Sensory systems may adapt to behavioral requirements through state-dependent changes. In the forebrain song-system nucleus HVc of zebra finches, state-dependent auditory responses have been described in multiunit recordings. Here we report on behavioral state-dependent changes in the activity of distinct HVc neuronal classes. HVc projection neurons were identified by electrically stimulating HVc's target nuclei, the robust nucleus of the archistriatum and Area X, in anesthetized zebra finches. Projection neurons and two classes of putative interneurons could be distinguished on the basis of extracellular spike waveforms, with the first two factors of a principal components analysis accounting for 81% of the variance in spike morphometric values. Spike width was the best single variable for distinguishing among the neuronal classes. Putative interneurons had much higher firing rates spontaneously and in response to song than did projection neurons, which had extremely low spontaneous rates and phasic responses to song. Recordings from HVc in behaving animals were dominated by the two classes of putative interneurons. Both classes showed strong, selective, and temporally similar auditory responses during sleep, but only one class of interneurons reliably maintained auditory responses on waking. These responses were weaker and less selective than those seen during sleep. The observation that HVc auditory responsiveness in awake zebra finches is restricted to some classes of neurons may help explain prior multiunit results that suggested nearly complete suppression of HVc auditory responses in awake birds. We propose that the heterogeneous effects of behavioral state on distinct subpopulations of HVc neurons allow HVc to participate in multiple roles during song production, conspecific song recognition, and possibly memory consolidation during sleep.

How stable are neural activity patterns compared across periods of sleep? We evaluated this question in adult zebra finches, whose premotor neurons in the nucleus robustus arcopallialis (RA) exhibit sequences of bursts during daytime singing that are characterized by precise timing relative to song syllables. Each burst has a highly regulated pattern of spikes. We assessed these spike patterns in singing that occurred before and after periods of sleep. For about half of the neurons, one or more premotor bursts had changed after sleep, an average of 20% of all bursts across all RA neurons. After sleep, modified bursts were characterized by a discrete, albeit modest, loss of spikes with compensatory increases in spike intervals, but not changes in timing relative to the syllable. Changes in burst structure followed both interrupted bouts of sleep (1.5-3 h) and full nights of sleep, implicating sleep and not circadian cycle as mediating these effects. Changes in burst structure were also observed during the day, but far less frequently. In cases where multiple bursts in the sequence changed in a single cell, the sequence position of those bursts tended to cluster together. Bursts that did not show discrete changes in structure also showed changes in spike counts, but not biased toward losses. We hypothesize that changes in burst patterns during sleep represent active sculpting of the RA network, supporting auditory feedback-mediated song maintenance.

The detection of patterned spiking activity is important in the study of neural coding. A pattern filtering approach is developed for pattern detection under the framework of point processes, which offers flexibility in combining temporal details and firing rates. The detection combines multiple steps of filtering in a coarse-to-fine manner. Under some conditional Poisson assumptions on the spiking activity, each filtering step is equivalent to classifying by likelihood ratios all the data segments as targets or as background sequences. Unlike previous studies, where global surrogate data were used to evaluate the statistical significance of the detected patterns, a localized p-test procedure is developed, which better accounts for firing modulation and nonstationarity in spiking activity. Common temporal structures of patterned activity are learned using an entropy-based alignment procedure, without relying on metrics or pair-wise alignment. Applications of pattern filtering to single, presumptive interneurons recorded in the nucleus HVc of zebra finch are illustrated. These demonstrate a match between the auditory-evoked response to playback of the individual bird's own song and spontaneous activity during sleep. Small temporal compression or expansion, or both, is required for optimal matching of spontaneous patterns to stimulus-evoked activity.

Auditory feedback (AF) plays a critical role in vocal learning. Previous studies in songbirds suggest that low-frequency (< ~1 kHz) components may be salient cues in AF. We explored this with auditory stimuli including the bird’s own song (BOS) and BOS variants with increased relative power at low frequencies (LBOS). We recorded single units from BOS-selective neurons in two forebrain nuclei (HVC and Area X) in anesthetized zebra finches. Song-evoked responses were analyzed based on both rate (spike counts) and temporal coding of spike trains. The BOS and LBOS tended to evoke similar spike-count responses in substantially overlapping populations of neurons in both HVC and Area X. Analysis of spike patterns demonstrated temporal coding information that discriminated among the BOS and LBOS stimuli significantly better than spike counts in the majority of HVC (94%) and Area X (85%) neurons. HVC neurons contained more and a broader range of temporal coding information to discriminate among the stimuli, than Area X neurons. These results are consistent with a potential role of temporal coding in differentiating in the spectral components of the BOS in HVC and Area X neurons.

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