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1Institute of Neuroinformatics, Universität Zürich/Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland; and 2Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
Submitted 22 May 2006; accepted in final form 23 August 2006
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ABSTRACT |
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INTRODUCTION |
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; Nadasdy et al. 1999; Palchykova et al. 2006; Walker and Stickgold 2006). Premotor activity in the songbird nucleus robustus of the arcopallium (RA) is correlated with song vocalizations on a temporal scale in the submillisecond range (Chi and Margoliash 2001; Leonardo and Fee 2005; Yu and Margoliash 1996). RA neuron activity has also been recorded during sleep, where it strongly resembles song-related activity (Dave and Margoliash 2000; Dave et al. 1998). Song and sleep-related spike patterns are believed to be generated by common neural mechanisms because during both singing and sleep, RA spike patterns are driven by sparse sequences in HVC (high vocal center) projection neurons (Hahnloser et al. 2002, 2006). Given that HVC projection neurons are of such importance to RA activity in the singing and sleeping states, we speculate that projection neurons upstream of HVC maybe of similar importance to the generation of sparse HVC sequences. Here we are interested in elucidating the feedforward drive provided to HVC during sleep from the nucleus interface of the nidopallium (NIf).
HVC receives input predominantly from the thalamic nucleus uveaformis (Uva) in the thalamus, the medial magnocellular nucleus of the anterior nidopallium (MMAN), and NIf (Fortune and Margoliash 1995; Nottebohm et al. 1982; Rosen and Mooney 2006; Vates et al. 1996). NIf is embedded in the auditory field L complex and is believed to be important for auditory–motor interactions in the song-control system. Lesions of NIf have been found to abolish selectivity in HVC neurons to acoustic playback of the bird's own song (BOS) (Cardin et al. 2005; Coleman and Mooney 2004; Janata and Margoliash 1999). Similarly, spontaneous activity in HVC of anesthetized birds is strongly reduced after reversible or irreversible NIf lesions (Cardin et al. 2005; Coleman and Mooney 2004).
We are interested in elucidating the sleep-related activity patterns of NIf neurons projecting to HVC (NIfHVC neurons). These NIf neurons were previously identified in anesthetized birds and found to be more densely firing during BOS playback than HVC projection neurons (Coleman and Mooney 2004). Our goal is to identify potential differences in the manner in which these NIf neurons drive activity patterns in downstream areas in the premotor and the anterior forebrain pathways (Okuhata and Saito 1987) involved in song production and song learning (Bottjer et al. 1984; Doupe 1993; Doupe et al. 2004; Scharff and Nottebohm 1991; Sohrabji et al. 1990). To characterize the relationship of sleep-related activity patterns in higher premotor brain areas, we study coherency functions from paired recordings of NIfHVC neurons and HVC projection neurons and, further, study the effects of reversible pharmacological inactivation on sleep-related spike trains. Our study reveals new insights into the dynamics of sleep-related activity patterns. Parts of this presentation were previously published in abstract form (Fee et al. 2002).
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METHODS |
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). Subjects
Zebra finches (Taeniopygia guttata) were obtained from commercial suppliers (Old Bridge, NJ and Animal Diffusion, Villarimboud, Switzerland). Animals were maintained on a day–night reversed 12-h light cycle to assist in obtaining sleep during daytime experimental sessions. Data were taken from a total of 36 adult zebra finches (>90 days).
Surgery
Surgery began approximately at the onset of the night cycle. Birds were anesthetized with 1–3% isoflurane in oxygen. A small hole (about 200 µm) was made in the dura over each area and the uncovered brain was protected with 2% low-melting agarose (Sigma). Bipolar stimulation electrodes (Teflon-insulated 50-µm-diameter stainless steel wire spaced 0.5 mm apart) were inserted into HVC and RA or Area X and secured to the scull with dental acrylic. Wound margins were treated with lidocaine gel. The animal was placed in a small foam restraint and subsequently moved to the recording apparatus without further anesthesia. In some of the experiments, to promote sleep, the bird was given a single dose (1–10 µg) of melatonin (Sigma) right after surgery, injected subcutaneously in phosphate-buffered saline.
Electrophysiology
ANTIDROMIC ACTIVATION.
Bipolar stimulation electrodes in HVC were used for antidromic identification of NIf neuron type and RA (or Area X) stimulation electrodes were used for identification of HVC neuron type (Fig. 1A). Electrical stimulation was produced using an isolated stimulation unit (AMPI) with intensities varying from 50 to 500 µA. For most experiments, single monophasic current pulses of 0.2-ms duration were used as stimuli. Single or paired single-unit recordings were made in HVC and NIf. High signal-to-noise (>10:1) recordings were made using sharp glass microelectrodes (5–15 M
, borosilicate, 1.0 mm OD, 0.7 mm ID) pulled on a vertical electrode puller (Model 730, Kopf Instruments) and filled with 2 M NaCl. Signals were amplified using a Neurodata IR285 (Cygnus Instruments) or Axoclamp-2B (Axon Instruments) intracellular amplifiers. Custom electronics were used for additional amplification (gain of 100) and filtering (300-Hz high-pass five-pole Bessel filter, 10-kHz low-pass constant delay filter). Extracellular signals were digitized to 16-bits precision at a sampling rate of 20 kHz and stored in blocks of 200 s on a Pentium-based PC running custom LabVIEW software (National Instruments).
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platinum tungsten electrode. Every 20 µm between 2- and 3-mm penetration depth, we recorded the responses to 30 HVC stimulations at 1-s intervals (about 200-µA amplitude). After assessing the extent of NIf by observing the stimulation responses on an oscilloscope, we made two electrolytic lesions above and below NIf, 900 µm apart. Lesions were made by injecting a constant current of 15 µA for about 12 s. These lesions were later identified in histological sections to precisely locate the electrode track within sagittal slices of the brain. Stimulation response shown in Fig. 1B was quantified by the root-mean-squared (RMS) voltage amplitude in the 1- to 7-ms interval after stimulation onset (averaged over the 30 stimulations per site).
NIF NEURON IDENTIFICATION.
NIf neurons were found and isolated using ongoing 1-Hz stimulation in HVC to elicit spike responses. Once a cell was isolated, stimulation threshold current was measured and stimulus intensity was set between 10 and 50% above threshold to produce a reliable response. Continuous analog records were acquired for 60 stimuli at 1-s intervals. The response latency of an antidromic spike is defined as the interval between stimulus onset and the first evoked spike. Latency variability is defined as the SD of response latency. We estimate that the contribution to the latency variability arising from our fixed sampling rate of 20 kHz is <16 µs (dashed line in Fig. 1E). Because neurons in neighboring field L may also project to HVC (Fortune and Margoliash 1995), we tried to avoid recording from field-L neurons by recording only from those NIf regions with very dense multiunit response (hash) to stimulation, shown to correlate well with NIf extent (Fig. 1B). Furthermore, we observed that field L neurons projecting to HVC (LHVC neurons) had longer response latencies to HVC stimulation than did NIfHVC neurons. Also, LHVC neurons showed strong phasic responses to brief sound clicks, whereas NIfHVC neurons did not. For the NIf neurons classified as interneurons, we were similarly conservative and applied this classification only to neurons found at penetration depths within the electrophysiologically identified borders of NIf.
REVERSIBLE LESIONS IN NIF.
The effect of lidocaine and muscimol injections in NIf on sleep-burst rate in HVC interneurons was examined by microinjection of lidocaine or muscimol in 2% phosphate-buffered saline during single-unit recordings of HVCI neurons. Injections were made from pulled glass pipettes (50-µm tip size) using a pressure-injection system (Pico Spritzer). Injected volumes were 10–100 nl. Control injections were made in NIf with phosphate-buffered saline. Typically, injection pipettes were placed in the center of NIf after an electrophysiological recording session during which the extent of NIf was well characterized by multiple electrode penetrations. To measure the effect of the injection, we counted the number of bursts in the HVCI neuron in the minute before and after the injection. Statistical significance of burst-rate differences before and after the injection was assessed using the Mann–Whitney U test (P < 0.001). In one case the bird woke up right after the injection (by observation of eye opening). Because the burst rate in HVC neurons is known to be highly state dependent (Hahnloser et al. 2006), data from this trial were discarded.
HISTOLOGY. After each experiment, the animal was killed by intramuscular injection of 20% urethane or nembutal. For the NIf identification experiment, birds were transcardially perfused with 4% paraformaldehyde (PFA) in 25 mM NaPO4 buffer. The brain was removed for histological examination of Nissl-stained (or unstained) slices to verify the location of stimulating and recording electrodes and drug injection sites. All experiments were carried out in accord with protocols approved by the local IACUC and the Veterinary Amt of the Canton of Zurich, Switzerland.
Spike train analysis
All spike train analysis procedures were implemented with custom-made Matlab (The MathWorks, Natick, MA) scripts.
INSTANTANEOUS FIRING RATE. One way of representing the activity at time t of a neuron is by the instantaneous firing rate R(t), a continuous function defined by the inverse of the interspike interval (ISI) surrounding time t.
BURST RATE. We identified a group of at least two spikes as a burst if the instantaneous firing rate continuously exceeded 100 Hz. The burst rate is defined as the number of events per unit time in which the instantaneous firing rate crosses the 100-Hz barrier from below.
ISI PROBABILITY DENSITY FUNCTION (PDF).
For each neuron A we computed the ISI pdf hA(
) as a basic characterization of its spike train ( stands for the ISI). First we computed the ISI histogram with bin centers i, chosen on a logarithmic scale (i = 1, ..., 100). The ISI pdf hA(i) is simply a normalized ISI histogram satisfying 
i hA(i) = 1.
AUTOCOVARIANCE FUNCTION.
The autocovariance function CAA(t) of a spike train 
A(t) (modeled as a sum of delta functions) is a measure of spike density fluctuations. It is computed as
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is the mean firing rate of neuron A and T is the duration of the recording (the denominator T–|t|results in an unbiased estimation of autocovariance at large times t << T). The first term on the right side of Eq. 1 is known as the autocorrelation function. By subtracting the term 2 in Eq. 1, the asymptotic value of the autocovariance function is zero. For better visibility, we plotted autocovariance functions on a logarithmic timescale ti in Fig. 3, C and D by integration between adjacent points, i.e., the discrete function CAA(i)=
titi+1 CAA(t)dt is plotted.
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). Renewal spike trains have no memory because each ISI is a random variable that is independent of the previous ISI. To perform this test, we computed renewal autocovariance functions CAAR(t) derived from the ISI pdfs under renewal assumptions (here superscript R stands for renewal) and compared these functions to the measured autocovariance functions. For a derivation of CAAR(t) see, e.g., Gerstner and Kistler (2002). Any deviation of CAA(t) from CAAR(t) is indicative of a memory in the spike train, causing ISIs to be conditional on previous ISIs. A deviation at time ti is significant if CAAR(i) differs from CAA(i) by >3SE of CAA(i) (the SE was computed by jackknifing; see METHODS on the coherency analysis).
We also tested whether autocovariance functions differed from renewal functions on the population level. Average autocovariance functions 
CAA(t)
A and CAAR(t)A for the two NIf neuron types are shown by the full and dashed lines in Fig. 3, C and D. At each time ti we tested whether the autocovariance and renewal functions at that time had identical medians using the Wilcoxon rank-sum test (P < 0.01). Time intervals where deviations were significant are indicated by thick horizontal bars in Fig. 3, C and D.
COHERENCY ANALYSIS.
We quantified correlations between spike trains in simultaneously recorded cells by the coherency function 
AB(t), which is similar to the cross-covariance function, but includes an additional normalization by the autocovariance functions of the two spike trains. This normalization serves to discount for correlations arising from bursting tendencies of neurons (Kimpo et al. 2003; Thomson and Chave 1991). In the frequency domain 
, the coherency function AB() is computed by normalizing the cross-covariance function CAB() between neurons A and B by the square roots of the autocovariance functions
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AB(t) by inverse Fourier transformation of AB(). As in our previous study (Hahnloser et al. 2006), we smoothed the coherency function AB(t) by convolution with a Gaussian windowing function of 4-ms SD. Finally, the smoothed coherency functions were downsampled to 1-ms temporal resolution by summing over coherencies in 1-ms bins. Our findings do not critically depend on the parameter values for smoothing and downsampling.
To depict the typical coherency function between neuron types, we plotted the average coherency function AB(t)AB (Figs. 6, C and E and 7, B and D).
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ABi(t) is the coherency function of the jackknifed data with the ith window missing, and 
AB(t) = ABi(t)i is the average coherency function of the jackknifed data.
We set the significance threshold of measured coherencies to 3
J(t), shown by dashed lines in Figs. 6 and 7. This threshold corresponds to a roughly 99% confidence for the significance test. Whenever the coherency function exceeded the significance threshold, a peak emerged. The distributions of peak amplitudes versus peak latencies are shown in the summary plots beneath the average coherency functions in Figs. 6, C and E and 7, B and E.
We also analyzed coherency functions on a population level, to see whether they had some characteristic shape. For all neuron pairs of given types, the average coherency function was compared with the average significance threshold shown by dashed lines in Figs. 6, C and E and 7, B and D. Characteristic peaks in average coherency functions were found by testing whether differences between the coherency functions and the significance thresholds had zero median using the Mann–Whitney signed-rank test (P < 0.01). Time intervals where the median coherency function was significantly larger than the median renewal function are indicated by thick horizontal bars in Figs. 6C and 7D.
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RESULTS |
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NIf localization
NIf is surrounded by auditory areas of the field-L complex (L1, L2, and L3). Because neurons in field L1 and L3 have projection targets in the shelf of HVC, one could imagine confounding them with NIf neurons when searching for antidromic responses to HVC stimulation. However, there are cytoarchitectural and morphological differences between NIf and areas of the field-L complex. Cytoarchitectural studies revealed increased cell density in NIf compared to that in L1 and L3, and morphological studies revealed differences in predominant Golgi cell types found in NIf and field L (Fortune and Margoliash 1992, 1995). Based on these studies we expected to find increased density of extracellular responses to HVC stimulation when placing the recording electrode inside NIf.
By quantifying the multiunit response density (stimulation hash) by the root-mean-squared (RMS) voltage trace in a small time interval after HVC stimulation, we indeed found a robust increase in RMS voltage when the recording electrode was positioned within NIf (Fig. 1B). In this experiment, we verified the passage of the electrode track through NIf by examining histological sections of the brain in which NIf was retrogradely labeled by fluorescent indicator (see METHODS). In the following experiments, we used the clearly visible increase in stimulation hash at short stimulus latencies as an operational assessment of NIf extent. All single-unit recordings were performed at penetration depths within this antidromically characterized region.
Classification of NIf neurons
We identified two distinct NIf response patterns to HVC stimulation (Fig. 1, C and D). Putative HVC-projecting NIf neurons responded to low-intensity stimulation (50–500 µA) in HVC with a single spike. Near-threshold stimulation produced latencies to the first spike between 0.9 and 3.2 ms (average 1.6 ± 0.6 ms, n = 20) with small latency variability in the range 14–69 µs (37 ± 16 µs, n = 20, Fig. 1E). Putative NIf interneurons responded to low-intensity stimulation in HVC, with an increasing number of spikes for increasing stimulus intensity. Near-threshold stimulation produced latencies to the first spike ranging from 1.8 to 5.4 ms (mean 3.2 ± 1.4 ms, n = 5) and a large latency variability in the range 236–887 µs (559 ± 317 µs, n = 5, Fig. 1E). On the basis of these results, we infer that the neurons with small latency variability were HVC-projecting (NIfHVC) neurons. For simplicity, we refer to the neurons with large latency variability as NIf interneurons (NIfI neurons), although we cannot rule out that these neurons project outside of NIf. In agreement with this classification, in all cases tested (n = 20), we found that neurons with latency variability <100 µs (putative NIfHVC neurons) exhibited spike collisions when stimulated immediately after a spontaneous spike (Fig. 1C, bottom). Neurons with latency variabilities >100 µs (putative NIfI neurons), on the other hand, did not exhibit collisions (Fig. 1D, bottom). The vast majority of neurons isolated within the borders of NIf, as defined by the presence of antidromic hash, had small-latency variability and had firing patterns as described below for NIfHVC neurons. Long-latency variability neurons were rarely found and all of these had firing patterns similar to those described below for NIfI neurons. In the subsequent experiments, we identified each recorded NIf cell by its antidromic stimulation response, distinguishing NIfI neurons from NIfHVC neurons by their differences in spike latency variability.
The spike widths, measured at 25% of the peak amplitude, were smaller for NIfI neurons (0.16 ± 0.02 ms, range 0.13–0.19 ms, n = 9) than they were for NIfHVC neurons (0.20 ± 0.04 ms, range 0.13–0.32 ms, n = 51, Fig. 1F). However, based on the considerable overlap between spike width distributions, we were unable to classify neuron type based on spike width or even spike waveform (using principal component analysis).
In many simultaneous recordings in NIf and HVC, we also identified HVC neuron type by RA stimulation. In the course of these experiments, we found two neurons in NIf (in two birds) that projected both to HVC and to RA, as judged by the spike collision test. However, we were not able to determine whether these neurons formed monosynaptic connections with both HVC and RA neurons or whether the axonal pathways of these HVC-projecting neurons simply passed in close proximity to RA, but without targeting RA neurons. Note that RA has not been identified as a projection target of NIf neurons in the literature.
Spike train properties of two NIf neuron types
During sleep, a large fraction of spikes in both NIf neuron types fell into high-frequency bursts (Fig. 2, A and B). Nevertheless, NIfHVC and NIfI neurons differed considerably in their spontaneous firing patterns during sleep. For example, the average firing rate of NIfHVC neurons was 5.4 Hz, whereas that of NIfI neurons exceeded 27 Hz. NIfHVC neurons fired fewer bursts per second and exhibited larger average instantaneous firing rates during bursts than did NIfI neurons (Wilcoxon rank-sum test, n = 117 NIfHVC neurons and n = 13 NIfI neurons, P < 0.01). Average firing rates, firing rates during bursts, burst rates, burst widths, number of spikes per burst, and the fraction of total spikes that are part of bursts are summarized in Table 1.
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). We previously identified a suitable timescale to measure these firing-rate fluctuations to be 3 s. When analyzed in similar 3-s windows, we did not find similarly large fluctuations of firing rates in NIf neurons. Average firing rates of NIfHVC neurons were distributed in the range from 0 to 
22 Hz (filled circles in Fig. 2C). This range of firing rates is comparable to that of HVCX neurons, an HVC neuron type of average firing rate less than a third that of NIfHVC neurons. NIfI neurons rarely displayed firing rates <5 or >50 Hz (Fig. 2D). In comparison, HVCI neurons, despite having half the average firing rate, achieved about twice the range of NIfI firing rates. In Fig. 2, C and D, we also show firing-rate distributions of NIf neurons in burst spike trains (spike trains for which all single spikes not part of a burst have been removed for the analysis), verifying that on a coarse timescale, small firing-rate fluctuations also applied to bursts. This lack of extensive firing-rate fluctuations in NIf motivated us to measure spike train statistics in NIfHVC neurons also in awake birds, as judged by their open eyes after touching their tails (n = 3 birds). In data from 3-min recording sessions, we found that firing rates and burst rates of NIfHVC neurons in the awake state were not statistically different from the firing rates measured during sleep (Wilcoxon rank-sum test, n = 5 NIfHVC neurons, P < 0.01; Table 1). Note that in our previous sleep studies in HVC and RA, we never observed bursts in awake birds in any neuron type.
ISI and autocovariance functions
We decided to further study NIf spike trains and the biophysical mechanisms responsible for their generation by examining ISI probability density functions (pdfs), and autocovariance functions. These functions are computed and analyzed in the following.
The average ISI pdf of each NIf neuron type is bimodal, with a common first peak corresponding to the ISIs during bursts and a second peak roughly corresponding to the interburst interval (Fig. 3, A and B). The average ISI pdf of NIfHVC neurons is reminiscent of that of HVC projection neurons because these neurons also emit virtually no spikes at an ISI of about 10 ms (Hahnloser et al. 2006). Similarly, the average ISI pdf of NIfI neurons is reminiscent of that of HVCI neurons.
In HVC projection neurons there is evidence of intrinsic (not synaptically driven) bursting as judged by the stereotypy of recorded sleep bursts (Hahnloser et al. 2002), whereas in HVC interneurons and RA neurons, there is no such evidence (Dave and Margoliash 2000). We investigated the burst stereotypy of NIf neurons by computing autocovariance functions. In 83 of 117 NIfHVC neurons, we found an oscillatory behavior of autocovariance functions on a very short timescale, with an example shown in the inset of Fig. 3C. This oscillatory behavior is a consequence of stereotyped burst patterns and suggests a cellular rather than a network mechanism for burst generation in NIfHVC neurons. Only one of 13 NIfI neurons showed an oscillatory autocovariance function, thus providing weaker evidence for intrinsic bursting mechanisms in NIfI neurons (a typical nonoscillatory autocovariance function is shown in the inset of Fig. 3D).
The simplest generative process of a spike train with a given ISI pdf is the renewal process, according to which each ISI is drawn randomly and independently of other ISIs from a fixed ISI pdf. By exploring whether NIf neuron spike trains are compatible with a renewal process for their generation, we derived from their ISI pdfs expected autocovariance functions under renewal assumptions (see METHODS). By comparison with these renewal (autocovariance) functions, we found further evidence for intrinsic bursting tendencies in NIfHVC neurons. Of the 109 NIfHVC neurons tested, 57 exhibited autocovariance functions that were smaller than the equivalent renewal functions somewhere in the time interval 3–45 ms (jackknife, P < 0.01). Other than these cases, no significant differences with renewal statistics were observed. This absence of long sequences of short ISIs suggests a limitation of NIfHVC neurons to produce long bursts. In this respect, NIfHVC neurons are similar to HVC projection neurons, in which a similar trend was seen.
A different behavior was seen in NIfI neurons. Of the 13 NIfI neurons tested, 10 exhibited autocovariance functions compatible with renewal assumptions and three exhibited autocovariance functions larger than renewal functions somewhere in the interval 3–31 ms. This behavior of NIfI neurons is reminiscent of RA and HVCI neurons, in which bursts were also found to be longer than expected by renewal statistics.
Given these diverse behaviors of individual NIf neurons, we also tested their compatibility with renewal statistics on the population level. We found that the median autocovariance function of NIfHVC neurons was smaller than the median renewal function in the time interval 6–30 ms (Kruskal–Wallis test, P < 0.01). However, the median autocovariance function of NIfI neurons was not significantly different from the median renewal function (Kruskal–Wallis test, P < 0.01, Fig. 3D). In conclusion, renewal tests in two NIf neuron types reveal differences between their burst-generation mechanisms. Some form of depressive dynamics seems to shorten NIfHVC neuron bursts, whereas no such mechanism appears to apply to NIfI neurons.
NIfHVC neurons display a firing decrement in their autocorrelation function. In the range of about 10–100 ms, the average autocorrelation function of NIfHVC neurons clearly dips below its asymptotic value (Fig. 3E). In this time range, NIfHVC neurons exhibit a reduced spike probability down to 40% of baseline. We have not observed such a firing decrement in any other neuron type in NIf, HVC, RA, or lateral magnocellular nucleus of the anterior nidopallium (LMAN); it may represent a soft refractory period unique to NIfHVC neurons.
Reversible lesions in NIf
To determine the role of NIfHVC population activity for the generation of sleep bursts in HVC, we examined the effect of reversible inactivation of NIf while recording activity in single HVCI neurons (Fig. 4). When we injected the sodium-channel blocker lidocaine into NIf, the spontaneous bursting of HVCI neurons immediately stopped (Fig. 4A) and recovered after about 3–4 min (Fig. 4A, bottom). The HVCI neuron burst rate in the 60-s interval after the injection was significantly smaller than that in the 60-s interval preceding the injection (Mann–Whitney U test, P < 0.01, n = 3/3 birds, three injections per bird, Fig. 4C). Saline injections did not lead to a significant change in HVCI neuron burst rates (Mann–Whitney U test, P < 0.01, n = 3/3 birds, three injections per bird). We realized that a caveat of our lidocaine injections was that they might also have affected fibers of passage from Uva to HVC. To restrict the effect of injections to neural dendrites and somata in the NIf region, we also injected the -aminobutyric acid type A (GABAA) agonist muscimol into NIf. Similarly as for the lidocaine injections, we observed an immediate and significant reduction in HVCI neuron burst rate in the minute after the muscimol injection (Mann–Whitney U test, P < 0.01, n = 3/3 birds, three injections per bird, P < 0.01, Fig. 4B). Recovery of burst rates was not observed within more than 20 min after muscimol injections. Despite these reduced burst rates, we found that HVCI neurons kept spiking after NIf inactivation (see Fig. 4, A and B), suggesting that lidocaine and muscimol did not leak into HVC. Furthermore, the average firing rate of HVCI neurons in the 50 s after lidocaine or muscimol injections into NIf was 10.2 ± 5.4 Hz and so was smaller than the typical firing of HVCI neurons during sleep: 15 ± 9.0 Hz (Hahnloser et al. 2006), but was not significantly different from average firing rates of these neurons measured in the awake bird: 7.2 ± 7.0 Hz (Hahnloser et al. 2006). These results suggest that NIf provides essential input to HVC that is necessary to transform the regular spiking of HVCI neurons in the awake state into bursting in the sleeping state. The reduced firing and bursting of HVCI neurons after NIf inactivation agrees with previous inactivation and norepinephrine injection experiments in the anesthetized bird (Cardin and Schmidt 2004b; Coleman and Mooney 2004).
Paired recordings in identified HVC and NIf neurons
To directly compare firing properties of NIf neurons with those of HVC neurons, we performed paired recordings in these areas. NIf neuron type was identified by antidromic stimulation in HVC and HVC neuron type was identified by antidromic stimulation in either RA or Area X.
From our single-neuron recordings we know that NIfHVC neurons have a weak, but significant tendency to form burst epochs (in Fig. 3C, in the time interval 0.75–3.4 s the median autocovariance function is larger than the median renewal function, Kruskal–Wallis test, P < 0.01). However, as to be expected, in paired recordings of NIfHVC and HVCI neurons, we observed much stronger burst epochs in HVCI neurons than in NIfHVC neurons (Fig. 5A). To analyze cofluctuations of firing rates in more detail, we computed correlation coefficients of firing rates measured in nonoverlapping 3-s windows. For the neuron pair shown in Fig. 5A, the correlation coefficient of 0.15 was found to be significant (P < 0.05). In fact, most NIfHVC–HVCI neuron pairs showed significantly correlated firing rates (Fig. 5B, top left). By incorporating previously recorded data in HVC into our analysis (Hahnloser et al. 2006), we found that NIfHVC–HVCI firing-rate correlations are comparable to those of HVCRA–HVCI neurons (Fig. 5B, bottom left). Similarly, firing rates in most NIfHVC–HVCRA neuron pairs were significantly correlated, and also some NIfHVC–HVCX neuron pairs showed correlated firing rates. Thus on the level of firing-rate correlations with HVCI neurons, there is a similarity between NIf and HVC projection neurons, despite their large differences in burst rates.
The firing-rate correlations involving interneurons in NIf and HVC are stronger and more reliable than those involving projection neurons in NIf and HVC. In 99 neuron pairs not involving a projection neuron in either HVC or NIF, we found only one pair that did not exhibit significantly correlated firing rates (Fig. 5B, right). Average correlation coefficients for all neuron pairs studied are summarized in Table 2.
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To study the relationship between spikes in NIfHVC neurons and HVC projection neurons on a high temporal resolution, we examined raster plots of NIfHVC neuron activity aligned on HVCRA neuron bursts. We quantified the paired recordings by coherency functions, derived from the full spike trains including single spikes. We found that very often the HVCRA neuron bursts were preceded by NIfHVC neuron bursts by just a few milliseconds (Fig. 6, A and B). For most NIfHVC–HVCRA neuron pairs, we found one significant peak in the coherency function (20/34 pairs exhibited each one significant peak at median time lag of –5 ms, in the range –21 to 1 ms; 14/34 pairs exhibited no significant peak, Fig. 6C, bottom). The average coherency function between NIfHVC and HVCRA neurons exhibited a peak at –5 ms, well above the average significance threshold (Fig. 6C, top). The median coherency function was above the median significance threshold in the time interval from –7 to –5 ms (Wilcoxon signed-rank test, P < 0.01). Thus NIfHVC neurons on average spike just a few milliseconds before HVCRA neurons do.
We also examined raster plots of NIfHVC activity aligned on HVCX neuron bursts. In many pairs, as was the case with HVCRA neurons, bursts in HVCX neurons tended to be preceded by NIfHVC neuron bursts. However, in some cases, bursts in the HVCX neuron preceded the NIfHVC neuron bursts (Fig. 6D). In summary, most NIfHVC–HVCX neuron pairs exhibited at least one significant coherency peak (8/12 pairs exhibited at least one significant peak at median time lag –2 ms, in the range –8 to 20 ms, Fig. 6E). The three main peaks at a positive time lag in Fig. 6E are from three different birds and are unlikely to be a sampling artifact. The average coherency function between NIfHVC and HVCX neurons exhibited two distinct peaks, one peak at –3.5 ms, well above the average significance threshold, and the other at 15 ms, roughly coinciding with the average significance threshold (Fig. 6E, top). From these data, differences between the two types of HVC projection neurons are apparent. At a positive time lag >5 ms there was no peak in 34 pairs with HVCRA neurons, whereas there were six peaks in 12 pairs with HVCX neurons. The only HVCRA neuron we found to somewhat reliably burst before the NIfHVC neuron was the one shown in Fig. 6B. However, as can be seen, significance of the coherency function at a positive time lag was not achieved.
In paired recordings of NIfHVC neurons with HVCI neurons we observed frequent bursts in the HVCI neuron in close proximity to NIfHVC neuron bursts (Fig. 7A). In most NIfHVC–HVCI neuron pairs there was at least one significant peak in the coherency function (32/37 pairs exhibited at least one significant peak with a median time lag of 7 ms, range –24 to 123 ms, Fig. 7B, bottom). In agreement with the notion that NIfHVC neurons tend to burst before HVCI neurons, the average coherency function between NIfHVC and HVCI neurons exhibited a peak at 5 ms, well above the average significance threshold(Fig. 7B, top). The median coherency function of NIfHVC–HVCI pairs was significantly larger than the median significance threshold in the time interval from 2 to 14 ms (Wilcoxon signed-rank test, P < 0.01).
We were not able to record from many NIfI neurons. An example raster plot of a NIfI neuron, simultaneously recorded with an HVCRA neuron is shown in Fig. 7C. There was a highly significant coherency peak as a result of the many NIfI neuron bursts produced just a few milliseconds before the HVCRA neuron bursts. On the level of a small population, we could verify this tendency (3/3 HVCRA–NIfI pairs exhibited at least one significant peak with a median time lag of –9 ms).
For the paired recordings of NIfI neurons with HVCI neurons we did not produce raster plots because of the high burst densities in both of these neuron types. Coherency plots peaked significantly at a small negative time lag with respect to HVCI neuron spikes (3/3 significant pairs with median time lag of –4 ms in the range –9 to –2 ms), showing that the NIfI neurons were strongly coherent with HVCI neurons (Fig. 7D).
In conclusion, the time lags of coherency peaks were in agreement with a causal relationship between NIf neuron spikes and subsequent activity in HVC. Some of our observations suggest interesting mechanisms by which NIf neurons drive bursts in HVC neurons during sleep, discussed in the next section.
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DISCUSSION |
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Given the absence of any known projection target of NIf other than HVC, we speculate that the second NIf neuron type belongs to a class of interneurons. One reason for our speculation is based on the high burst rates in NIfI neurons, analogous to interneurons in RA and HVC (Hahnloser et al. 2006; Leonardo and Fee 2005). With RA and HVC interneurons being inhibitory (Rosen and Mooney 2003; Spiro et al. 1999), it may well be that NIfI neurons are inhibitory as well. This classification is quite plausible, given that our GABA and muscimol injection experiments and previous such experiments (Cardin and Schmidt 2004a; Coleman and Mooney 2004) strongly suggest an abundance of GABA receptors in NIf. However, further experiments will be necessary to determine the neurotransmitter type released by NIfI neurons.
We suspect the high coherencies between NIfI and HVC neurons arise from common input from NIfHVC neurons, just as HVCI neurons are strongly coherent with RA neurons, likely by virtue of common input from HVCRA neurons (Hahnloser et al. 2006). Currently, the only evidence that NIfI neurons are synaptically driven by NIfHVC neurons is that they respond with short latency to HVC stimulation, shown here to antidromically activate NIfHVC neurons. More paired NIfI and HVC recordings would help to further clarify the relationship between NIfI and HVC activity.
We did not find significant differences between firing rates of NIf neurons in the awake and the sleeping states. This finding contrasts with a previous study where arousal of lightly sedated birds led to reduction of spontaneous NIf activity (Cardin and Schmidt 2004a). This apparent disagreement might arise from differences between the sedated state and the sleeping state or might be attributed to effects of residual melatonin in our experiments. In any case, we believe the most interesting phenomenon to be that NIf cells burst in the awake state, a property that has never been observed in either RA or HVC cells.
We speculate that there exists a mechanism that gates the response of HVC to ongoing drive from NIf, much as there is a mechanism that gates the transmission of auditory responses in HVC from NIf (Cardin and Schmidt 2003, 2004b). According to our view, NIfHVC neurons would constantly bombard and occasionally activate HVC projection neurons. Not all of these events would necessarily result in a dense sleep-burst epoch in HVC, to agree with the moderate firing-rate correlations of NIfHVC and HVC projection neurons with HVCI neurons in Fig. 5. However, on some occasions, a dense sleep-burst epoch would form, activating most HVC neurons. Because we found a weak but nonnegligible tendency of NIfHVC neurons to also form sleep-burst epochs (Figs. 3C and 5, A and B), we speculate that Uva might somehow control the generation of sleep-burst epochs, for example, by coordinating neuromodulatory input (Akutagawa and Konishi 2005). Further influences on HVC excitability might also result from input from the MMAN (Foster and Bottjer 2001; Foster et al. 1997), as well as from direct cholinergic (Shea and Margoliash 2003) or other neuromodulatory input to HVC (Appeltants et al. 2000).
To our surprise, despite large differences between average firing rates in NIf and HVC projection neurons, we also found many similarities on a global and a local level. First, NIfHVC and HVCRA neurons have a similar burst density on the level of the entire population, when considering that burst rates in NIfHVC neurons were tenfold higher, but their population size is about tenfold smaller. Also, thanks to their soft refractoriness on the timescale of song syllables (about 100 ms), firing-rate distributions of NIfHVC neurons were found to be identical to those of HVCRA neurons (Fig. 5C).
A central question pertaining to NIf output is whether NIf activity serves only to initiate a sequence of self-sustained sleep bursts in HVC or whether every single burst in HVC is driven by a particular set of NIf bursts. In the former case, a particular set of strongly synchronized bursts in NIf might serve as the single initialization signal for the HVC bursts. In the latter case, transiently increased burst rates in NIf might lead to repeated excitation of HVC neurons beyond their firing thresholds. Our paired recordings involving NIfHVC neurons can be helpful to tease these two possibilities apart. Our main observation was an interesting difference in the coherencies of NIfHVC neurons with the two types of HVC projection neurons. HVCRA neurons burst reliably just after NIfHVC neurons did, whereas HVCX neurons burst reliably after and before NIfHVC neurons. From the coherencies with HVCRA neurons we would be led to conclude our first hypothesis—that NIf activity serves to initialize HVC sleep-burst sequences—is correct. However, the HVCX bursts preceding the NIfHVC bursts suggest that, at least on the timescale of a few milliseconds, NIf neurons engage in stereotyped sequential activity. Thus the more complete picture suggested by our data is that there is a first population burst in NIf that activates mainly HVCX neurons. Then, a second population burst just a few milliseconds later activates HVCRA and HVCX neurons. Such a scenario can explain why HVCRA bursts are rarely observed to precede NIfHVC bursts, whereas HVCX neurons are observed to frequently burst before and after NIfHVC neurons. It is interesting to note that, in our previous recordings of HVC neuron pairs (Hahnloser et al. 2006), whereas most HVCX - HVCRA pairs were not coherent, the single pair we found that showed coherent bursting did so with the HVCX neuron leading by roughly 20 ms. Coherency differences between the two types of HVC projection neurons might result from two distinct classes of NIfHVC neurons, from HVC-intrinsic mechanisms such as inhibition (Mooney 2000; Solis and Perkel 2005) or from other brain areas such as Uva. For example, Uva projection neurons might directly drive HVCX neurons and indirectly drive HVCRA neurons by activation of NIf.
We can speculate about the functional role of the observed latency differences between HVCRA and HVCX neurons. It is clear that signals originating in HVC and arriving in RA take longer if they do so by traveling along the anterior forebrain pathway involving Area X (Okuhata and Saito 1987) than by traveling directly from HVC to RA. Thus the anticipatory firing of HVCX neurons can partially compensate for signal delays that would normally occur if they fired simultaneously with HVCRA neurons. The signal propagation time from HVC to RA along the anterior forebrain pathway has been estimated to be around 40 ms (Abarbanel et al. 2004; Kimpo et al. 2003; Troyer and Doupe 2000). With our measured time difference of 20 ms (15 + 5 ms) between the second peak in HVCX coherency functions and the first peak in HVCRA coherency functions, it follows that about half the signal propagation time along the anterior forebrain pathway is compensated for by anticipated departure of HVCX neuron signals. With an estimated propagation time of 4 ms for HVCRA signals to reach RA (Hahnloser et al. 2006), we conclude that bursts in NIf tend to reach RA by the premotor pathway 16 ms before they reach RA by the anterior forebrain pathway.
What could be the purpose of such a latency adjustment? Some previous models of vocal learning emphasized the importance of differences in propagation delay through the premotor pathway and through the anterior forebrain pathway. In these models HVCX activity is to be viewed as an efference copy of premotor signals in HVCRA neurons (Abarbanel et al. 2004; Troyer and Doupe 2000). Given the known modulatory role of LMAN for song (Kao et al. 2005; Olveczky et al. 2005; Scharff and Nottebohm 1991), anticipation of HVC signals transmitted along the anterior forebrain pathway could be important to maintain proper registration of premotor signals along the two efferent pathways of HVC.
Given our acute experimental protocol, we were unable to relate individual spike patterns in the sleeping bird to premotor activity during singing. Therefore we analyzed burst events, knowing that bursting in HVC and in RA is a signature of the singing state. Our reversible inactivation experiments provide new insights into the role of NIf for burst generation in HVC (Fig. 4). Given the strong correlations between HVCI neurons and other HVC neuron types during sleep (Hahnloser et al. 2006), and given the strong reduction of auditory-evoked subthreshold activity in all HVC neuron types after NIf inactivation (Coleman and Mooney 2004), we speculate that NIf drives sleep bursts in all HVC neuron types and, presumably, also in RA. Such a role of NIf during sleep is highly remarkable given that NIf neurons exhibit singing-related activity (Hahnloser and Fee 2003; McCasland 1987), although HVC seems to be able to produce song-related bursts in adult zebra finches with bilateral NIf lesions (Cardin et al. 2005). In combination with the loss of auditory responses in HVC neurons after NIf inactivation (Coleman and Mooney 2004), our results imply a surprising involvement of NIf in conveying song-specific activity, both on a replay and on a sensory level. With the possibility of existence of off-line learning mechanisms during sleep (Deregnaucourt et al. 2005; Margoliash 2001; Nadasdy et al. 1999), we could imagine that auditory hallucination of the BOS originating in NIf is the driving force of such memory consolidation processes.
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GRANTS |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Address for reprint requests and other correspondence: R.H.R. Hahnloser, Institute of Neuroinformatics, UZH/ETHZ, Winterthurerstrasse 190, 8057 Zurich, Switzerland (E-mail: rich{at}ini.phys.ethz.ch)
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ABSTRACT
INTRODUCTION
1 mV) when the electrode is inside NIf and low otherwise. C: electrical stimulation in HVC elicits spike responses in NIfHVC neurons at a single precise time lag (top). Stimulating 0.5 ms after a spontaneous spike in a NIfHVC neuron leads to response failure (bottom). D: antidromic responses in NIfI neurons have larger latency variability (top) and do not fail when HVC is stimulated right after a spontaneous spike (bottom). The arrows in C and D mark the stimulation artifacts. E: a plot of spike latency vs. latency variability allows for segregation of NIfHVC neurons from NIfI neurons based on a latency-variability threshold of 0.1 ms. F: spike waveforms of NIfHVC neurons (top) and of NIfI neurons (bottom) are very similar.
