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Neurons in auditory cortex are selective for the frequency content of acoustical stimuli. Classically, this response selectivity is studied at the single-neuron level. However, current research often employs functional imaging techniques to investigate the organization of auditory cortex. The signals underlying the imaging data arise from neural mass action and reflect the properties of populations of neurons. For example, the signal used for functional magnetic resonance imaging (fMRI-BOLD) was shown to correlate with the oscillatory activity quantified by local field potentials (LFPs). This raises the questions of how the frequency selectivity in neuronal population signals compares with the tuning of spiking responses. To address this, we quantified tuning properties of auditory-evoked potentials (AEP), different frequency bands of the LFP, analog multi-unit (AMUA), and spike-sorted single- and multiunit activity in auditory cortex. The AMUA showed a close correspondence in frequency tuning to the spike-sorted activity. In contrast, for the LFP, we found a clear dissociation of high- and low-frequency bands: there was a gradual increase of tuning-curve similarity, tuning specificity, and information about the stimulus with increasing LFP frequency. Although properties of the high-frequency LFP matched those of spiking activity, the lower-frequency bands differed considerably as did the AEP. These results demonstrate that electrophysiological population responses exhibit varying degrees of frequency tuning and suggest that those functional imaging methods that are related to high-frequency oscillatory activity should well reflect the neuronal processing of sound frequency.

Despite the extensive physiological work performed on auditory cortex, our understanding of the basic functional properties of auditory cortical neurons is incomplete. For example, it remains unclear what stimulus features are most important for these cells. Determining these features is challenging given the considerable size of the relevant stimulus parameter space as well as the unpredictable nature of many neurons' responses to complex stimuli due to nonlinear integration across frequency. Here we used an adaptive stimulus optimization technique to obtain the preferred spectral input for neurons in macaque primary auditory cortex (AI). This method uses a neuron's response to progressively modify the frequency composition of a stimulus to determine the preferred spectrum. This technique has the advantage of being able to incorporate nonlinear stimulus interactions into a “best estimate” of a neuron's preferred spectrum. The resulting spectra displayed a consistent, relatively simple circumscribed form that was similar across scale and frequency in which excitation and inhibition appeared about equally prominent. In most cases, this structure could be described using two simple models, the Gabor and difference of Gaussians functions. The findings indicate that AI neurons are well suited for extracting important scale-invariant features in sound spectra and suggest that they are designed to efficiently represent natural sounds.