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In non-human primates a scheme for the organization of the auditory cortex is frequently used to localize auditory processes. The scheme allows a common basis for comparison of functional organization across non-human primate species. However, although a body of functional and structural data in non-human primates supports an accepted scheme of nearly a dozen neighboring functional areas, can this scheme be directly applied to humans? Attempts to expand the scheme of auditory cortical fields in humans have been severely hampered by a recent controversy about the organization of tonotopic maps in humans, centered on two different models with radically different organization. We point out observations that reconcile the previous models and suggest a distinct model in which the human cortical organization is much more like that of other primates. This unified framework allows a more robust and detailed comparison of auditory cortex organization across primate species including humans.

The focus of most research on auditory cortical neurons has concerned the effects of rather simple stimuli, such as pure tones or broadband noise, or the modulation of a single acoustic parameter. Extending these findings to feature coding in more complex stimuli such as natural sounds may be difficult, however. Generalizing results from the simple to more complex case may be complicated by nonlinear interactions occurring between multiple, simultaneously varying acoustic parameters in complex sounds. To examine this issue in the frequency domain, we performed a parametric study of the effects of two global features, spectral pattern (here ripple frequency) and bandwidth, on primary auditory (A1) neurons in awake macaques. Most neurons were tuned for one or both variables and most also displayed an interaction between bandwidth and pattern implying that their effects were conditional or interdependent. A spectral linear filter model was able to qualitatively reproduce the basic effects and interactions, indicating that a simple neural mechanism may be able to account for these interdependencies. Our results suggest that the behavior of most A1 neurons is likely to depend on multiple parameters, and so most are unlikely to respond independently or invariantly to specific acoustic features.