Encoding of ultrasonic vocalizations in the auditory cortex

Abstract

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 conspecific vocalizations, we presented a library of natural and temporally transformed ultrasonic vocalizations (USVs) to awake rats while recording neural activity in the primary auditory cortex (A1) with chronically implanted multielectrode probes. Many neurons reliably and selectively responded to USVs. The response strength to USVs correlated strongly with the response strength to frequency-modulated (FM) 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 ultrasonic frequency range. For responses of each neuron to each stimulus group, we fitted a novel predictive model: a reduced generalized linear-nonlinear 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 that of an analogous spectrogram-based linear-nonlinear 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.

Multiscale Optical Ca2+ Imaging of Tonal Organization in Mouse Auditory Cortex

“…This capability is timely, as the encoding of vocalizations in primate auditory cortex has become a potentially tractable realm for studying higher-order processing, where different cortical fields play complementary but as-yet-poorly-understood roles (Romanski and Averbeck, 2009). Vocalization studies in rodents (advantageous for facile transgenic manipulation) would furnish a critical counterpoint; yet, comparatively little is known, with detailed electrode studies mainly focused on AI (Carruthers et al, 2013), and the likely essential role of other subregions sparingly explored (Geissler and Ehret, 2004). In this regard, our multiscale imaging approach could prove particularly advantageous.…”

Cortical Interneurons Differentially Shape Frequency Tuning following Adaptation

“…Electrodes were targeted to A1 on the basis of stereotaxic coordinates and relation to blood vessels. In electrophysiological recordings, the location was confirmed by examining the click and tone pip responses of the recorded units for characteristic responses of neurons in A1, as described previously by our group in the rat (Blackwell et al, 2016; Carruthers et al, 2015; Carruthers et al, 2013; Natan et al, 2016) and in the mouse (Aizenberg et al, 2015; Natan et al, 2015). Mice were placed in the recording chamber, anesthetized with isoflurane, and the headpost secured to a custom base, immobilizing the head.…”

“…Electrophysiological data from 32 channels were bandpass filtered at 10-300 Hz for LFP and current-source density (CSD) analysis or at 600-6000 Hz for spike analysis, digitized at 32 kHz and stored for offline analysis (Neuralynx). Spikes belonging to single units were clustered using commercial software (Offline Sorter, Plexon) (Carruthers et al, 2013). Putative excitatory neurons were identified based on their expected response patterns to sounds and lack of significant suppression of the spontaneous firing rate due to light (Lima et al, 2009; Moore and Wehr, 2013).…”

“…Speakers were calibrated prior to the experiments to ±3 dB over frequencies between 1 and 40 kHz, by placing a microphone (Brüel and Kjaer) in the location of the ear contralateral to the recorded A1 hemisphere, recording speaker output and filtering stimuli to compensate for acoustic aberrations (Carruthers et al, 2013). First, to measure tuning, a train of 50 pure tones of frequencies spaced logarithmically between 1 and 80 kHz, at 65 dB SPL relative to 20 μPa, in pseudo-random order, was presented 20 times.…”

Contextual modulation of sound processing in the auditory cortex

“…For complicated sounds, such as vocalizations, sensory representations are modified between primary and secondary auditory areas, generating invariance to acoustic distortions of these complex signals [54,55]. Similarly, in high-level human and avian auditory areas, responses to vocalizations embedded in multi-speaker choruses are background invariant, and strongly reflect behavioral detection of the attended speaker [56,57].…”