Short Bouts of Vocalization Induce Long-Lasting Fast Gamma Oscillations in a Sensorimotor Nucleus

Abstract

Performance evaluation is a critical feature of motor learning. In the vocal system, it requires the integration of auditory feedback signals with vocal motor commands. The network activity that supports such integration is unknown, but it has been proposed that vocal performance evaluation occurs offline. Recording from NIf, a sensorimotor structure in the avian song system, we show that short bouts of singing in adult male zebra finches (Taeniopygia guttata) induce persistent increases in firing activity and coherent oscillations in the fast gamma range (90 -150 Hz). Single units are strongly phase locked to these oscillations, which can last up to 30 s, often outlasting vocal activity by an order of magnitude. In other systems, oscillations often are triggered by events or behavioral tasks but rarely outlast the event that triggered them by more than 1 s. The present observations are the longest reported gamma oscillations triggered by an isolated behavioral event. In mammals, gamma oscillations have been associated with memory consolidation and are hypothesized to facilitate communication between brain regions. We suggest that the timing and persistent nature of NIf's fast gamma oscillations make them well suited to facilitate the integration of auditory and vocal motor traces associated with vocal performance evaluation.

At the interface of the auditory and vocal motor systems: NIf and its role in vocal processing, production and learning

“…Multiunit activity is generally strongest shortly before and during syllable production and weakest during the times corresponding to silent intervals between syllables. Lewandowski and Schmidt (2011) quantified multiunit motor activity in NIf and found that it precedes the onset of song introductory notes by an average 45.7 ± 15.7 ms, which is remarkably similar to the song premotor onset value of 45 ms reported for HVC (Schmidt, 2003; Kozhevnikov and Fee, 2007). Consistent with the idea that activity in NIf during song production is motor and not auditory related, vocalization-related multiunit activity in NIf ends 26.5 ± 13.5 ms before vocal offset, and this cessation of premotor activity is accompanied by a general suppression of neuronal activity in NIf (Fig.…”

“…At the multiunit level, motor activity in NIf, HVC, and RA is broadly similar (Yu and Margoliash, 1996; Schmidt, 2003; Kozhevnikov and Fee, 2007; Lewandowski and Schmidt, 2011). Unlike single unit activity, multiunit activity in all three of these nuclei is characterized by increases in neural activity that precede vocal output and continue throughout song (Fig.…”

“…Auditory activity in NIf is of particular interest because NIf’s input to HVC is the largest single source of auditory information to the song system (Vates et al, 1996; Cardin and Schmidt, 2004a; Coleman and Mooney, 2004; Cardin et al, 2005; Bauer et al, 2008). In addition, NIf provides nearly all of HVC’s spontaneous excitatory drive (Cardin and Schmidt, 2004a; Cardin et al, 2005), has premotor bursts that precede similar bursts in HVC (McCasland, 1987; Lewandowski and Schmidt, 2011), and drives the replay of premotor-like bursting in HVC during sleep (Hahnloser and Fee, 2007). Given the critical necessity of HVC for song learning and production, NIf is well positioned to have a significant impact on song system function.…”

Sparse spectral estimation from point process observations

“…The multitaper estimate is among the most reliable nonparametric spectral estimation methods. The foregoing method of estimating the PSD of the smoothed PSTH, which we denote by PSTH-PSD, is one of the most commonly-used methods for extracting the spectral representation of the spiking data [11,12,13]. We have considered a frequency spacing of 0.125Hz for the PSD estimates here, which corresponds to N = 1200 in our proposed method, and DC components of the normalized PSD's are eliminated in the figures.…”

“…Following the common frequency-domain analysis of neural data such as EEG, existing methods for point process spectral estimation often compute a continuous estimate of the spiking rate and analyze the power spectral density (PSD) of this estimate. The spiking rate estimation is either done by simply smoothing the spiking histogram [11,12,13] or using generalized linear Gaussian state-space models to estimate the conditional intensity function (CIF) of the point process [14,15]. However, these approaches suffer from the following shortcomings: Firstly, they are limited in terms of their spectral resolution as smoothing in the time domain for spiking rate estimation results in distortion in the frequency domain.…”