Max-Planck Institute for Mathematics in the Sciences Leipzig, Germany.
Front Comput Neurosci. 2014 Mar 7;8:26. doi: 10.3389/fncom.2014.00026. eCollection 2014.
To date a number of studies have shown that receptive field shapes of early sensory neurons can be reproduced by optimizing coding efficiency of natural stimulus ensembles. A still unresolved question is whether the efficient coding hypothesis explains formation of neurons which explicitly represent environmental features of different functional importance. This paper proposes that the spatial selectivity of higher auditory neurons emerges as a direct consequence of learning efficient codes for natural binaural sounds. Firstly, it is demonstrated that a linear efficient coding transform-Independent Component Analysis (ICA) trained on spectrograms of naturalistic simulated binaural sounds extracts spatial information present in the signal. A simple hierarchical ICA extension allowing for decoding of sound position is proposed. Furthermore, it is shown that units revealing spatial selectivity can be learned from a binaural recording of a natural auditory scene. In both cases a relatively small subpopulation of learned spectrogram features suffices to perform accurate sound localization. Representation of the auditory space is therefore learned in a purely unsupervised way by maximizing the coding efficiency and without any task-specific constraints. This results imply that efficient coding is a useful strategy for learning structures which allow for making behaviorally vital inferences about the environment.
迄今为止，许多研究表明，通过优化自然刺激集合的编码效率，可以再现早期感觉神经元的感受野形状。一个尚未解决的问题是，有效编码假说是否解释了明确表示不同功能重要性的环境特征的神经元的形成。本文提出，较高听觉神经元的空间选择性是学习自然双耳声音有效代码的直接结果。首先，证明了基于自然模拟双耳声音的频谱图训练的线性有效编码变换-独立成分分析（ICA）提取了信号中存在的空间信息。提出了一种简单的分层 ICA 扩展，允许对声音位置进行解码。此外，还表明可以从自然听觉场景的双耳记录中学习到具有空间选择性的单元。在这两种情况下，学习的频谱图特征的相对较小子群体足以进行精确的声音定位。因此，通过最大化编码效率并在没有任何特定任务约束的情况下，以纯无监督的方式学习听觉空间的表示。这一结果表明，有效编码是学习允许对环境进行行为上至关重要推断的结构的有用策略。
