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Representation of spectral and temporal envelope of twitter vocalizations in common marmoset primary auditory cortex
Authors:Nagarajan Srikantan S  Cheung Steven W  Bedenbaugh Purvis  Beitel Ralph E  Schreiner Christoph E  Merzenich Michael M
Affiliation:Department of Bioengineering, University of Utah, Salt Lake City, Utah 84112-9458, USA. sri@utah.edu
Abstract:Cortical sensitivity in representations of behaviorally relevant complex input signals was examined in recordings from primary auditory cortical neurons (AI) in adult, barbiturate-anesthetized common marmoset monkeys (Callithrix jacchus). We studied the robustness of distributed responses to natural and degraded forms of twitter calls, social contact vocalizations comprising several quasi-periodic phrases of frequency and AM. We recorded neuronal responses to a monkey's own twitter call (MOC), degraded forms of their twitter call, and sinusoidal amplitude modulated (SAM) tones with modulation rates similar to those of twitter calls. In spectral envelope degradation, calls with narrowband channels of varying bandwidths had the same temporal envelope as a natural call. However, the carrier phase was randomized within each narrowband channel. In temporal envelope degradation, the temporal envelope within narrowband channels was filtered while the carrier frequencies and phases remained unchanged. In a third form of degradation, noise was added to the natural calls. Spatiotemporal discharge patterns in AI both within and across frequency bands encoded spectrotemporal acoustic features in the call although the encoded response is an abstract version of the call. The average temporal response pattern in AI, however, was significantly correlated with the average temporal envelope for each phrase of a call. Response entrainment to MOC was significantly correlated with entrainment to SAM stimuli at comparable modulation frequencies. Sensitivity of the response patterns to MOC was substantially greater for temporal envelope than for spectral envelope degradations. The distributed responses in AI were robust to additive continuous noise at signal-to-noise ratios > or =10 dB. Neurophysiological data reflecting response sensitivity in AI to these forms of degradation closely parallel human psychophysical results on the intelligibility of degraded speech in quiet and noisy conditions.
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