Inhibition, not excitation, is the key to multimodal sensory integration.

Multimodal neuronal maps, combining input from two or more sensory systems, play a key role in the processing of sensory and motor information. For such maps to be of any use, the input from all participating modalities must be calibrated so that a stimulus at a specific spatial location is represented at an unambiguous position in the multimodal map. Here we discuss two methods based on supervised spike-timing-dependent plasticity (STDP) to gauge input from different sensory modalities so as to ensure a proper map alignment. The first uses an excitatory teacher input. It is therefore called excitation-mediated learning. The second method is based on an inhibitory teacher signal, as found in the barn owl, and is called inhibition-mediated learning. Using detailed analytical calculations and numerical simulations, we demonstrate that inhibitory teacher input is essential if high-quality multimodal integration is to be learned rapidly. Furthermore, we show that the quality of the resulting map is not so much limited by the quality of the teacher signal but rather by the accuracy of the input from other sensory modalities.