Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation

• Main Author: Wiktor eMlynarski
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2014-03-01
• Series: Frontiers in Computational Neuroscience
• Subjects: auditory scene analysis; efficient coding; binaural hearing; natural sound statistics; spectrotemporal receptive fields
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00026/full

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<br/>coding hypothesis explains formation of neurons which explicitly represent environmental features of different functional importance. <br/>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. <br/>This results imply that efficient coding is a useful strategy for learning structures which allow for making behaviorally vital inferences about the environment.