Nelli H. Salminen

A magnetoencephalography study was conducted to reveal the neural code of interaural time difference (ITD) in the human cortex. Widely used crosscorrelator models predict that the code consists of narrow receptive fields distributed to all ITDs. The present findings are, however, more in line with a neural code formed by two opponent neural populations: one tuned to the left and the other to the right hemifield. The results are consistent with models of ITD extraction in the auditory brainstem of small mammals and, therefore, suggest that similar computational principles underlie human sound source localization.

Previous work on the human auditory cortex has revealed areas specialized in spatial processing but how the neurons in these areas represent the location of a sound source remains unknown. Here, we performed a magnetoencephalography (MEG) experiment with the aim of revealing the neural code of auditory space implemented by the human cortex. In a stimulus-specific adaptation paradigm, realistic spatial sound stimuli were presented in pairs of adaptor and probe locations. We found that the attenuation of the N1m response depended strongly on the spatial arrangement of the two sound sources. These location-specific effects showed that sounds originating from locations within the same hemifield activated the same neuronal population regardless of the spatial separation between the sound sources. In contrast, sounds originating from opposite hemifields activated separate groups of neurons. These results are highly consistent with a rate code of spatial location formed by two opponent populations, one tuned to locations in the left and the other to those in the right. This indicates that the neuronal code of sound source location implemented by the human auditory cortex is similar to that previously found in other primates.

Our native language has a lifelong effect on how we perceive speech sounds. Behaviorally, this is manifested as categorical perception, but the neural mechanisms underlying this phenomenon are still unknown. Here, we constructed a computational model of categorical perception, following principles consistent with infant speech learning. A self-organizing network was exposed to a statistical distribution of speech input presented as neural activity patterns of the auditory periphery, resembling the way sound arrives to the human brain. In the resulting neural map, categorical perception emerges from most single neurons of the model being maximally activated by prototypical speech sounds, while the largest variability in activity is produced at category boundaries. Consequently, regions in the vicinity of prototypes become perceptually compressed, and regions at category boundaries become expanded. Thus, the present study offers a unifying framework for explaining the neural basis of the warping of perceptual space associated with categorical perception.