Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation
This addresses the problem of understanding neural representation formation in auditory processing for neuroscience and computational modeling, though it is incremental as it builds on existing efficient coding frameworks.
The paper tackles the question of whether efficient coding explains the formation of neurons that represent important environmental features, showing that spatial selectivity in higher auditory neurons emerges from learning efficient codes for natural binaural sounds, with a small subpopulation of learned features enabling accurate sound localization.
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.