Identifying the Origins of Auditory Objects: Application of Generalized Linear-Nonlinear Models and Noise-Correlation Analysis to the Neural Representation of Rat Vocalizations in Rat Primary Auditory Cortex
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. 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. 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. 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. 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.