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Origins of basal ganglia output signals in singing
juvenile birds
Morgane Pidoux, Tejapratap Bollu, Tori Riccelli and Jesse H. Goldberg
J Neurophysiol 113:843-855, 2015. First published 12 November 2014; doi:10.1152/jn.00635.2014
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J Neurophysiol 113: 843– 855, 2015.
First published November 12, 2014; doi:10.1152/jn.00635.2014.
Origins of basal ganglia output signals in singing juvenile birds
Morgane Pidoux, Tejapratap Bollu, Tori Riccelli, and Jesse H. Goldberg
Department of Neurobiology and Behavior, Cornell University, Ithaca, New York
Submitted 25 August 2014; accepted in final form 7 November 2014
songbird; basal ganglia; globus pallidus; striatum
an evolutionarily conserved set of
brain nuclei critical for motor learning (Graybiel et al. 1994).
BG output neurons in the internal pallidal segment (GPi) and
the substantia nigra pars reticulata control behavior through
their projections to the brain stem and the ventral “motor”
thalamus (Albin et al. 1989; Penney and Young 1983). Across
behavioral paradigms and model systems, the activities of BG
output neurons are time-locked to behavior in diverse ways:
neurons may exhibit rate increases or decreases in response to
task cues or reward, as well as prior to, during, or following
movement initiation or termination (Anderson and Horak
1985; Anderson and Turner 1991; Bryden et al. 2011; Freeze et
al. 2013; Georgopoulos et al. 1983; Gulley et al. 1999; Joshua
et al. 2009; Kunimatsu and Tanaka 2010; Meyer-Luehmann et
al. 2002; Nambu et al. 1990; Schmidt et al. 2013; Sheth et al.
2011; Turner and Anderson 2005; 1997; Yoshida and Tanaka
2009).
The behavior-locked firing patterns of GPi neurons are
controlled by multiple parallel circuits inside the BG (Fig. 1A)
(Tachibana et al. 2008), including the hyperdirect pathway, as
well as the direct and indirect pathways, which themselves can
be modulated by striatal interneurons (Fig. 1A) (Gittis et al.
2010; Hikosaka and Wurtz 1989). Songbirds provide a unique
opportunity to determine how these pathways shape behaviorlocked firing of BG outputs. First, songbird area X is a
THE BASAL GANGLIA (BG) ARE
Address for reprint requests and other correspondence: J. H. Goldberg, Dept.
of Neurobiology and Behavior, Cornell Univ., W121 Mudd Hall, Ithaca, MA
14853 (e-mail: [email protected]).
www.jn.org
striato-pallidal nucleus homologous to the mammalian BG
(Fig. 1B) (Doupe et al. 2005; Farries et al. 2005; Farries and
Perkel 2002; Goldberg et al. 2010; Goldberg and Fee 2010).
Second, during vocal babbling, BG outputs discharge with a
peculiar homogeneity that, to our knowledge, has not been
observed in other model systems or behaviors: GPi-like output
neurons in area X exhibit brief peaks in activity in the milliseconds prior to syllable onsets and brief rate decreases prior to
offsets. Pre-onset decreases or pre-offset increases are never
observed (Goldberg et al. 2012; Goldberg and Fee 2012). The
striking homogeneity of these signals appears only during the
babbling stage of vocal development. At later plastic and adult
song stages, each GPi-like neuron exhibits its own idiosyncratic timing relationship to song, increasing or decreasing its
firing rate at onsets or offsets or at reproducible time points in
between (Goldberg et al. 2010; Hessler and Doupe 1999;
Iwasaki et al. 2013; Woolley et al. 2014).
To determine the origins of this “known” BG output signal,
we recorded area X neurons during vocal babbling. We find
that putative medium spiny neurons (MSNs) and fast spiking
(FS) and tonically active (TAN) interneurons exhibit similar
song-locked discharge as the GPi neurons: rate increases at
onsets and decreases at offsets. Meanwhile, external pallidal
segment (GPe)-like neurons exhibit rate decreases at onsets.
These syllable locked signals were abolished by lesion of the
premotor cortical nucleus HVC. Together, these findings are
consistent with roles for the indirect and hyperdirect pathways
in shaping BG output signals during early stages of vocal
development.
MATERIALS AND METHODS
Animals. Subjects were seven control juvenile male zebra finches,
35–50 days posthatch, and four HVC-lesioned zebra finches, 40 –55
days posthatch. Birds were obtained from the Cornell zebra finch
breeding facility (Ithaca, NY). The care and experimental manipulation of the animals were carried out in accordance with guidelines of
the National Institutes of Health and were reviewed and approved by
the Cornell Committee on Animal Care. All data are from juvenile
birds singing undirected song.
Chronic neural recordings and histology. Recordings were carried
out using a modified version of a motorized microdrive described
previously (Fee and Leonardo 2001) mounted with arrays of 5–10
single electrodes (3–5 M⍀, Pt/Ir and Tungsten from microprobes.
com). During microdrive implant surgery, the array was stereotactically targeted to area X based on previously established coordinates
(5.8 mm anterior, 1.5 mm lateral, and 2.8 mm ventral), at a head angle
20° displaced from the flat groove on the anterior border of the bird’s
skull. Electrophysiological data were band-pass filtered in custom
analog circuits (0.25–15 kHz) and were acquired at 40 kHz using
custom Matlab acquisition software. Units accepted for analysis had
signal-to-noise ratios (average spike peak amplitude compared with
SD of noise) greater than 10:1 and were sorted by threshold crossings
and template matching implemented in custom Matlab code, as
0022-3077/15 Copyright © 2015 the American Physiological Society
843
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Pidoux M, Bollu T, Riccelli T, Goldberg JH. Origins of basal
ganglia output signals in singing juvenile birds. J Neurophysiol
113: 843– 855, 2015. First published November 12, 2014;
doi:10.1152/jn.00635.2014.—Across species, complex circuits inside
the basal ganglia (BG) converge on pallidal output neurons that
exhibit movement-locked firing patterns. Yet the origins of these
firing patterns remain poorly understood. In songbirds during vocal
babbling, BG output neurons homologous to those found in the
primate internal pallidal segment are uniformly activated in the tens of
milliseconds prior to syllable onsets. To test the origins of this
remarkably homogenous BG output signal, we recorded from diverse
upstream BG cell types during babbling. Prior to syllable onsets, at the
same time that internal pallidal segment-like neurons were activated,
putative medium spiny neurons, fast spiking and tonically active
interneurons also exhibited transient rate increases. In contrast, pallidal neurons homologous to those found in primate external pallidal
segment exhibited transient rate decreases. To test origins of these
signals, we performed recordings following lesion of corticostriatal
inputs from premotor nucleus HVC. HVC lesions largely abolished
these syllable-locked signals. Altogether, these findings indicate a
striking homogeneity of syllable timing signals in the songbird BG
during babbling and are consistent with a role for the indirect and
hyperdirect pathways in transforming cortical inputs into BG outputs
during an exploratory behavior.
ORIGINS OF BASAL GANGLIA OUTPUT SIGNALS
B
Mammal
Songbird
Direct pathway
HVC
LMAN
FS
MSND1
LTS
MSND2
TAN
GPe
STN
Hyperdirect pathway
FS
Indirect Pathway
Hyperdirect pathway
Cortex
MSN
LTS
TAN
GPe
GPi
GPi
Thalamus
Thalamus
Indirect pathway
A
Direct pathway
844
previously described (Goldberg and Fee 2012). Small electrolytic
lesions (20 ␮A for 15 s) were made through the electrodes at the
conclusion of experiments for histological verification of electrode
position.
Data analysis. Spikes were sorted offline using custom Matlab
software. We represented neural activities as instantaneous firing rates
(IFR), R(t), defined at each time point as the inverse of the enclosed
interspike interval as follows (Eq. 1):
R共 t兲 ⫽
1
ti⫹1 ⫺ ti
, for ti ⬍ t ⱕ ti⫹1
where ti is the time of the ith spike. Peak firing rates (99th percentile
rate) were calculated for each neuron as the inverse of its 1st
percentile interspike interval. Nonsinging firing rates were calculated
from spiking activity during silent periods more than 10 s separated
from singing.
Identification of cell types. BG cell types were identified on the
basis of singing-related neural activity [for putative GPe, GPi, MSN,
TAN, and low-threshold spike (LTS)] and spike waveform (for
putative FS cells) as described previously (Goldberg et al. 2010;
Goldberg and Fee 2010). For each neuron, the spike width was
calculated as the half-width of the average of 50 spike waveform
examples. Units with spike widths less than 0.06 ms were part of a
distinct cluster and were identified as putative FS neurons (Goldberg
and Fee 2010). Neurons with mean firing rate during singing less than
5 Hz and spike width greater than 0.06 ms were classified as putative
MSNs; neurons with mean firing rate during singing greater than 10
Hz and peak firing rate (inverse of the 1st percentile interspike
interval) less than 600 Hz were identified as putative TANs; those
with peak firing rate greater than 600 Hz were identified as LTS
neurons. Pallidal neurons were distinguished from the above striatal
cell types by mean firing rate during singing greater than 50 Hz.
Furthermore, to distinguish GPe neurons from GPi pallidal neurons,
we used previously established criteria that leverage the distinct
spectral properties of GPe and GPi firing: GPe-like neurons exhibit
slow rate modulations with long burst and pauses, whereas GPi-like
neurons exhibit high-frequency modulations without long bursts or
pauses. To quantitatively distinguish these neurons, we smoothed the
IFR with an finite impulse response equiripple filter with a pass band
below 25 Hz and stop band greater than 75 Hz, with 80-dB attenuation
in the stop band. To remove the DC offset (high average firing rate),
IFRs were mean-subtracted using an infinite impulse response 1-Hz
high-pass filter generated with coefficients b ⫽ [1 ⫺1] and a ⫽ [1
⫺0.9988]. The peak of this smoothed IFR produces two distinct
clusters that separate GPe-like neurons, which exhibit peaks in
smoothed IFR greater than 250 Hz, from GPi-like neurons, which
exhibit peaks less than 250 Hz (Goldberg et al. 2010).
Analysis of correlations of neural activity to syllable onsets and
offsets. Statistical significance of rate increases and decreases in
syllable onset (or offset) aligned histograms was calculated as described previously (Goldberg and Fee 2012). Neural activity was
aligned to the 200 ms preceding and following all syllable onsets and
offsets. Only cells recorded for greater than 50 syllables were included
in the study. To determine the significance of firing-rate peaks and
troughs, a rate histogram (bin size, 10 ms) was generated of the real
data. Then a surrogate histogram was generated in which each trial of
syllable-aligned neural activity was time-shifted by a uniformly distributed random amount over a range equal to the duration of the
histogram (400 ms). The shift was circular, such that spikes wrapped
around to the beginning of the histogram, preserving the overall spike
J Neurophysiol • doi:10.1152/jn.00635.2014 • www.jn.org
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Fig. 1. Basal ganglia (BG) circuitry in mammals and songbirds. A: three main pathways run in parallel in mammalian BG circuits and converge on BG output
neurons in the globus pallidus, internal segment (GPi). First, in the direct pathway, medium spiny neurons (MSNs) directly inhibit BG outputs via the MSN ¡
GPi projection. Second, in the indirect pathway, the external pallidal segment (GPe), an inhibitory nucleus, is interposed between the MSNs and the GPi, such
that indirect pathway MSNs may disinhibit GPi neurons through the MSN ¡ GPe ¡ GPi projection. Third, in the hyperdirect pathway, cortical inputs can
activate GPi neurons through the glutamatergic subthalamic nucleus (Nambu et al. 2002). Additionally, BG output neurons may directly suppress each other’s
firing through extensive collaterals (Brown et al. 2014). Finally, three classes of striatal interneuron, low-threshold spiking (LTS), fast spiking (FS) and tonically
active neurons (TAN), modulate MSN firing, but their roles in shaping BG output signals remain unknown (English et al. 2012; Gittis and Kreitzer 2012;
Szydlowski et al. 2013). B: similar circuits exist in area X nucleus of songbirds, but there are also important differences. First, no homolog of the mammalian
subthalamic nucleus has yet to be reported inside area X. A hyperdirect pathway is manifest via inputs from HVC and lateral magnocellular nucleus of the anterior
nidopallium (LMAN) directly to pallidal output neurons, but this pathway lacks recurrent subthalamic nucleus-GPe connections important in mammals. Second, it
remains unknown if there are different area X MSN subtypes that give rise to direct and indirect pathways via distinct outputs to GPi- and GPe-like neurons, respectively.
ORIGINS OF BASAL GANGLIA OUTPUT SIGNALS
A
*
IFR (Hz)
750
0
0
Time (s)
B
F
*
0.2
mV
C
600
IFR (Hz)
0
-0.2
*
-0.1
0
0.1
0.2
Time relative to onset (s)
G
10
-0.1
0
0.1
0.2
Time relative to offset (s)
400
D
H
0
-12
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
E
40
0
-40
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
370
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
12
Δ Rate (Hz)
370
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
12
Δ Rate (Hz)
0
-0.2
Rate (Hz)
Rate (Hz)
400
J
0
-12
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
40
Δ Rate (Hz)
IFR (Hz)
600
Δ Rate (Hz)
8
0
-40
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
Fig. 2. GPi-like neurons in area X exhibit rate peaks at syllable onsets during
vocal babbling. A: spectrogram showing vocal babbling of a juvenile bird is
shown above the instantaneous firing rate (IFR) of a single GPi axon terminal
recorded in the medial nucleus of the dorsolateral thalamus (DLM). B:
expanded view of song (top), spiking activity (middle) and IFR (bottom)
aligned to the onset of a single syllable (marked with blue asterisks). C and D:
syllable onset-aligned rate histograms for the neuron in shown in A and B (C)
and for all GPi-like neurons recorded in area X and axon terminals recorded in
DLM (D) (shading indicates ⫾SE) (n ⫽ 26). E: scatter plot showing the
magnitude of statistically significant maximal rate increases (black triangles)
and decreases (red inverted triangles) plotted against the time, relative to
syllable onset, at which they occurred. Note that each cell can contribute at
most one increase and decrease to this plot. Note also that the x-axis is the same
for B–E. F–J: data are plotted exactly as in B–E for syllable offsets. ⌬, Change.
J Neurophysiol • doi:10.1152/jn.00635.2014 • www.jn.org
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statistics of the data. The minimum and maximum of the surrogate
rate histogram was then obtained with 1,000 repetitions of randomly
shifted data. P values for the rate minimum and maximum of the real
data set were calculated by analyzing the frequency with which
shifted data sets generated larger maxima or smaller minima in firing
rates. Maximal rate increases and decreases with P values ⬍0.05 were
considered significant. The amplitude of the rate modulation was
computed as the maximal deviation from the average rate during the
⫾200 ms around syllable onset (or offset). The timing of the rate
change relative to the syllable onset (or offset) was computed as the
time of maximal rate change above (for rate increases) or below (for
rate decreases) the baseline rate. To construct population rate histograms (as in Fig. 2D), histograms from individual cells were meansubtracted and then averaged across the group.
Most syllables during vocal babbling are less than 200 ms in
duration, and, because syllable onsets are preceded by syllable offsets
and syllable offsets are followed by syllable onsets, average rate
histograms of activity ⫾200 ms around a syllable boundary include
firing rate changes, not only at the zero time point of the boundary of
interest, but also over the preceding and following onsets and offsets
(Aronov et al. 2008). For this reason, rate histograms of neurons with
onset- and offset-locked firing appear periodic and exhibit side peaks
and troughs. Thus, for each histogram with a significant rate change,
we defined the “dominant” rate change (either an increase or a
decrease) as the one that exhibited the largest deviation from the
neuron’s average firing rate (Table 1). For each dominant rate change,
we computed five values: 1) its magnitude (in spike rate, Hz); 2–4) the
timing of its peak (2), onset (3), and offset (4) (in milliseconds relative
to syllable boundary, as in Fig. 2, E and J, and reported in Table 1);
and 5) its modulation width (defined as the duration between its offset
and onset). The onset was defined as the bin that exceeded 2 SDs from
the mean in the baseline rate. The offset was the bin that returned to
below 2 SDs from mean. The modulation width (ms) was the time
separating the onset and offset of the rate change (Table 1).
For MSNs and putative FS cells with very low baseline firing rates
and strong peaks at syllable onsets, significant peaks were also
observed in offset-aligned histograms; closer inspection of offsetaligned rasters showed that these offset-peaks were due to high
probability of firing in relation to the following (e.g., Fig. 3H) or
preceding (e.g., see Fig. 5I) syllable onset. Thus, for MSNs and FS
interneurons, we also defined a dominant activation as occurring at
either onset or offset, the dominant one being the one with the larger
peak. In all cases, this dominant rate change occurred in the syllable
onset aligned histograms. Note that, because these cell classes exhibit
such low baseline firing rates, we did not test for significance of rate
decreases in syllable-aligned histograms of MSN or FS activity.
To quantitatively test if average firing rate peaks (and dips) were
clustered at syllable boundaries beyond predicted by chance, we determined, for each neuron, the timing of the dominant rate change at onset
and offset, as described above. Next, for each cell class, we constructed
a “real data” histogram that plotted the number of cells exhibiting a
dominant rate change as a function of time relative to the syllable
boundary (⫺0.3 to ⫹0.3 s relative to syllable boundary; 0.2-s bins).
We also computed 1,000 surrogate histograms with the same data, but
with each cell’s timing randomly shuffled. We computed the P value
of the peak observed in the real data by analyzing the frequency with
which shuffled data sets generated a larger peak than what was
observed. P values ⬍0.01 were considered significant.
To determine the reliability of neuronal firing rate changes across
syllables, we computed the percentage of syllables in which a neuron
exhibited an increased rate at the time of its dominant rate change
(quantified in the average syllable onset aligned rate histogram).
Specifically, for each neuron with a dominant rate change, we examined the mean IFR that occurred in a 60-ms time window centered at
the time of its dominant rate change and then determined the frequency with which this mean was greater than that observed in the
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846
ORIGINS OF BASAL GANGLIA OUTPUT SIGNALS
Table 1. The timing and magnitude of syllable-locked firing patterns across putatively identified cell classes during vocal babbling
GPi
Dominant rate change, syllable onset, Hz
Peak of dominant rate change, syllable onset, ms
Onset of dominant rate change, syllable onset, ms
Width of dominant rate change, onset, ms
No. cells with dominant rate increase at onset
No. cells with dominant rate decrease at onset
Onset reliability, %syllable onsets exhibiting
dominant rate change
Dominant rate change, syllable offset, Hz
Peak of dominant rate change, syllable offset, ms
Onset of dominant rate change, syllable offset, ms
Width of dominant rate change, offset, ms
No. cells with dominant rate increase at offset
No. cells with dominant rate decrease at offset
Offset reliability, %syllable offsets exhibiting
dominant rate change
18.9 ⫾ 6.1
⫺16 ⫾ 32
⫺45 ⫾ 20
41 ⫾ 20
22/26
0/26
60 ⫾ 6
MSN
GPe
FS
TAN
LTS
4.3 ⫾ 1.7
⫺9 ⫾ 32
⫺3 ⫾ 60
30 ⫾ 13*†
15/20
N/A
N/A
⫺19.8 ⫾ 7.4
⫺18 ⫾ 30
⫺37 ⫾ 30
47 ⫾ 11
0/15
12/15
59 ⫾ 5
15.9 ⫾ 14.0
⫺8 ⫾ 10
⫺27 ⫾ 17
38 ⫾ 13
4/5
N/A
N/A
20.0 ⫾ 8.2
⫺15 ⫾ 13
⫺43 ⫾ 13
53 ⫾ 14
11/13
0/13
67 ⫾ 7*‡
N/S
N/S
N/S
N/S
0/10
0/10
N/S
N/A
N/A
N/A
N/A
0/5
N/A
N/A
⫺17.2 ⫾ 12.7
⫺17 ⫾ 16
⫺43 ⫾ 13
⫺53 ⫾ 13
0/13
7/13
56 ⫾ 3
N/S
N/S
N/S
N/S
0/10
0/10
N/S
⫺13.7 ⫾ 6.6
⫺29 ⫾ 42
⫺43 ⫾ 46
37 ⫾ 14
40/26
14/26
55 ⫾ 15
N/A
N/A
N/A
N/A
0/20
N/A
N/A
15.0 ⫾ 5.0
⫺1 ⫾ 60
⫺27 ⫾ 67
38 ⫾ 16
6/15
0/15
55 ⫾ 17
preceding 60-ms window, as previously described (Goldberg and Fee
2012).
HVC lesions. HVC was lesioned bilaterally using large electrolytic
lesions, as previously described in detail (Aronov et al. 2008; Goldberg and Fee 2011). Lesions were confirmed histochemically using
fluorescent antibody to neuronal nuclei (Mouse anti-Neu-N; Millipore, Temecula, CA) and/or the absence of retrograde label (10,000
mol wt Alexa dextran; www.lifetechnologies.com) of HVC following
injection into area X during the microdrive implant surgery. In control
birds, tracer injection into area X results in pronounced labeling of
HVC. These neurons were confirmed to be absent following HVC
lesion.
RESULTS
GPi-like neurons in areka X, as well as their axon terminals
recorded in the medial nucleus of the dorsolateral thalamus
(DLM) exhibit increased firing rates in the millisecond prior to
onsets and decreases prior to offsets (Fig. 2, Table 1; rate
changes were clustered at syllable boundaries, P ⬍ 0.001, see
MATERIALS AND METHODS) (Goldberg et al. 2012; Goldberg and
Fee 2012). Different pathways inside area X could “construct”
these GPi output signals. For example, if the direct pathway is
primarily involved, one would expect direct pathway MSNs to
exhibit decreased and increased probability of firing prior to
onsets and offsets, respectively. If the MSN-GPe-GPi indirect
pathway is involved, one might expect GPe neurons to exhibit
rate decreases at onsets, and increases at offsets. Hyperdirect
pathway involvement, via direct cortical projections to area X
output neurons (Farries et al. 2005), might bypass intrinsic BG
circuits altogether and makes no clear prediction about their
firing. Finally, the relationship between striatal interneuronal
firing to BG output signals remains poorly understood. To test
these distinct scenarios, we recorded upstream cell types inside
area X.
Putative MSNs are distinguished by their very low firing rate
(⬍5 Hz during singing) and their ultra-sparse song-locked
firing (Goldberg and Fee 2010; Woolley et al. 2014). MSNs
fired similarly sparsely in babbling birds, exhibiting a small but
significant increase in firing rate during singing (mean rate
during silent periods: 0.22 ⫾ 0.8 Hz vs. mean rate during
singing 0.80 ⫾ 0.5 Hz, P ⬍ 0.05, paired t-test, n ⫽ 20 neurons)
and discharging during only 10.6 ⫾ 6.0% of syllables (mean ⫾
SD). Inspection of syllable-locked firing revealed that most
exhibited a significantly higher probability of spiking at syllable onsets (rate increase of 4.3 ⫾ 1.7 Hz occurring at ⫺9.0 ⫾
32 ms relative to syllable onsets, statistically significant rate
increases in n ⫽ 15/20 neurons, Table 1, Fig. 3F, see MATERIALS
AND METHODS). MSNs with onset peaks also exhibited peaks in
offset-aligned histograms. However, examination of syllable
offset-aligned spike rasters that were sorted by the arrival time
of the preceding or following syllable onset clearly showed that
these peaks were attributable to the discharge related to the
neighboring onsets (e.g., see Fig. 3, H and I). Thus for each
MSN we defined the dominant peak as occurring in either onset
or offset aligned histograms as the one with the larger rate
change from baseline (see MATERIALS AND METHODS). Of the
15/20 neurons that exhibited statistically significant syllablelocked activations, all exhibited dominant peaks at syllable
onsets, and none of them at offsets (Fig. 3, Table 1, peaks were
clustered at onsets, P ⬍ 0.001, see MATERIALS AND METHODS).
The timing of MSN firing does not support a direct-pathway
origin of GPi onset peaks. If anything, MSN-GPi inhibition
would produce pauses prior to syllable onsets and peaks prior
to syllable offset, the opposite of what was observed in the
GPi-like neurons. Yet MSNs also target GPe-like neurons,
which can suppress firing of GPi neurons. If this pathway
contributes to syllable timing signals in GPi neurons, one
would expect GPe neurons to exhibit rate decreases prior to
onsets, and increases prior to offsets. To test this hypothesis,
we recorded GPe-like neurons in area X. As reported previously, these neurons discharged like many primate GPe neu-
J Neurophysiol • doi:10.1152/jn.00635.2014 • www.jn.org
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Values are means ⫾ SD across cells within a cell class. GPi, globus pallidus internal segment; MSN, medium spiny neurons; GPe, globus pallidus external
segment; FS, fast spiking; TAN, tonically active; LTS, low-threshold spiking; N/A, not applicable. For each cell class, the magnitude of the dominant maximal
rate change (Hz), its timing relative to syllable onset and offset, its reliability across syllables, and the number of cells with statistically significant dominant rate
increases and decreases in syllable onset- and offset-aligned histograms are shown (see MATERIALS AND METHODS). Note that these data were acquired from
syllable-locked rate histograms that exhibited statistically significant rate changes. N/S indicates that there were no such histograms in that cell class dataset. Note
also that rate decreases in rate histograms on onset/offset reliability were not computed in MSN and FS cell classes due to their sparse firing. A different metric,
the percentage of syllables with a spike, is shown in Table 3. See MATERIALS AND METHODS for analysis details. *P ⬍ 0.05 in unpaired t-test compared with the
cell classes indicated. †⬍GPe and TAN. ‡⬎GPi and GPe.
ORIGINS OF BASAL GANGLIA OUTPUT SIGNALS
A
*
0.1
mV
500 ms
G
B
*
0.1
mV
*
H
1800
1800
Syllable No.
Syllable No.
C
0
I 10
Δ Rate (Hz)
J
Δ Rate (Hz)
-1
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
10
0
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
1
n=15/20
5
0
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
0
-1
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
K
10
Δ Rate (Hz)
Δ Rate (Hz)
0
5
n=0/20
5
0
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
Fig. 3. Putative MSNs in area X exhibit rate peaks at syllable onsets during
vocal babbling. A: spectrogram showing vocal babbling of a juvenile bird is
shown above the spiking activity of a single MSN in area X. B: expanded view
of song (top) and spiking activity (bottom) aligned to the onset of a single
syllable (marked with blue asterisks). C: syllable onset aligned raster plot
showing the spiking activity of this neuron during 1,800 syllable renditions.
The raster rows are sorted by the duration to the syllable offset (curved blue
line at right of raster). To visualize sparse spikes as raster tics, tic height was
5 syllables. D and E: syllable onset-aligned rate histograms for the neuron in
shown in A–C (D) and for all MSNs recorded in area X (E) (shading indicates ⫾
SE) (n ⫽ 20). F: scatter plot showing the magnitude of significant dominant
rate increases (black triangles) plotted against the time, relative to syllable
onset, at which they occurred. Note that the x-axis is the same for B–F. G–K:
data are plotted exactly as in B–E for syllable offsets. In the raster in H, the data
are sorted by the arrival time of the following syllable onset. Note that the
post-offset spiking is aligned to the following syllable onset. K: 0/20 MSNs
exhibited their dominant rate peak at syllable offsets.
rons, exhibiting large bursts and long pauses during singing, a
type of firing never observed in the thalamus-projecting GPilike outputs in area X (Goldberg et al. 2010; Woolley et al.
2014) (Table 2, see MATERIALS AND METHODS). Consistent with a
role for the indirect pathway in shaping GPi firing, GPe
neurons exhibited, on average, rate decreases at syllable onsets,
and some also exhibited rate increases prior to offsets (n ⫽
12/15 and 6/15, respectively; Fig. 4, Table 1, rate changes were
significantly clustered at syllable boundaries, P ⬍ 0.001, see
MATERIALS AND METHODS). Together, these findings are consistent with a scenario in which MSN rate increases suppress GPe
firing at syllable onsets, which in turn may disinhibit GPi firing
at this time.
Three types of striatal interneuron are conserved between
songbirds and mammals, FS, LTS and TAN, yet it remains
unclear how their activities shape MSN and GP firing. We
recorded units putatively identified as these subclasses and
found that both FS and TAN cell types also exhibited rate
peaks at syllable onsets and decreases at syllable offsets (Figs.
5 and 6, Table 1). Meanwhile, LTS neurons exhibited highly
randomly timed high-frequency bursts, with weak, if any,
relationship to syllable timing (Fig. 7, Table 1).
Note that all of the syllable-locked signals reported thus far
are computed from syllable-aligned rate histograms, which
reflect the average firing of a neuron across syllables. Yet a
neuron need not increase its discharge at every syllable to yield
a strong peak in the histogram. For each neuron in the GPi,
GPe and TAN cell classes, we defined its “reliability” as the
percentage of syllables during which an observed rate change
occurred in the direction of the dominant rate change (see
MATERIALS AND METHODS). At syllable onsets, TAN neurons
exhibited increased firing rates prior to 67 ⫾ 7% of syllables
(P ⬍ 0.01 compared with GPi: 60 ⫾ 6%; and to GPe: 59 ⫾
5%, Table 1). There were no significant intergroup differences
in reliability of syllable-offset locked rate changes (Table 1).
We next wondered what input to area X might cause these
remarkably homogenous firing patterns. The two main cortical
inputs to area X, HVC and lateral magnocellular nucleus of the
anterior nidopallium (LMAN), play different roles in vocal
babbling. LMAN lesions abolish singing altogether in juvenile
babbling birds, and abolish vocal variability in older plastic
song birds, causing premature song crystallization (Aronov et
al. 2008; Bottjer et al. 1984; Brainard and Doupe 2001;
Olveczky et al. 2005; Scharff and Nottebohm 1991). Meanwhile, HVC lesions have little, if any, impact on song structure
during vocal babbling (Aronov et al. 2008). Thus HVC lesions
provide an opportunity to record area X neurons during a
singing behavior that is highly similar to vocal babbling.
To test how syllable onset and offset aligned firing patterns
depend on inputs from HVC, we recorded GPi, GPe and MSN
cell classes after complete bilateral HVC lesion in juvenile
birds (see MATERIALS AND METHODS). As reported previously,
song after HVC lesion resembled vocal babbling (Figs. 8 –10)
(Aronov et al. 2008; Chen et al. 2014b; Goldberg and Fee
2011). We also found that the basic spiking statistics of pallidal
neurons were not significantly affected by HVC lesion, and the
same analyses that distinguished these cell types in intact birds
distinguished them in HVC-lesioned birds (Table 2). In MSNs,
average firing rate during singing, but not during silent periods,
was slightly but significantly elevated in HVC-lesioned birds
(average rate during singing, control: 0.88 ⫾ 0.50 Hz vs.
J Neurophysiol • doi:10.1152/jn.00635.2014 • www.jn.org
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0
-0.2 -0.1
0
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1
E
F
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0
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Rate (Hz)
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ORIGINS OF BASAL GANGLIA OUTPUT SIGNALS
Table 2. Cell-class specific spiking characteristics of pallidal neurons persist following HVC lesion
Rate, nonsinging, Hz
Rate, singing, Hz
CV, nonsinging
CV, singing
99th percentile ISI, nonsinging, ms
99th percentile ISI, singing, ms
Peak in smoothed firing rate (range: min–max)
GPi
GPi (No HVC)
GPe
GPe (No HVC)
119.5 ⫾ 48.0
263.1 ⫾ 67.5
0.33 ⫾ 0.13
0.50 ⫾ 0.10
16.4 ⫾ 6.8
11.2 ⫾ 4.4
195 ⫾ 21 (153–228)
122.1 ⫾ 27.8
249.2 ⫾ 58.3
0.30 ⫾ 0.09
0.43 ⫾ 0.09
15.5 ⫾ 6.7
12.1 ⫾ 5.6
189 ⫾ 28 (151–249)
113.5 ⫾ 28.8
125.5 ⫾ 33.3
0.36 ⫾ 0.08
0.87 ⫾ 0.27
20.2 ⫾ 7.8
42.5 ⫾ 30.0
404 ⫾ 88 (266–583)
108.2 ⫾ 37.6
152.3 ⫾ 49.3
0.36 ⫾ 0.10
0.85 ⫾ 0.35
22.9 ⫾ 11.7
34.9 ⫾ 22.5
404 ⫾ 112 (253–587)
Values are means ⫾ SD across cells within a cell class. Singing-related changes in firing rates and interspike interval (ISI) statistics were not affected by HVC
lesion. The peak in the smoothed instantaneous firing rate, the metric used to distinguish GPe- from GPi-like neurons, was also not affected (see MATERIALS AND
METHODS). CV, coefficient of variation.
DISCUSSION
We studied the origins of BG output signals during vocal
babbling in juvenile zebra finches. Previous studies showed
that, at this early stage of vocal development, the GPi-like BG
output neurons in the songbird area X exhibit rate increases
prior to syllable onsets and decreases prior to syllable offsets.
Here we report that syllable-locked firing is similarly homogenous for upstream BG cell classes. Putative MSN, TAN and
FS cell classes exhibited transient rate increases at syllable
onsets, and rate decreases at offsets, while GPe-like neurons
exhibit transient decreases at onsets. Meanwhile, putative LTS
neurons were unmodulated by syllable timing. HVC lesions
did not significantly affect babbling behavior but largely abolished these song timing signals inside area X.
Together, these findings are consistent with a role for the
MSN-GPe-GPi indirect pathway inside area X in transforming
cortically derived timing signals into a BG output signal. Yet
several caveats are relevant to this interpretation, and other
scenarios cannot be ruled out. First, HVC projects directly onto
pallidal neurons in area X (Farries et al. 2005). Thus the peak
in GPi firing at syllable onsets might also arise from hyperdirect inputs form HVC (Fig. 1B). Support for a role of the
hyperdirect pathway is that many MSNs exhibited onset peaks
(Fig. 3F) after the occurrence of GPe onset troughs (Fig. 4E)
and GPi onset peaks (Fig. 2E; Table 1). These “late-peaking”
MSN neurons are therefore unlikely to play a role in sculpting
the average BG output signal and may reflect more complex
processing inside the BG, perhaps related to syllable phonology or elongation. Note that, for this hyperdirect pathway alone
to explain the syllable locked firing of the other neurons in area
X, GPi-GPe-MSN feedback would be required, which, although not impossible, is opposite the direction of signal
propagation described in this circuit in mammals (Hikosaka
2007; Jaeger and Kita 2011; Nambu et al. 2002).
A second caveat is that we were unable to determine in our
recordings which “type” of MSN we recorded and thus have no
direct evidence for their participation in direct or indirect
pathways or both. Specifically, area X MSNs may express
exclusively D1 receptors or D2 receptors, but, unlike mammalian MSNs, most songbird ones express both (Kubikova et al.
2010). Based on evolutionarily conserved patterns (Stephenson-Jones et al. 2012), it is possible that these distinct subtypes
project differentially to GPe- and GPi-like neurons in area X,
but this remains unknown. Assuming that we randomly sampled MSN subtypes in area X, it is likely that the 20 we
recorded represented a mix of MSN cell types. If this is the
case, then this would suggest that both direct and indirect
pathway MSNs synchronously exhibit rate increases at syllable
initiation, similar to what was recently observed in mice in the
context of locomotor initiation (Cui et al. 2013). However, we
cannot rule out the possibility that the 5/20 MSNs that did not
exhibit strong syllable-locked firing were a specific MSN
subtype.
A third caveat is that, while GPi firing increases, on average,
prior to syllable onsets, rate increases were not observed prior
to each individual syllable (see reliability in Table 1) (Goldberg and Fee 2012). By focusing on average syllable locked
rate histograms, we did not emphasize neural firing that varies
on a syllable-by-syllable basis; instead, we focused on possible
sources of the robust GPi onset signal only observable by
averaging across syllables. Thus, while our data are consistent
with a role for the indirect and hyperdirect pathways in shaping
J Neurophysiol • doi:10.1152/jn.00635.2014 • www.jn.org
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HVC-lesioned: 1.81 ⫾ 1.8 Hz, P ⬍ 0.05 Wilcoxon rank-sum
test, n ⫽ 13 MSNs). MSNs in HVC-lesioned birds also
discharged during a higher percentage of syllables (percentage
of syllables with MSN spike, control: 10.6 ⫾ 6.0%, vs. HVC
lesioned: 18.1 ⫾ 7.7%, P ⬍ 0.05 Wilcoxon rank-sum test)
(Table 3).
HVC lesions largely abolished syllable-locked firing across
cell types. In HVC-lesioned birds producing babbling-like
vocalizations, only 2/15 GPi-like neurons had significant rate
peaks at syllable onsets, and none had significant rate decreases
at offsets (Fig. 8). Only 2/19 GPe-like neurons had rate
decreases at syllable onsets, and 2/19 had peaks at offsets (Fig.
9). Only 2/13 MSNs exhibited rate peaks in syllable onset
aligned histogram (Fig. 10), and none exhibited significant rate
changes at offsets. Together, these data suggest that HVC is a
principal driver of syllable timing-related firing in area X of
babbling birds.
The importance of HVC in driving syllable-locked firing
raises the specific prediction that timing signals in area X
should be syllable specific. Specifically, in subsong and early
plastic songbirds, short syllables less than 50 ms are abolished
by LMAN lesion, elongated by mild LMAN cooling, but not
strongly affected by HVC lesion or cooling (Aronov et al.
2011; Veit et al. 2011). To test if area X neurons exhibit
diminished timing-related signals at short syllables, we examined onset- and offset-locked firing at short (⬍50 ms) syllables.
The magnitude of dominant peaks and dips at short-syllable
onsets was reduced in all cell types (Table 4), consistent with
a diminished role of HVC in contributing to the initiation of
brief syllables.
ORIGINS OF BASAL GANGLIA OUTPUT SIGNALS
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Fig. 4. GPe-like neurons in area X exhibit rate decreases at syllable onsets
during vocal babbling. A: spectrogram showing vocal babbling of a juvenile
bird is shown above the spiking activity (middle) and IFR (bottom) of a single
GPe-like neuron recorded in area X. B: expanded view of song (top) and
spiking activity (bottom) aligned to the onset of a single syllable (marked with
blue asterisks). C: syllable onset aligned raster plot showing the spiking
activity of this neuron during 250 syllable renditions. The data are sorted by the
duration to the syllable offset (curved blue line at right of raster). Tic height in
the raster is 1 syllable. Syllable onset-aligned rate histograms for the neuron in
shown in A–C (D) and for all GPe neurons recorded in area X (shading
indicates ⫾ SE) (n ⫽ 15) (E). F: scatter plot showing the magnitude of
significant rate increases (black triangles) and rate decreases (red inverted
triangles) plotted against the time, relative to syllable onset, at which they
occurred. Note that the x-axis is the same for B–F. G–K: data are plotted
exactly as in B–E for syllable offsets. K: raster rows are sorted by the duration
to the next syllable onset.
this average BG output signal, on a trial-by-trial basis, signal
propagation is likely to be more complex.
A final caveat relates to our assignment of cell class type in
our area X recordings. In past work, we discovered six distinct
singing-related firing patterns in area X (Goldberg et al. 2010;
Goldberg and Fee 2010), and we attempted to map their
identities to the six cell classes that were characterized in area
X in vitro (Carrillo and Doupe 2004; Farries et al. 2005; Perkel
et al. 2002). This mapping was based on the similarities to
well-characterized firing patterns of potentially homologous
mammalian BG cell types. Yet, because extracellular recording
alone is not sufficient to unambiguously identify a neuron type,
our cell type assignments in this paper represent our current
best hypotheses for cell identity. This caveat is particularly
important for the striatal interneurons whose firing patterns
during behavior remain poorly understood.
An outstanding question is the functional significance of the
syllable timing signals inside area X during babbling. The
production of vocal babbling is not strongly affected by lesions
of HVC or of area X (Aronov et al. 2008, 2011; Chen et al.
2014a; Goldberg and Fee 2011); therefore, these signals are not
necessary for vocal motor production and are unlikely to be
actually driving syllable onsets and offsets. Instead, because
area X lesions block learning (Goldberg and Fee 2011; Scharff
and Nottebohm 1991), syllable timing signals in area X might
reflect a learning signal important for advancing the subsongplastic song transition. Specifically, this transition is the earliest manifestation of vocal motor learning in birdsong and is
characterized by the emergence of a “protosyllable” of discernable phonological and temporal structure that is repeated in a
rhythmic fashion (Aronov et al. 2011; Tchernichovski et al.
2001). The critical neural event underlying protosyllable emergence is the linkage of a syllable offset to its own onset. This
linkage enables a syllable to become stereotyped in duration
and to be repetitively produced: two events that underlie the
first emergence of song rhythm (Aronov et al. 2011; Lipkind et
al. 2013; Saar and Mitra 2008; Veit et al. 2011). Given the
importance of syllable onset and offset times in this process, an
intriguing hypothesis is that syllable boundary signals observed
inside area X in this study are important for the early emergence of temporal structure in the song itself. To test this and
other hypotheses, it will be necessary to manipulate activity in
area X firing during babbling, for example, with opto- or
chemogenetics. Manipulations that influence song in “real
time” would support a model where area X is an “actor” that
biases vocalizations. Manipulations that only affect protosyllable learning would support a model where area X may
function more like a “critic” (Doya and Sejnowski 1995; Fee
and Goldberg 2011; Sutton and Barto 1998). Notably, protosyllables resemble “reduplication” events that occur early in
human babbling (Doupe and Kuhl 1999; Oller 1980). Because
BG disorders impair vocal learning in humans (Enard 2011),
determining the functional significance of BG timing signals in
babbling birds may illuminate neural mechanisms by which the
earliest exploratory utterances acquire nascent temporal patterns during learning.
GRANTS
Funding to J. H. Goldberg was provided by the National Institute of
Neurological Disorders and Stroke (Grant R00-NS-067062), the Klingenstein
Neuroscience Foundation, and the Pew Biomedical Scholars Award.
J Neurophysiol • doi:10.1152/jn.00635.2014 • www.jn.org
Downloaded from on April 1, 2015
D
Δ Rate (Hz)
C
6
849
850
ORIGINS OF BASAL GANGLIA OUTPUT SIGNALS
DISCLOSURES
REFERENCES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
Author contributions: M.P. and J.H.G. conception and design of research;
M.P., T.B., T.R., and J.H.G. performed experiments; M.P., T.R., and J.H.G.
analyzed data; M.P., T.R., and J.H.G. interpreted results of experiments; M.P.,
T.R., and J.H.G. prepared figures; M.P., T.B., T.R., and J.H.G. approved final
version of manuscript; T.B., T.R., and J.H.G. edited and revised manuscript;
J.H.G. drafted manuscript.
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Fig. 5. Putative FS interneurons in area X exhibit rate increases at syllable
onsets during vocal babbling. A: spectrogram showing vocal babbling of a
juvenile bird is shown above the spiking activity (middle) and IFR (bottom) of
a single FS neuron recorded in area X. B: expanded view of song (top) and
spiking activity (bottom) aligned to the onset of a single syllable (marked with
blue asterisks). C: syllable onset aligned raster plot showing the spiking
activity of this neuron during 1,100 syllable renditions. The data are sorted by
the duration to the syllable offset (curved blue line at right of raster). Tic height
in the raster is 3 syllables. Syllable onset-aligned rate histograms for the neuron
in shown in A–C (D) and for all FS interneurons recorded in area X (shading
indicates ⫾ SE) (n ⫽ 5) (E). F: scatter plot showing the magnitude of dominant
rate increases (black triangles, n ⫽ 4/5 FS neurons exhibited significant
dominant rate peaks at onset) plotted against the time, relative to syllable onset,
at which they occurred. Note that the x-axis is the same for B–F. G–K: data are
plotted exactly as in B–E for syllable offsets. H: the syllable-offset aligned
raster is sorted by the arrival time of the following syllable onset (curved blue
line at right of raster). K: 0/5 FS neurons exhibited significant dominant rate
peaks at offset.
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0
-40
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
Fig. 6. Putative TAN interneurons in area X exhibit rate increases at syllable
onsets during vocal babbling. A: spectrogram showing vocal babbling of a
juvenile bird is shown above the spiking activity (middle) and IFR (bottom) of
a putative TAN interneuron recorded in area X. B: expanded view of song (top)
and spiking activity (bottom) aligned to the onset of a single syllable (marked
with blue asterisks). C: syllable onset aligned raster plot showing the spiking
activity of this neuron during 1,800 syllable renditions. The data are sorted by
the duration to the syllable offset (curved blue line at right of raster). Tic height
in the raster is 1 syllable. Syllable onset-aligned rate histograms for the neuron
shown in A–C (D) and for all TAN interneurons recorded in area X (shading
indicates ⫾ SE) (n ⫽ 13) (E). F: scatter plot showing the magnitude of
significant rate increases (black triangles) and rate decreases (red inverted
triangles) plotted against the time, relative to syllable onset, at which they
occurred. Note that the x-axis is the same for B–F. G–K: data are plotted
exactly as in B–E for syllable offsets. H: the syllable-offset aligned raster is
sorted by the arrival time of the following syllable onset (curved blue line at
right of raster).
J Neurophysiol • doi:10.1152/jn.00635.2014 • www.jn.org
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G
B
D
6
851
852
ORIGINS OF BASAL GANGLIA OUTPUT SIGNALS
A
*
2
mV
IFR (Hz)
800
0
Time (s)
B
5
F
*
*
2
mV
G
1100
Syllable No.
H
10
0
-10
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
I
40
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
10
Δ Rate (Hz)
40
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
Δ Rate (Hz)
E
60
Rate (Hz)
60
Rate (Hz)
D
0
-10
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
Fig. 7. Putative LTS interneurons in area X do not exhibit strong syllablelocked firing during vocal babbling. A: spectrogram showing vocal babbling of
a juvenile bird is shown above the spiking activity (middle) and IFR (bottom)
of a putative LTS interneuron recorded in area X. B: expanded view of song
(top) and spiking activity (bottom) aligned to the onset of a single syllable
(marked with blue asterisks). C: syllable onset aligned raster plot showing the
spiking activity of this neuron during 1,100 syllable renditions. The data are
sorted by the duration to the syllable offset (curved blue line at right of raster).
Tic height in the raster is 4 syllables. Syllable onset-aligned rate histograms for
the neuron shown in A–C (D) and for all LTS interneurons recorded in area X
(shading indicates ⫾ SE) (n ⫽ 10) (E). Note that the x-axis is the same for B–E.
F–I: data are plotted exactly as in B–E for syllable offsets. H: the syllableoffset aligned raster is sorted by the arrival time of the following syllable onset
(curved blue line at right of raster).
J Neurophysiol • doi:10.1152/jn.00635.2014 • www.jn.org
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Syllable No.
C1100
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ORIGINS OF BASAL GANGLIA OUTPUT SIGNALS
A
853
B
HVC
100 μm
C
D
No
HVC
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E
IFR (Hz)
600
*
0
0
Time (s)
F
8
I
*
2
mV
IFR (Hz)
-0.1
0
0.1
0.2
Time relative to onset (s)
290
260
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
12
Δ Rate (Hz)
H
J
0
-0.2
-0.1
0
0.1
0.2
Time relative to offset (s)
290
Rate (Hz)
Rate (Hz)
G
0
-0.2
600
0
-12
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
K
260
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
12
Δ Rate (Hz)
IFR (Hz)
600
*
I
0
-12
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
Fig. 8. HVC lesions abolish syllable-locked firing in area X GPi neurons. A–D:
histological verification of complete HVC lesion. A and B: in unlesioned birds,
HVC is clearly visible in dark-field (A) and via fluorescent cell bodies
retrogradely labeled from injection of Alexa dextran tracer into area X (white
dotted line, see MATERIALS AND METHODS) (B). C and D: electrolytic lesion of
HVC was confirmed by visible destruction of the structure visible in dark-field
(C) and in the failure to observe retrogradely labeled neurons following
injection of tracer into area X (D). Scale bar in A applies to A–D. E:
spectrogram showing post-HVC lesion song of the juvenile bird whose brain
is shown in C and D. This song is shown above the IFR of a GPi neuron in area
X. F: expanded view of song (top), spiking activity (middle) and IFR (bottom)
aligned to the onset of a single syllable (marked with blue asterisks). Syllable
onset-aligned rate histograms for the neuron are shown in E and F (G) and for
all GPi-like neurons recorded in area X after HVC lesion (shading indicates ⫾
SE) (n ⫽ 17) (H). Note that the x-axis is the same for F–H. I–K: data are
plotted exactly as in F–H for syllable offsets. In G and H and J and K, the
ordinate scale was matched to the scale used in homologous plots for control
GPi neurons in Fig. 2.
J Neurophysiol • doi:10.1152/jn.00635.2014 • www.jn.org
854
ORIGINS OF BASAL GANGLIA OUTPUT SIGNALS
A
A
*
*
1
mV
IFR (Hz)
750
100 ms
B
Time (s)
F
400
0
Rate (Hz)
150
Rate (Hz)
0
-15
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
I
100
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
15
0
-15
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
Fig. 9. HVC lesions abolish syllable-locked firing in area X GPe neurons. A:
spectrogram showing post-HVC lesion song of a juvenile bird is plotted above
the spiking activity (middle) and IFR (bottom) of a single GPe-like neuron
recorded in area X of a bird with complete bilateral HVC lesion. B: expanded
view of song (top) and spiking activity (bottom) aligned to the onset of a single
syllable (marked with blue asterisks). C: syllable onset aligned raster plot
showing the spiking activity of this neuron during 400 syllable renditions. The
data are sorted by the duration to the syllable offset (curved blue line at right
of raster). Tic height in the raster is 1 syllable. Syllable onset-aligned rate
histograms for the neuron in shown in A–C (D) and for all GPe neurons
recorded in area X of HVC-lesioned birds (shading indicates ⫾ SE) (n ⫽ 19)
(E). F–I: data are plotted exactly as in B–E for syllable offsets.
5
0
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
1
E
0
G
0
-1
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
10
Rate (Hz)
D 10
H
Δ Rate (Hz)
Δ Rate (Hz)
E
100
-0.2 -0.1
0
0.1
0.2
Time relative to onsets (s)
15
Syllable No.
G
Rate (Hz)
150
500
5
0
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
1
H
0
-1
-0.2 -0.1
0
0.1
0.2
Time relative to offsets (s)
Fig. 10. HVC lesions abolish syllable-locked firing in area X MSNs. A:
spectrogram showing post-HVC lesion song of a juvenile bird is shown above
the spiking activity of a single MSN in area X. B: expanded view of song (top)
and spiking activity (bottom) aligned to the onset of a single syllable (marked
with blue asterisks). C: syllable onset aligned raster plot showing the spiking
activity of this neuron during 500 syllable renditions. The data are sorted by the
duration to the syllable offset (curved blue line at right of raster). Tic height in
the raster was 5 syllables. D and E: syllable onset-aligned rate histograms for
the neuron in shown in A–C (D) and for all MSNs recorded in area X (E)
(shading indicates ⫾ SE) (n ⫽ 20). F–H: data are plotted exactly as in B–E for
syllable offsets. H: in the raster, the data are sorted by the arrival time of the
following syllable onset.
Table 3. Spiking characteristics of putative MSNs following HVC
lesion
n
Rate, nonsinging, Hz
Rate, singing, Hz
Syllables with spike, %
MSN
MSN (No
HVC)
20
0.22 ⫾ 0.79
0.88 ⫾ 0.50
10.6 ⫾ 6.0
13
0.64 ⫾ 0.72
1.81 ⫾ 1.18*
18.1 ⫾ 7.7*
Values are means ⫾ SD across cells within a cell class; n, no. of medium
spiny neurons (MSNs). *P ⬍ 0.05 in Wilcoxon rank sum tests.
J Neurophysiol • doi:10.1152/jn.00635.2014 • www.jn.org
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D
F
C
500
Syllable No.
400
*
Syllable No.
C
*
*
Syllable No.
2
mV
*
0.1
mV
Δ Rate (Hz)
B
7
Δ Rate (Hz)
0
0
ORIGINS OF BASAL GANGLIA OUTPUT SIGNALS
855
Table 4. Syllable-locked rate modulations during syllables ⬍50 ms in duration
Dominant rate change, short syllable onsets, Hz
Dominant rate change, short syllable offsets, Hz
GPi
MSN
GPe
FS
TAN
LTS
11.3 ⫾ 15.0*
⫺14.2 ⫾ 19.0
0.9 ⫾ 2.4*
N/A
⫺10.8 ⫾ 10.0*
14.4 ⫾ 22.0
4.8 ⫾ 6.9
N/A
10.8 ⫾ 14.0*
⫺8.6 ⫾ 6.8
N/S
N/S
Values are means ⫾ SD across cells within a cell class. Data are plotted as in Table 1 showing magnitude of the dominant rate change for short syllables.
*P ⬍ 0.05, indicates that the rate change was significantly less than that observed for all-duration syllables, shown in Table 1.
Downloaded from on April 1, 2015
J Neurophysiol • doi:10.1152/jn.00635.2014 • www.jn.org