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To date a number of studies have shown that receptive field shapes of earlysensory neurons can be reproduced by optimizing coding efficiency of naturalstimulus ensembles. A still unresolved question is whether the efficient codinghypothesis explains formation of neurons which explicitly representenvironmental features of different functional importance. This paper proposesthat the spatial selectivity of higher auditory neurons emerges as a directconsequence of learning efficient codes for natural binaural sounds. Firstly,it is demonstrated that a linear efficient coding transform - IndependentComponent Analysis (ICA) trained on spectrograms of naturalistic simulatedbinaural sounds extracts spatial information present in the signal. A simplehierarchical ICA extension allowing for decoding of sound position is proposed.Furthermore, it is shown that units revealing spatial selectivity can belearned from a binaural recording of a natural auditory scene. In both cases arelatively small subpopulation of learned spectrogram features suffices toperform accurate sound localization. Representation of the auditory space istherefore learned in a purely unsupervised way by maximizing the codingefficiency and without any task-specific constraints. This results imply thatefficient coding is a useful strategy for learning structures which allow formaking behaviorally vital inferences about the environment.

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