。 我们的理论的目标是预测在听觉处理的不同阶段发现的刺激-反应转换。首先，我们将获得理论预测的线性感受野的分布，共同最大化的信号噪声比和熵的输出时，与自然的声音呈现的合奏过滤器。 其次，我们将推导出非线性刺激-反应转换，这些转换可以通过生物学上合理的网络获得，并且将最小化神经元之间的互信息。 这些神经网络将执行一种形式的独立分量分析，其中产生的操作是提取自然声音中的独立声学特征。 我们还将开发新的方法来估计鸣禽单个神经元和神经元集合传递的信息。 将分两步评估理论模型的拟合优度。 首先，我们将比较理论上的刺激-反应函数与直接从神经数据中获得的相同阶数的函数。 第二，我们将评估如何以及刺激反应函数描述实际的神经转换，通过比较预测的反应和预测的信息率与实际的反应和测量的信息率。 我们的分析将使我们了解复杂的自然声音在动物和人类的听觉系统中是如何处理的。 了解生物系统如何处理自然声音将有助于开发工程应用中的声音压缩，语音识别和助听器和听觉修复的声音预处理的新算法。

DESCRIPTION (provided by applicant): Using as a starting point the postulate that sensory systems have evolved to perform optimal transformations on behaviorally relevant or natural stimuli, we will use systems analysis methods and information theoretic principles to develop a theory of auditory processing. The goals of our theory will be to predict the stimulus-response transformations that are found at different stages of auditory processing. First, we will obtain theoretical predictions for the distribution of linear receptive fields by jointly maximizing signal to noise ratio and entropy in the output of the ensemble of filters when presented with natural sounds. Second, we will derive non-linear stimulus-response transformations that can be obtained with biologically plausible networks and that will minimize the mutual information across neurons. These neural networks will perform a form of independent component analysis, in which the resulting operation is to extract independent acoustical features in natural sounds. We will also develop novel methods to estimate the information transmitted by single neurons and ensembles of neurons in songbirds. The goodness of fit of the theoretical models will be assessed in two steps. First we will compare the theoretical stimulus-response functions with the functions of the same order that will be obtained directly from the neural data. Second, we will evaluate how well the stimulus-response functions describe the actual neural transformation by comparing predicted responses and predicted information rates with actual responses and measured information rates. Our analysis will give us insight on how complex natural sounds are processed in the auditory system of animals and humans. Understanding how biological systems process natural sounds will be instrumental in the development of novel algorithms in engineering applications for sound compression, speech recognition and sound pre-processing for hearing aids and auditory prosthetics.