  An accurate auditory space map can be learned from auditory experience, for
example during development or in response to altered auditory cues such as a
modified pinna. We studied neural network models that learn to localise a
single sound source in the horizontal plane using binaural cues based on
limited supervisions. These supervisions can be unreliable or sparse in real
life. First, a simple model that has unreliable estimation of the sound source
location is built, in order to simulate the unreliable auditory orienting
response of newborns. It is used as a Teacher that acts as a source of
unreliable supervisions. Then we show that it is possible to learn a continuous
auditory space map based only on noisy left or right feedbacks from the
Teacher. Furthermore, reinforcement rewards from the environment are used as a
source of sparse supervision. By combining the unreliable innate response and
the sparse reinforcement rewards, an accurate auditory space map, which is hard
to be achieved by either one of these two kind of supervisions, can eventually
be learned. Our results show that the auditory space mapping can be calibrated
even without explicit supervision. Moreover, this study implies a possibly more
general neural mechanism where multiple sub-modules can be coordinated to
facilitate each other's learning process under limited supervisions.
