In this paper, an auditory attention driven computational model of the auditory midbrain is proposed based on a spiking neural network [17] in order to localize attended sound sources in reverberant environments. Both bottom-up attention driven by sensors and top-down attention driven by the cortex are modeled at the level of an auditory midbrain nucleus - the inferior colliculus (IC). Improvements of the model in [17] is made to increase biological plausibility. First, inter-neuron inhibitions are modeled among the IC neurons which have the same characteristic frequency but different spatial response. This is designed to mimic the precedence effect [15] to produce localization results in reverberate environments. Secondly, descending projections from the auditory cortex (AC) to the IC are model to simulate the top-down attention so that focused sound sources can be better sensed in noise or multiple sound source situations. Our model is implemented on a mobile robot with a manikin head equipped with binaural microphones and tested in a real environment. The results shows that our attention driven model can give more accurate localization results than prior models.
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