Encoding of Ultrasonic Vocalizations in the Auditory Cortex

Mon, 02 Dec 2013 19:00:00 -0500

One of the central tasks of the mammalian auditory system is to represent information about acoustic communicative signals, such as vocalizations. However, the neuronal computations underlying vocalization encoding in the central auditory system are poorly understood. While neurons in the lower brain areas of the auditory stream are somewhat understood in terms of the spectral and temporal features that drive them, these simple features do a poor job of explaining the activity of neurons in the cortex. The auditory cortex is the area of the brain where we start to see neurons that respond selectively to distinct auditory objects, such as vocalizations, regardless of small spectral and temporal perturbations of these objects.

Rats communicate via ultra-sonic vocalizations (USVs). Despite their behavioral prevalence, little is known about how these vocalizations are encoded in the auditory cortex. To learn how the rat auditory cortex encodes information about con-specific vocalizations, we presented a library of natural and temporally transformed ultra-sonic vocalizations (USVs) to awake rats, while recording neural activity in the primary auditory cortex (A1) using chronically implanted multi-electrode probes. The USVs that we presented were recorded during a friendly interaction between two male rats, and were typical of vocalizations that previous studies have associated with the communication of positive affect. We found that the auditory cortex exhibited specialized circuits that encoded the USVs. Many neurons reliably and selectively responded to USVs. The response strength to USVs correlated strongly with the response strength to frequency modulated sweeps and the FM rate tuning index, suggesting that related mechanisms generate responses to USVs as to FM sweeps. The response strength further correlated with the neuron's best frequency, with the strongest responses produced by neurons whose best frequency was in the ultra-sonic frequency range. We identified the specific computation that the auditory system performs in order to represent USVs with high accuracy.

For responses of each neuron to each stimulus group, we fitted a novel predictive model: a reduced generalized linear-non-linear model (GLNM) that takes the frequency modulation and single-tone amplitude as the only two input parameters. The GLNM accurately predicted neuronal responses to previously unheard USVs, and its prediction accuracy was higher than for an analogous spectrogram-based linear non-linear model. This computation was specific to the statistical structure of the original USVs, as the response strength of neurons and the model prediction accuracy were higher for original, rather than temporally transformed vocalizations. These results indicate that A1 processes original USVs differentially than transformed USVs, indicating preference for temporal statistics of the original vocalizations. Our study identified the neuronal computation that underlies specialization for behaviorally important sounds. Similar processing is likely to play an important role in speech encoding.