The Journal of lietrosoence. F &nary 17. 2010 • 30(71:2783-2791 • 2783

The Journal of lietrosoence. F &nary 17. 2010 • 30(71:2783-2791 • 2783 BehaviorallSystems/Cognitive Neuronal Stability and Drift across Periods of Sleep: Premotor Activity Patterns in a Vocal Control Nucleus of Adult Zebra Finches Peter L Rauske,I Zhiyi Chi? Amish S. Dave,' and Daniel Margoliashi Departments of 'Organismal Biology and Anatomy and 2Statistics, University of Chicago, Chicago, Illinois 60611 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. Introduction Sleep-dependent behavioral plasticity has been observed in a broad range of perceptual, motor, and higher-level cognitive tasks in studies in adult humans (Kami et al., 1994; Stickgold et al., 2000; Fischer et al., 2002; Walker et al., 2002; Fenn et al., 2003; Wagner et al., 2004; Brawn et al., 2008). Electrophysiological studies support a role for active processes during sleep affecting memory consolidation in humans (Maquet et al., 2000; Peigneux et al., 2004; Reis et al., 2009), and behavioral and electrophysio- logical studies in animals implicate sleep in plastic mechanisms. Sleep modulates plastic changes in ocular dominance histograms in the developing visual cortex of young cats (Frank et al., 2001; Aton et al., 2009), the emergence of song system neuronal burst- ing in juvenile birds at the onset of song learning (Shank and Margoliash, 2009), and experience -dependent changes in the correlations of activity patterns of rat hippocampal neurons (Poe et al., 2000). These results emphasize changes measured in populations of neurons. Sleep-dependent changes in the individual activity pat- terns of single neurons during behavior are not well defined, however, and thus there is little data on the stability of single neuron activity patterns across periods of sleep. In this study, we address this issue in the birdsong system. Male zebra finches Ikuwed June 5,2009: tedsed Dec. 13.2609; 5,00165 150.11,2010. lkswalmassappaled in part to/ KalIcnai Ir60cats °Math GT/Ell M1159831 ten Mast ;awful loPaa Arnida.Penry D.I.gattaret end Sitpilin D. Shea 55tongalitittv.5 ct thls mint600n. (cert:pc00fficesti0510 te addressed to DI. Wet L.Pathlo.Ser6o0MccartifoimaxeRcgrankkitateatio0 Inuct6e of 010460 3451. Swerlx Sued, SW< 1476. (hog011 6051 S brt01. 1)01.1015230/151.0050 3112402010 Com6ght 02010 the authors D2706174/10/10/761-12515.000) court females with "directed" singing: precisely structured, regu- lar songs comprising introductory notes followed by a sequence of syllables organized into a "motif." Directed songs are even more highly regulated than the undirected songs males otherwise sing (Sossinka and Bohner, 1980; Kao et al., 2005; Glaze and Troyer, 2006). Associated with directed singing are highly structured bursts of activity in presumptive projection neurons in the nucleus ro- bustus arcopallialis (RA) (Yu and Margoliash, 1996). Each spike burst has submillisecond precision in its timing relative to its corresponding syllable within the motif (Chi and Margoliash, 2001; Leonardo and Fee, 2005). These bursts have a well-defined number of spikes in a well-defined temporal pattern, both of which vary across bursts emitted at different times in the song. A given burst thus has a specific identity associated with onset time, number of spikes, and pattern of spikes. RA neurons show highly regulated oscillatory spontaneous activity, become completely suppressed about 50 ms before onset of song, and may achieve instantaneous firing rates of almost 800 Hz during singing. Thus, the nervous system expresses almost the entire dynamic range available to precisely modulate the activity of single RA neurons during singing. We took advantage of the reliability and precision of this sys- tem to examine neuronal stability over extended periods of time. RA extracellular recordings can be stable with high signal-to- noise ratio (SNR), but the technical challenge of maintaining high-quality recordings over the required durations and behav- iors in freely moving animals required by this design limited the size of the dataset. Nevertheless, we were able to directly compare premotor activity of the same single neurons before and after EFTA01076046 2781 • .I. ileurosci., Febuary 17,2010 • 30I71:1783-2791 Rauske et al. • Xeuronal Stability and Mt across Sleep periods of sleep. To the best of our knowledge, such comparisons have not been reported in any premotor system. Materials and Methods To examine the effects of sleep on the stability of premotor burst patterns in RA neurons, we recorded neuronal activity in three types of experi- mental sessions: short-sleep (or interrupted -sleep) sessions, long-sleep (or normal circadian -sleep) sessions, and awake-only sessions. For both types of sessions including sleep, we recorded the activity of the same single RA neurons whilebirdssang or produced learned calls (sec below) both before and after the period of sleep. We developed algorithms to identify changes to burst patterns across periods of sleep, as well as sta- tistical techniques to compare the frequency of such changes with that observed in the absence of sleep. Eleetrophysiology and design of the experiments. All animal procedures were approved by an Institutional Animal Care and Usc Committee. Adult male zebra finches (n = 13) were habituated to either a 16/8 h or 14/10 h light/dark cycle. We found no systematic differences between the two conditions, and combine the data for aggregate statistical analyses. The birds were implanted with microdrives with electrodes targeting PA; the implant design and surgical procedures have been described in detail previously (Dave et al., 1999). Briefly, a recording device carrying four glass-coated Pt-Ir electrodes (impedance, 1.2-2.0 MS/ at 1 kHz) was implanted under modified Equithesin anesthesia over RA. During re- cordingsessions starting 2-4 d later, a flexible cable connected the head- gear to an overhead commutator to allow the bird free movement within the cage. Differential recordings were used to minimize movement arti- facts. Recording sites were obtained by audiovisual monitoring of the recordings while using a drive screw to manually advance the electrodes. Birds were manually restrained during this procedure, then carefully released into the cage while trying to maintain unit isolation. Recording sessions began at various times during the day, and we recorded only sites with at least one unit that could be well isolated. In all cases, a conspecific female was introduced into an adjacent half-cage to elicit directed singing and calling. [In male zebra finches, contact or so-called "long" calls are learned vocalizations whose production in- volves PA premotor activity (Zann, 1985; Simpson and Vicario, 1990), and they are treated equivalently with song syllables in this study.' After collecting high SNR spike data during vocalizations comprising at least 10 song motifs and/or contact calls, or in the normal circadian rhythm depending on experimental design (see below), the cage lights were doused. After the bird was quiescent for several minutes, activity in RA entered a characteristic bursting mode. This distinct state was never observed in an awake bird, and bursting disappeared whenever the bird was disturbed or became active. Spontaneous bursting in RA and its efferent sensorimotor control nucleus (HVC) has come to be used as an assay for sleep. It is reliably associated with the onset of sleep postures and strong, selective auditory responses (Dave et al., 1998; Dave and Margo- Hash, 2000; Nick and Konishi, 2001; Hahnloser ct al., 2002, 2006; Cardin and Schmidt, 2003; Rauske a al., 2003; Shank and Margoliash, 2009) and has been correlated with EEC measures of sleep (Nick and Kon- ishi, 2001; Hahnloser et al., 2006; Shank and Margoliash, 2009). Dur- ing recording sessions including 1.5-3 h darkness (labeled "short sleep"; n = 10 neurons, 4 birds), we recorded continuously from the isolated RA single units, enabling us to estimate the amount of time birds actually slept by examining the bursting activity (or lack thereof) during the dark period. We used a quantitative measure of spontaneous PA bursting as a sleep assay, described below. During some recording sessions, we also verified by direct observation (infrared monitoring) that the bird's eyes were closed and respiration slowed when PA activity indicated sleep (Dave et al., 1998). During the short-sleep recording sessions, we also presented playback of the bird's own song. Recordings of the bird's own song were scaled to 70 dB root-mean-squared amplitude and presented randomly at 10-30 s intervals beginning immediately after turning out the lights. After 50- 250 repetitions of song playback, we recorded 20-60 min of ongoing spiking activity while the bird remained asleep. Thereafter, the lights were then turned back on, rousing the bird, after 1.5-3 h of sleep. Birds then directed singing toward the adjacent female, and we continued recordings until single-unit isolation was lost. Auditory stimulation en- abled us to verify the responsiveness to the bird's own song that RA neurons exhibit exclusively during sleep (Dave et al., 1998). Further- more, this was a preliminary experiment to test the hypothesis that sleep- related changes in singing behavior result from drift arising from neural replay during sleep activity without concomitant auditory feedback (Deregnaucourt et al., 2005). We hypothesized that playback would pro- vide structured activity during sleep, possibly preventing sleep-related changes, but failed to see systematic differences between short-sleep (au- ditory stimulation) and long-sleep (no stimulation) sessions (see Re- sults), a null result with respect to the sleep-drift hypothesis. We do not consider this hypothesis further in this study. In some additional, exceptional cases = 5 neurons, 3 birds), we successfully gambled on our ability to maintain stable unit isolation across a full night of sleep (8 or 10 h), maintaining the normal light/dark cycle. All but one of these cases involved single-unit isolation, with the exception being a site in which a pair of units could be reliably distin- guished from background activity but not from each other; this "double- unit" site was treated similarly to single units in our analysis. No auditory stimuli were presented during sleep for these sites, but ongoing activity was sampled throughout the night to verify the presence of bunting activity in RA that indicated the bird remained asleep. When the next day's light cycle began, recordings continued until unit isolation was lost. Finally, we augmented this data set with additional recordings during vocalizations in recording sessions that did not indude sleep (see below). Analysis of deep. Sleep was objectively defined behaviorally (eye clo- sure, body posture), and we also developed a quantitative measure of spontaneous bursting in RA neurons to use as an assay for sleep. We first established a baseline for a neuron's spontaneous spiking activity during periods before and after darkness when the bird was awake and active, but not vocalizing. We used 1-4 min segments of neuronal activity both before and after darkness, dividing the spiking activity into 3 s segments. For each segment, the distribution of interspike intervals (1S1s) was ap- proximately Gaussian because of the highly regular spiking activity of RA neurons in awake, nonvocalizing birds. We cakulated for each segment's ISI distribution the mean (IS1-MEAN) and standard deviation (1SI-SD). The resulting range of values across all awake segments for each single unit provided an estimate of the baseline variability in spiking activity in the awake bird. To quantify the amount of sleep during darkness, we similarly divided spiking activity into 3 s segments, calculating the ISI-MEAN and IS1-SD for each segment. Any segment whose ISI-MEAN and 151-517 both fell within a 95% confidence interval as determined by the baseline awake distributions was labeled "awake"; all other segments were labeled as `sleep" (for exampk,see supplemental Fig. I, available at unvw.jneurosci.org as supplemental material). Such labeling agreed well with visual inspec- tion of spiking activity, with segments including sleep-typical depressed firing rates and/or bunting reliably labeled as sleep. Video surveillance under infrared illumination verified that the bird was quiescent with closed eyelids in >95% of sleep-labeled segments. We had not developed reliable EEG recording techniques and an understanding of sleep staging in zebra finches except toward the end of these studies (Low et al., 2008); nevertheless, our analysis reliably distinguished sleep from wakang. Song syllables, spike bursts, and a definition of bunt opts. Vocalizations and onset and offset times for each syllable were identified by manual inspection of spectrographs. A syllable was defined as a stereotyped vocal gesture containing no silent interval 710 ms: in addition to the tradition- ally defined song syllables that comprise song "motifs" (stereotyped se- quence of syllables), wealso included introductory notes at the beginning of singing bouts and isolated "long" calls, both of which recruit RA bunting activity, in our definition of "syllable" for this study. Syllable onset times and spike times were merged for each site to create a raster plot of spiking activity associated with each syllable type. For each sylla- ble, we included the spiking activity beginning 50 ms before syllable onset and ending with the syllable offset. We used simple thresholding techniques to identify spike times for most, extremely well-isolated single units. For a few sites with more ambiguous isolation, we used theSpiicesort program, which uses a Bayes- ian approach to identify putative spikes with distinct spike-shape models EFTA01076047 Rauske et al. • Neuronal Stability and Drift across Sleep 1. Neurovi, February 17,2010 .30(71:2783-2794. 278S (Lewicki, 1994). To confirm single-unit isolation in all cases, we visually inspected overbid waveforms from all identified spike times to confirm that spike shapes were consistent throughout our recordings, and we used ISI distributions to confirm the hallmarks of single-unit isolation in RA (i.e., an approximately Gaussian distribution of ISIs during behav- ioral quiescence and a lack of ISIs <1 ms). For the majority of sites (28 of 42 single units), we were able to confidently identify 100% of all spikes after manual inspection. The remaining single-unit sites, as well as the "double-unit" site, included a small number of ambiguous spikes, so we estimate that we achieved 98-99% correct classification. In these cases, the ambiguities were attributable to either the extreme attenuation of spike amplitude during bursting (Yu and Margoliash, 19%) or sporadic background spiking activity that could not be reliably distinguished from attenuated spikes in the recordings with the lowest SNR. These sites, however, did not show any greater or lesser stability of temporal patterns of spike bursts—the principal dependent variable of this study—than did those sites with completely reliable spike identification. RA activity during singing is characterized as having high-frequency bunts of spikes organized into trains of bursts. Each burst in the train of bunts is distinguished from the others both by the pattern of spikes and the timing of the bunt relative to the syllable (Yu and Margoliash, 1996; Dave and Margoliash, 2000; Leonardo and Fee, 2005). In this study, we defined a bunt as a sequence of consecutive spikes with all interspike intervals <10 ms. This simple definition reliably identified all bunts of two or more spikes of an RA neuron during singing. In all cases, we also could readily identify a canonical sequence of bursts for each syllable (Yu and Margoliash, 19%). Aligning multiple renditions of the sequences of bunts relative to the onset of a given syllable (as in a raster plot) created stacks of bursts, with each "burst stack" associated with a particular time relative to syllable onset and a particular temporal pattern of spikes. We identified 2.1 ± 1.3 bursts for each syllable across all the neurons, with some syllables not eliciting any bunts and one neuron reliably emitting eight bunts for a particularly long and complex syllable. The principal data set consisted of 115 distinct burst stacks emitted during singing both before and after sleep by 15 RA neurons (seven birds). To compare the stability of temporal structure in premotor activ- ity in the absence of sleep, we also examined the activity of RA neurons recorded in periods of singing and/or calling that did not include sleep. We included in this data set the same 15 neurons used in the sleep analysis, separating out the pre-sleep activity and postsleep activity into distinct sessions, each of which did not include sleep (i.e., 115 bunt stacks from presleep recordings, and 115 burst stacks from postsleep record- ings, for a total of 230 burst stacks). To expand our data set to include sessions of longer duration without sleep, we included the additional 28 PA neurons recorded from 10 birds (six new, four that were also repre- sented in our sleep-inclusive recordings) in experiments where the lights were not turned out and the birds remained awake throughout, yielding an additional 321 burst stacks. Thus, this "augmented" data set com- prised a total of 551 distinct burst stacks recorded from 13 birds. During one awake-only session, we also briefly recorded one putative RA interneuron characterized by a low baseline firing rate and an espe- cially narrow spike width (0.13 ms peak to trough,compared to a range of 0.19-0.41 ms for all other RA neurons we recorded), but we did not include this unit in our analyses because of insufficient spike isolation during singing. Analysis of burst structure and definition of features and structural changes. To evaluate changes to the temporal structure of premotor bunts across many renditions, we (I) developed a procedure to align all presleep or postsleep bunts for a given bunt stack, (2) generated func- tions that captured the temporal features of the aligned bursts, and (3) evaluated the significance of any temporal or spike count differences between presleep and postsleep groups of spikes. To optimally align bunt renditions within a presleep or postsleep bunt stack, we used two procedures: /1-distance minimization (l.,- MIN), as described by Chi and Margoliash (2001), and cross-correlation maximization (CC-MAX). In both cases, the alignment of spike se- quences was accomplished by iteratively shifting each burst rendition to either globally minimize the summed L, distances (L,-MIN) or maxi- mize summed cross-correlation measures (CC-MAX) across all bunt pain, while preserving each individual burst's interspilce intervals. Pre-sleep and postsleep burst stacks were then aligned with each other according to similar procedures, with all of the bunts in each stack shifted as a whole so that the relative timing within each stack was preserved. The L, metric used in the L,-MIN method measures the difference between two spike sequences obtained by averaging over all spikes the temporal difference between each spike and its closest corresponding spike in the other sequence, so that optimal alignment would be achieved by minimizing this measure. To generate a cross-correlation measure for the CC-MAX method, we used the biweight kernel F(x) = (I — (W0)2] 2 for all < D, where D is a time window corresponding to the temporal precision of the cross-correlation measure (set to 1.5 ms, a value chosen to approximate the apparent temporal precision of RA premotor spike patterns).The total CC ofspikc trains S,,..., Sk was defined as !,,,,K(S„ S,), where K(S„ = !Rs — t) over s in S, and tin The alignment maximized the total CC by shifting each spike train S, while preserving each individual bunt's interspilce intervals. Once bursts within a stack were aligned, fine temporal structure was expressed as the tightly aligned spikes across renditions. A "feature" within a bunt was defined as a canonical spike, i.e.,a spike produced with reliable timing relative to the other spikes in the bunt across many or all renditions. To identify and quantify features, we defined for each group of spike trains an adjusted rate function, R(t) = — t11D), where s is the time of an individual spike within the spike train S, and G(x) = (1 — x2) for all Ix] < D and 0 for all Ix] > D with the predefined time window D = 1.2 ms. (Note that D = 1.2 ms results in a more precise firing rate estimate than the 1.5 ms time window used for the original bunt alignment, achieving a coarse-to-fine alignment procedure.) We then identified peaks in the rate function. This method captures the changes in features we visually observed but is sensitive to the definition of peaks in the rate function, for example, slight changes in the temporal jitter of a given spike. For a sample of N presleep spike trains, time T was identified as a feature location if it satisfied four criteria: (1) the averaged adjusted rate function had a local peak at time Tli.e.,r(7) Z r(s) for s between T ± D, where r( 7) = mean(R( Mover the sample]; (2) the peak at time Twas of significantly high amplitude compared with the variability of the rate function, Ir(T) 2 0.3 + , (0.975) X o (7), where a ( = SD(R( 7)) over the sample, and t,,,_, the inverse s-distribution function with N — I degrees of freedom]; (3) the variability of spike times within the pre- defined time window around T was sufficiently low, IsN_ ,(0.975) X a ( 7)5 D, where a (7) = SD (spike times between T ± 0)1; and (4) the average value of the adjusted rate function on either side of the peak fell off sufficiently quickly such that If] 5 2 ms, where 1 equals the maximal interval containing T over which r(s) 2 r(T)13. Under these criteria, —65% of all spikes in premotor bunts were identified with located fea- tures (4.8 ± 3.1 total spikes/burst; 3.1 ± 1.8 features/burst). We judged each burst stack as having a `structural change" across the sleep interval if three criteria were met. Pint, features in the presleep and postsleep adjusted rate functions did not align well. Each feature was evaluated to determine whether we could rule out the existence of a corresponding spike in the corresponding stack (i.e., presleep vs postsleep). If for any feature there was no corresponding feature in the corresponding stack within 0.25 ms, and there was no other peak within 0.5 ms in the opposite stack's rate function with a magnitude statistically indistinguishable from that of the feature being evaluated, then the burst stack was judged to meet this criterion. Second, there was a statistically significant change in mean spike count of at least 0.5 spikes/burst. This criterion arises from the observed loss (or, rarely, gain) in spikes across sleep intervals (see Results). Third, to reduce the effect of artifacts in alignment, structural changes were flagged only when the first two crite- ria were satisfied under both L,-MIN and CC-MAX alignment proce- dures. Overall, under both alignment procedures, 37 burst stacks satisfied the first criterion, and 60 satisfied the second, with 33 satisfying both. Thus, changes in spike timing typically were associated with changes in spike rate, but the reverse was not generally the case. Those bunt stacks found to undergo structural changes under these criteria EFTA01076048 2786 • 1. Neurosci., February 17,2010 • 3017):2783-2794 Rauske et al. • Neuronal Stability and Drift across Sleep corresponded well to those burst stacks that appeared to have altered spiking patterns under visual inspection. Analysis for separator intern& other than sleep. Sleep is a natural sepa- rator between groups of vocalizations, but we also explored whether changes to premotor activity occurred at times other than sleep. To this end, for each burst stack we sought to identify the interval between con- secutive renditions of bunts that was most likely to correspond to a change in burst structure, referring to the interval thus identified as the `separator interval." We began by measuring the similarity of all possible pairings of individual bursts within each burst stack, using theL, distance metric described above; greater L, distance implies less similarity. Then, we considered each interval between bursts as a candidate separator in- terval, except that we excluded the first four and last four such intervals to avoid boundary effects. For each candidate interval, we divided the bursts into preinterval and postinterval groups, and from the collection of can- didate intervals we identified the one interval that maximized the differ- ence between the mean L, distances of across-group comparisons (preintenel vs postinterval) and within-group comparisons (preinterval vs preinterval, or postinterval vs postinterval). This procedure tended to identify two groups of most-similar bursts,one exclusivelybefore and the other exclusively after the interval, dividing the bunt stack at a moment in time that often corresponded to a visibly noticeable change in burst structure (Fig. IA). We also used a modified procedure better suited to quantify a subset of the transitions in bunt structure. For these cases there was a distinct transition between distinct states, but with one of the states exhibiting less variability than the other. In these cases, the candidate interval that maximized the difference between mean L, distances did not always correspond to the visually observed transition. We found that for these cases, the transition typically coincided with a candidate interval that maximized the differences comparing L, variances for across-group and postinterval (or preinterval) candidate intervals as well as maximizing the differences between L, means for across-group and preinterval (or postinterval) candidate intervals. Therefine, in these cases, we designated the interval thus defined as the separator interval; in all other cases, we simply used the interval maximizing the L,-mean distances between across-group and within-group comparisons as the separator interval. Estimating occurrences of sleep-separator comparisons attributable to chance. Finally, we also developed a statistical procedure to compare the location of separator intervals in recordings that did and did not include sleep. To this end, we first identified separator intervals for the awake- only recording sessions. Then, for each bunt stack, we calculated the proportion of bursts occurring before the separator to the total number of bursts. A histogram of the resulting distribution suggested a quadratic distribution, so we used a quadratic fit to generate a baseline probability density function (PDF) (Fig. I B). The PDF estimate allowed us to test the hypothesis that the distribu- tion of L,-optimized separator intervals was the same in awake-only and sleep-inclusive recordings. In a bootstrap procedure, we sampled 115 fractions from the PDF and respectively multiplied these by the total number of renditions for each of the 115 burst stacks in our data set to get a random separator interval. We repeated this procedure 10,000 times to obtain a distribution of simulated separator intervals. This distribution was used to evaluate the likelihood that the number of separator intervals we observed to correspond with the period of sleep (either exactly or within one interval) would occur simply by chance. The quadratic shape of the PDF can be explained as follows. The greater likelihood of locating separator intervals near the endpoint of an experiment rather than in the middle is most likely attributable to the exaggerated effect of outliers on small groups of bunts. Theasymmetrical shape of the PDF (see Results) may reflect a slightly increased variability across bunt renditions later in experiments, when more time sometimes passed between singing bouts as birds became desensitized to the pres- ence of the adjacent female and tended to sing less frequently. Results We recorded from 43 RA single units while birds vocalized (sang or called; including one "double unit" that we treat equivalently to the single units in our analyses). Only a subset of these record-A II•01. post-sleep time B 15 % of bursts pre-sleep . post-sleep !HUHU . • • . . • OD ON MO • ep • II • ON' iN •• PIMIC • • • -.1k I • Man • • • • .. .. ay.,. . •• • .. . . so . • loss •• • • •• • • • • • x ,•. . • • • . similar • .. • • ... • .• • • r 1 II.1 more • • • I.' similar 0.9 mean LI distance 0.5 across-group ten roux 10 30 interval separator interval/ total # burst renditions Figure 1. Identifying intervals with possible changes to premotor burst patterns. A, Corn- parisons of pairs of burst renditions for a burst stack recorded before and after a period of sleep, using the L, distance metric. Each row and column represents a single burst rendition Dem. seated by rasters to the left of rows a above columns). Each colored box represents Mel, &stance between the bursts denoted by the given row and column according to a color map with red indicating the highest distances (less similarity) and blue representing the lowest L, &stances (more similarity). The sleep Interval is denoted by black dashed Ines. Note that burst comparisons above and to the left of the sleep.interval Imes (I.e., comparisons between preskep and postsleep bursts) show less similarity than 63 comparisom between bursts taking place exclusively before or after sleep. The graph at the bottom right shows the mean L, dis- tances between all pairs of burst renditions taking place across the separator interval (red line) &exclusively before& after the separator interval (blue line) for all possibk intervals. The sleep interval is &noted by the dashedline, where thedifferenceinmeanL i &stance between these twogroupsreaches a <leas peak; such a peak defines the optimized separator interval. 8, Esti- mated probability distreutem function for the location of the optimized separator interval in recording sessions that did not include sleep (551 burst sucks). the histogram shows the dia. tribution ofopeimized separator intervals relative to the total number &burst renditions within each recording session. A quadratic fit (line) was used to determine thePDF. ings was maintained through a period of sleep and subsequent vocalizations (Table I) (see Materials and Methods). It is likely that all of these cells were projection neurons targeting the brain- stem, given their fast (>30 Hz), regular baseline spiking activity and bursting activity during singing (Spiro et al., 1999; Leonardo and Fee, 2005). Each cell reliably burst with consistent timing relative to specific vocalizations such as a particular syllable or call; thus, raster plots of the neuronal activity aligned to vocaliza- tion onsets produced "stacks" of bursts, which were the basis for our analysis (see Materials and Methods). In the 37 cells for which we recorded singing, there were 10.1 ± 4.2 unique burst stacks EFTA01076049 Rauste et al. • Neuronal Stability and Drift across keep J. Reurovi, February 17, 2010.30(71:2783-2794.2787 Table 1. Distribution of recordings across different experimental conditions Recording session type Birds Neurons Burst stacks Sleep inclusive Tall) 7 15 115 Sleep inclusive (shod) 4 lob 83 Sleep inclusive (long) 3 5 32 No sleep° 13 43° 551 'Includesboth the pleserpenbt and pssuletpenls pleas el tit serpent:Mitt sessions as &Una no-slett moons. 'Includes cee"dothkunt °turned as a snub nese:con outatubsts. per song, and 1.9 -± 1.1 unique burst stacks per call. For the remaining six cells for which we only recorded calls, there were one to two unique burst stacks per call. We examined the effects of sleep on vocalization -related neu- ral activity in adult male zebra finches under two protocols: short, interrupted periods of sleep and full, uninterrupted nights of sleep. The distribution of recording sessions according to exper- imental protocol is reported in Table I. Birds in the short-sleep design (at = 10 neurons in 4 birds) experienced a period of dark- ness lasting 90-179 min (average, 136 -± 31 min), whereas the birds in the second design (n = 5 neurons in 3 birds) experienced a full 8 -10 h of darkness. For all birds, during the first 2-10 min of darkness, birds typically rapidly transitioned between short periods of wake and sleep. Initially, in some cases, sudden awak- enings apparently resulted from playback of the bird's own song that was presented during short-sleep sessions (see Materials and Methods), but birds quickly habituated to the song playback and began to reliably sleep through the stimulus. Birds were judged to begin an extended period of sleep when a full minute passed with no two consecutive 3 s intervals classified as "awake" (see Mate- rials and Methods) (see supplemental Fig. 1, available at www. jneurosci.org as supplemental material). Based on the measure of spontaneous activity, the onset of extended periods of sleep began 10.5 -± 8.8 min (range, 1.3-29.5 min) after the start of subjective night, and represented 78.8 -± 10.5% (range 67.8 —94.9%) of the total dark phase after sleep onset. Thus, whereas the recording situation probably disrupted the animal's total sleep and sleep architecture to some degree, each animal experienced a consid- erable amount of sleep including extended periods of uninter- rupted sleep. Premotor bursts change after sleep The structure of RA premotor bursts is highly conserved in songs directed toward females, as a bird repeats the same stereotyped syllable within and across songs (Yu and Margoliash, 1996). We frequently observed, however, changes to the structure of these bursts after sleep. The only systematic change across time de- scribed previously in RA premotor bursts is the submillisecond magnitude temporal drift in the timing between bursts (Chi and Margoliash, 2001). In contrast, in the present data, many clear and persistent changes in premotor patterns associated with the sleep interval were apparent by visual inspection beginning with the first song renditions after sleep; these were marked by the elimination or, rarely, addition of spikes in the postsleep pattern (Fig. 2). In cases where bursts had fewer spikes after sleep, the decrease in spike number was often accompanied by an increase in interspike interval (Fig. 2A, B). Typically it was difficult or impossible to identify a specific spike from the presleep bursts that was eliminated after sleep. Instead we saw a restructuring of the entire burst. Once a change occurred, it tended to be stable. Clear changes to burst structure after full nights of sleep were obvious in many cases. The changes persisted for as long as we could hold the recording —in one case, for several hours after waking (Fig. 3). Whatever the cellular or network effects that led to these changes, they achieved their suprathreshold effects during sleep or imme- diately after awakening, and persisted thereafter. To quantitatively assess these changes in burst structure, we aligned all presleep and postsleep bursts for each of the 115 burst stacks using two algorithms —Le-distance minimization and cross-correlation maximization —converted these into probabi- listic rate functions, located features within those functions, and identified reliable changes in features, which we call "structural changes" (see Materials and Methods). Using these criteria, we found that for recordings spanning a sleep interval, 33 of 115 burst stacks showed structural changes (Fig. 4). These 33 burst stacks were distributed across 10 of 15 neurons recorded across sleep, with each neuron exhibiting one (four neurons), two (three neurons), or six (two neurons) bursts with structural changes, but with one neuron exhibiting 11 bursts with structural changes. The statistical significance of all the results that follow was main- tained even with the neuron with 11 bursts with structural changes removed. Considering the sequence of syllables within motifs or the sequence of bursts within a syllable, there was no apparent ten- dency for structural changes to be associated with bursts that occurred in any particular syllable within the motif, or within any particular burst within a syllable. There was also no clear differ- ence in the number of changes in burst stacks associated with contact calls (5 of 21; 24%) compared to those associated with song syllables (28 of 94; 30%; p = 0.79, Fisher's exact test). We tested the hypothesis that the two different experimental designs affected the rate of occurrence of structural changes. There were no significant differences in the frequency of burst changes between short-sleep and long-sleep birds. The frequen- cies of structural changes [27% (22 of 83) short sleep, 34% (1 I of 32) long sleep; p = 0.40; x2 = 0.701 were similar under both experimental conditions, suggesting that the truncated period of sleep and the presence of auditory stimulation were not signifi- cant factors in driving premotor plasticity in RA neurons. Although rare, there were also examples of structural changes that occurred during the subjective day. For this analysis, we used an augmented data set including a number of daytime-only re- cordings (see Materials and Methods). To quantitatively assess the rate of structural changes while the birds were awake, we simply chose the longest vocalization -free interval with a suffi- cient number of vocalizations (ri a 8) both preceding and follow- ing the interval, and compared the bursts before and after this interval. Across 551 distinct burst stacks in the augmented data set (43 neurons, 13 birds; see Materials and Methods) (see Table 1 ), the rate of structural changes that occurred in recordings that did not span a sleep interval was much lower (3.3%; 18 of 551) than the rate of changes across the sleep interval (28.7%; 33 of 115), and this difference was significant ( p < 0.001; 1(2 = 87.0). Finally, since the recordings that included a period of sleep were generally the ones of greatest duration, we also tested whether the more frequent occurrences of changes to burst pat- terns in these recordings resulted from the additional passage of time rather than the presence of sleep. We selected the longest (100-360 min) awake-only recording sessions (n = 8, with 71 distinct burst classes). For each of these recordings, we defined an artificial separation interval (1.5-3 h) of similar duration to the dark periods in the shorter-sleep recording sessions and then compared the two sets of recordings. Even when controlling for EFTA01076050 27118 • .I. Neurosci., FebnNry 17,2010.3017):2783-2794 Retake et al. • Neuronal Stability and Drift across Sleep the passage of time, burst patterns under- went many more changes across sleep than across wakefulness. During short sleep-inclusive recording sessions, ap- proximately one-fourth (22 of 83) of bursts exhibited structural changes, most of which were obvious under visual inspec- tion. In the recordings with the imposed artificial separation interval, no structural changes were obvious under visual in- spection, and a much lower number of bursts (4 of 71; 6%; p C 0.001, Fisher's exact test) exhibited structural changes. Changes in premotor activity occur across sleep-inclusive intervals The preceding results show that using sleep as the separator interval reliably identifies changes in RA burst patterns, but this does not rule out the possibility that the changes actually tended to occur just before sleep (which could occur if the bird could anticipate the onset of the sleep period) or just after sleep. We formulated this hypothesis rigorously as a test of whether there are previously undetected changes to premotor burst patterns ex- pressed as a transition between distinct states, in which a previously stable temporal spiking pattern is replaced with a different pattern which then persists throughout the subsequent songs. We then compared those transitions to the occurrence of sleep. To test this hypothesis, for each of the 115 distinct burst stacks associated with the 15 neurons, we identified a separator interval using an algorithm designed to identify the interval most likely to rep- resent a transition to an altered burst pattern (see Materials and Methods), ig- noring when sleep actually occurred. A large proportion of the separator intervals thus identified occurred close to sleep: 30 of 115 separator intervals (seven neurons, four birds) either coincided with sleep or fell between the last two bursts before sleep or the first two bursts after sleep. To assess whether the degree of coincidence between the separator intervals and sleep was attributable to chance, we generated predictions of the underlying distribution of separator intervals using the premotor activity of RA neurons recorded in the contiguous periods that did not include sleep (see Materials and Methods). We then ran- domly sampled from the predicted distribution 10,000 times for each of the set of 115 burst stacks to estimate the distributions of separator intervals predicted by chance if the presence of sleep did not bias the locations of the separator intervals. Overall, in the simulated data, the average number of exact matches between separator intervals and sleep was 2.5 -± 1.6 burst stacks (of 115 total), with a maximum of 11 such matches—just half of the 22 exact matches between separator intervals and sleep C • before sleep after sleep B 71 1-7 before sleep ..4.4,44444.wavywo after I sleep i dui lia it 11 Slit [11110111 11,111! III 111111 r • ECM 144 10 Ron 2. Changestotemporal structure of RA premotor buntsacrossa period of sleep. A, Recordoms from an RA neuron that produced fewer spikes in premotor bursts associated veith a song syllable alien 2 h skep pftiod. Top, pectrograph of song aligned with simultaneous recoil of premotor neuronal activity. Middle, Recordings of neuronal activity d ring three renditions of the song syllable. Bottom, Neuronal activity during three more renditions of the same song viable ahe sleep. The kftmost vertical dashed hoe follows the fourth spike in al presleep bursts but precedes the fourth spike in all postsleep bursts, and the rightmost dashed line does the same with the eighth spikes. Note that whereas both weleep and postsleep bursts inconsistently Mdude an extra spite at the end, the postsleep hunts consistently produce one spike fewer than presleep bursts, oith an accompanying gap kit the middle of each bwst (arrows). 8, Recordings from another RA neuron that produced fewer premotor spikes during produc- tion eta contact call after a 2.5 h sleep period. The dashed line separates fourth spikes n each burst as per A.C. Recordings from a third RA neuron that showed extra spikes in two remoter burstsassodated with a song syllable aftera 2 h sleep period kale bars: A, top, 250 ms; bottom, 10 ms; I, top, 100 at; bottom, 10 ms; C, top, 300 ms; bottom, 25 at. we observed in our actual recordings. Furthermore, the same sampling procedure applied to the 93 burst stacks not showing exact matches between separator intervals and sleep yielded an average of 3.8 ± 1.9 examples of separator intervals occurring within -±1 interval of sleep. In contrast, there were eight such examples in the actual data, and only 3.7% of the random trials had eight or more such examples (Fig. 5A). In total, the 30 sleep- separator coincidences (exact or within one interval) we observed in the actual data set were much more than was ever observed in the simulated data (maximum, 17). These distinctions were also EFTA01076051 Rauske el al. • Neuronal Stability and Drift across Sleep .I.I0eurovi, February 17, 2010 30(7):2783-2794 • 2789 before sleep after sleep Figure 3. Persistent structural change to premotor bursts in a pair of RA neurons after a full night of sleep. Left, Raster plots of spiking activity during production of the song motif before(top)and after (middle)sleep. This site was the •double unit' described in the text, in which the activity of a pair of neurons could reliably be distinguished from the background activity but not horn each other; the data were treated equivalently to single unit data. Note the stability of the burst patternswithin the before.sleep and after•sleep groups, even over long periods of time (before sleep, 171 min; after sleep, 459 min). Rasters are aligned within each group using the L rminimizatim method, and the groups are aligned with a spectrographof the song motif (bottom). Right, Finer temporal detail. The persistent loss of spikes from the middle bursts is dear, and the temporal pattern of the bursts fails to return to the presleep pattern even after several hours of postsleep singing. Scale bars: Left, 100 ms; right, 25 ms. maintained when assessing coincidence between the separator interval and sleep with a 30 s criterion (Fig. 5B). A similar analysis comparing short-sleep recording sessions to awake-only record- ing sessions of similar duration confirmed this result (see supple- mental material available at www.jneurosci.org). Finally, the distribution of separator intervals drawn from the actual data that did not coincide with sleep (i.e., the 85 of 115 separator intervals differing from sleep by at least two intervals) did not exhibit significant difference from the corresponding simulated distribution ( p = 0.14, Kolmogorov—Smirnov test). Considered together, the overall distribution of separator intervals drawn from the actual data was significantly different from the simula- tion distribution ( p C 0.01, Kolmogorov—Smirnov test). We note that our technique for identifying separator intervals depends on statistical comparisons of the L, measure across pop- ulations of bunts, and therefore the estimates of the timing of putative changes to burst structure will exhibit noise that arises from rendition -to-rendition variability of premotor bursts in RA neurons. In fact, simply using sleep as an indicator for possible changes proved a more effective strategy for identifying structural changes, as only 27 of 115 bunt classes exhibited such changes across algorithm -identified separator intervals, compared with the 33 changes observed across sleep. Since sleep was more reli- able for locating structural changes than was the algorithmic ap- proach ignoring sleep, this supports the conclusion that under the conditions of our experiment, sleep itself induced discrete changes in premotor activity. Structural changes tend to duster across song In those neurons exhibiting structural changes in multiple burst stacks, we ob- served that changed bunts had a signifi- cant tendency to cluster together. Across the five neurons with changes to more than one burst stack within the song mo- tif, the spread of changed bursts (i.e., the interval between the first and last changed bursts) covered an average of 61% of the total number of bursts within each motif. However, given the number of changed bursts for each neuron, a random distri- bution of burst changes (determined by shuffling the distribution of changes within each neuron 100,000 times) would be expected to yield an average spread of 74 -± 7% of the total, and spreads as low as or lower than the observed 61% occurred in only 1.9% of the shuffled trials. The clustering was even more striking when excluding the neuron in which nearly ev- ery burst changed: in this restricted data set, the average spread between the first and last changed bursts covered 53% of the total number of bunts. This compares to an expected coverage of 69 -± 9% in the corresponding shuffled trials, with only 1.6% of shuffled trials showing coverage as low as actually observed. Across all neu- rons with multiple bunt changes, nearly one-third (6 of 19) of