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Zebra Finch Mates Use Their Forebrain Song System in Unlearned Call Communication

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Abstract and Figures

Unlearned calls are produced by all birds whereas learned songs are only found in three avian taxa, most notably in songbirds. The neural basis for song learning and production is formed by interconnected song nuclei: the song control system. In addition to song, zebra finches produce large numbers of soft, unlearned calls, among which "stack" calls are uttered frequently. To determine unequivocally the calls produced by each member of a group, we mounted miniature wireless microphones on each zebra finch. We find that group living paired males and females communicate using bilateral stack calling. To investigate the role of the song control system in call-based male female communication, we recorded the electrical activity in a premotor nucleus of the song control system in freely behaving male birds. The unique combination of acoustic monitoring together with wireless brain recording of individual zebra finches in groups shows that the neuronal activity of the song system correlates with the production of unlearned stack calls. The results suggest that the song system evolved from a brain circuit controlling simple unlearned calls to a system capable of producing acoustically rich, learned vocalizations.
Activity of RA neurons associated with calling and singing. All data from one representative example where the same RA unit fired during song as well as before stack call production. The recording was 4 h. A. Properties of the recorded unit and the stack calling exchange of the recorded male with his partner. Left: Interspike interval (ISI) histogram of the unit that was isolated after sorting. The histogram describes a neuron that has a modal ISI of 30.4 msec, which is typical for RA neurons recorded in free moving finches. Center: PSTH of male and female stacks aligned on the 137 male calls. Right: 196642 superimposed waveforms of the unit. B. RA activity associated with different stack call categories. RA unit firings aligned to the onset of the stack call. The call is amplified x10 as compared with the song shown in C. Stack calls categorized as ‘‘answered’’ (green, N = 44), ‘‘answer’’ (red, N = 28), or ‘‘no connection’’ (dark grey, N = 65) are associated with elevated RA firing before the call is produced. The RA activity patterns are very similar and seem independent of the stack call category. The call has an average FM value of 24.3 which is well within the range for stacks. The stack does not resemble any song syllable C. Binned activity of an RA neuron, aligned to 33 songs produced by this animal. The pattern is aligned to the first of the three repeated syllables (arrow). Binwidth: 5 msec. During song production, the firing rate of the unit corresponds with specific syllables. doi:10.1371/journal.pone.0109334.g004
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Zebra Finch Mates Use Their Forebrain Song System in
Unlearned Call Communication
Andries Ter Maat*, Lisa Trost, Hannes Sagunsky, Susanne Seltmann, Manfred Gahr
Max Planck Institute for Ornithology, Eberhard-Gwinner-Straße, Seewiesen, Germany
Abstract
Unlearned calls are produced by all birds whereas learned songs are only found in three avian taxa, most notably in
songbirds. The neural basis for song learning and production is formed by interconnected song nuclei: the song control
system. In addition to song, zebra finches produce large numbers of soft, unlearned calls, among which ‘‘stack’’ calls are
uttered frequently. To determine unequivocally the calls produced by each member of a group, we mounted miniature
wireless microphones on each zebra finch. We find that group living paired males and females communicate using bilateral
stack calling. To investigate the role of the song control system in call-based male female communication, we recorded the
electrical activity in a premotor nucleus of the song control system in freely behaving male birds. The unique combination
of acoustic monitoring together with wireless brain recording of individual zebra finches in groups shows that the neuronal
activity of the song system correlates with the production of unlearned stack calls. The results suggest that the song system
evolved from a brain circuit controlling simple unlearned calls to a system capable of producing acoustically rich, learned
vocalizations.
Citation: Ter Maat A, Trost L, Sagunsky H, Seltmann S, Gahr M (2014) Zebra Finch Mates Use Their Forebrain Song System in Unlearned Call Communication. PLoS
ONE 9(10): e109334. doi:10.1371/journal.pone.0109334
Editor: Johan J. Bolhuis, Utrecht University, Netherlands
Received March 14, 2014; Accepted September 8, 2014; Published October 14, 2014
Copyright: ß2014 Ter Maat et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. Most of the raw data are in the form of wave
files and the total storage occupied exceeds 10 TB. Relevant data are available from the corresponding author.
Funding: The study was undertaken with the budget of the Max Planck Society (www.mpg.de). The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* Email: termaat@orn.mpg.de
Introduction
Songbirds, which make up about half of all extant bird species,
have the ability to learn complex vocalizations like song and
certain types of distance calls beside their innate call repertoire,
whereas the closely related suboscine species produce only
unlearned song and calls. The emergence of the ability to produce
learned vocalizations is associated with the evolution of the
forebrain vocal control system, an interconnected network of brain
nuclei that shapes the song during learning and organizes the
motor output when singing [1,2]. At best, only rudimentary traces
of this system are found in the non-learning relatives of songbirds
[3]. Therefore, the vocal control system is thought to be uniquely
devoted to the control of learned sounds. The evolutionary steps
that led to the development of the learning-related forebrain vocal
system are as yet unknown, but it seems reasonable to assume that
the song system has evolved from circuits driving simpler
unlearned vocalizations.
In contrast to learned vocalizations, all birds, including
songbirds such as the zebra finch, produce an array of unlearned
call types that is present in both sexes [4,5]. Zebra finches use soft
‘‘tet’’ and ‘‘stack’’ calls (Figure 1A), which are not learned and are
thought to be important in close range communication [6]. They
are produced in very large numbers [7] by both males and females
[8]. Although these calls are not learned, learning might be
required for their timed initiation. The evidence for the precise
role of soft calls in communication is, however, anecdotical.
Calling exchanges in a social setting can only be determined when
the calls can be unequivocally ascribed to each individual.
Therefore, we have used miniature wireless microphones carried
by the animals to study the patterning of vocal interactions within
pairs as well as in groups of zebra finches. In this paper we identify
mutual stack calling as a defining property of the pair bond.
Since, like stack calls, song is used in a social context, the
association of the song control system with communicative calling
activity might shed light on its evolutionary history. The premotor
nucleus RA (nucleus robustus arcopallialis) is part of the song
motor pathway [1,9]. RA is electrically active during the
production of learned vocalizations [10,11] and controls the
spectral and temporal properties of song elements of zebra finches
[12]. RA, therefore, is a logical starting point for associating brain
activity with stack calling exchanges. Moreover earlier studies
suggest an involvement of RA in unlearned call production
[12,13]. Therefore, we studied brain and auditory activity in small
groups of socially interacting zebra finches, each animal carrying a
wireless microphone. Each male had electrodes implanted into RA
to record neuronal signals during vocal production while moving
freely. In this way we show that neurons in the song control system
perform precise and pronounced burst firing prior to stack calling.
Thus, RA has a function in the control of unlearned vocal social
interactions. Based on this, we propose an evolutionary scenario in
which the song control system evolved from a system that
controlled unlearned sounds that were used to communicate with
particular conspecifics in a social group; a process that involves
learned sensory-motor integration.
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Results
Associated stack call production between partners
Single pairs of zebra finches (N = 35 pairs) carrying wireless
microphones were kept in sound-proofed boxes for at least 7 days.
Each pair produced several thousand soft, short calls in addition to
contact calls and male song per day (Figure S1). Although not
every pair produced tet calls, we compared tet and stack calls in
order to be able to obtain a reliable criterion to identify stack calls.
Tet calls were shorter than stacks (Figure 1B; P = 1.9e–19), and
both types of call were shorter in males than in females
(P = 0.0025; call type 6gender interaction: P = 0.095). Stack and
tet calling rates were not significantly different between males and
females (10 pairs, paired t-test; stacks: P = .86; tets: P = .56). Stack
calls are easily distinguished from tet calls on the basis of a large
difference in FM (Frequency modulation; Figure 1A, C; call type:
P,0.0001). FM was larger in males than in females (gender:
P = 0.0003; interaction: P = 0.3267). In the following we will focus
on stack calls since these were produced reliably at high rates.
The production of the stack calls by two partners was clearly
time-locked, indicating that many calls were produced in response
to the calling of the partner (Figure 2A). Out of the 35 pairs
recorded, only two pairs did not show clear correspondences
between stacks. The number of stacks that were themselves an
answer, and the number that was answered showed less variation
than the total numbers produced: in each of 5 pairs that we
analyzed in detail and had developed a symmetrical calling
relationship the number of calls produced varied 7-fold, whereas
the number of stacks that were either an answer or received an
answer varied between 1320 and 558, slightly more than twofold
(Figure 2B).
Properties of answered and unanswered stacks
Since not every stack call produced an answer in the partner, we
parsed the stack vocalizations into those that were answered, the
answers and unconnected stacks. Stacks that followed the partner’s
stack within 0.5 sec were labeled ‘‘answer’’, those followed by a
stack call of the partner were ‘‘answered’’, and stacks falling
outside these two categories were called ‘‘no connection’’. The
fundamental frequency, wiener entropy and duration of these calls
were determined for stretches of 4 h in 5 pairs. Differences of
acoustical components between birds showed up as interactions in
the analysis, but there was no overall consistent feature that
distinguished between answers, answered and unconnected calls
(Figure S2). We have also looked into second order categories (e.g.
calls that are an answer and are in turn followed by a stack of the
partner). This did not yield any clear differences. The finding is
representative of all pairs with clear calling relationships.
Call patterns in social groups
We used a new cohort of zebra finches to determine the patterns
of interactions mediated by stack calls in group-housed birds.
Three groups of four, three groups of three pairs and five groups of
two pairs were kept in aviaries and each individual was equipped
with a backpack microphone. Prior to the group housing, pairs
were kept in soundproofed boxes and after two weeks as a pair,
they typically had established a pattern of stack calls that showed
significant association. After the group had settled for at least one
day, a matrix of association indices was calculated based on the
simultaneous wireless microphone recordings of all individuals in
the aviary (Figure 3). Pairs that did not establish a calling
relationship during the initial week also did not show mutual
calling during group housing (e.g. pair 3 in Figure 3). The calling
associations persisted unchanged when the pairs were again
separately housed in sound boxes. Mutual stack calling, therefore,
is likely to define pair bonding. Although mutual stack calling
mainly occurs between bonded partners, we occasionally recorded
exchanges of stack calls between animals in different pairs. This is
also illustrated in Figure 3 where a calling exchange exists between
the male of pair 1 and the female of pair 3. Figure S7–9 provide a
summary of all the experiments with group-housed zebra finches.
RA neuron firing is associated with call production
In 20 pairs that were kept in soundboxes and recorded with a
central microphone, the males carried a chronically implanted
tungsten electrode connected to a transmitting high-impedance
amplifier [14] to record electrical activity in RA while free moving.
In all cases we could differentiate between two different stack calls
Figure 1. Duration and FM of tet and stack calls in 6 pairs (10
randomly selected measurements per animal). A. Examples of
tets and stacks. Clearly, tets are much stronger frequency-modulated
than stacks. Note that our wireless microphones show more power in
the lower frequencies as compared with external microphones since
they record the near field. B. Tet calls had shorter durations than stacks
(P = 1.90e–19). In females, duration was slightly longer than in males
(P = 0.0025). C. Tets had higher FM-scores than stacks (P = 0.0001)
whereas FM-scores were generally lowest in females (P = 0.0003). All
tests: REML in JMP10 with pairs as random factor. Pitch was not
different between tets and stacks (not shown).
doi:10.1371/journal.pone.0109334.g001
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that were, in 17 cases exchanged between partners (Figure S10).
One of these stacks was invariably associated with RA activity
(Figure 4; Figure S10). This stack call was in all probability the call
of the male. Moreover, in 5 experiments where the stack calls
could be attributed unequivocally to each of the individuals
through the use of backpack microphones, the male call was
always associated with RA activity (Figure S11).
The multiunit recordings were sorted and based on waveform
and ISI (interspike interval) histogram putative single units were
identified (Figure S10, 11). The RA units in these free moving
animals had modal ISI’s of 38.0469.83 msec (N = 26). Under
these experimentally challenging conditions, firing was significant-
ly modulated (exceeding 1% confidence limits calculated from
1000 randomizations of call times) during stack calls in 22 out of
the 25 males (Figure 4). In 17 males, firing was increased
significantly during singing, and in 16 of those, RA modulation
occurred during stack calling (Table 1).
The associated firing patterns in RA in 26 units from 25 birds
can be classified as follows: Excitation before and during the call
occurred in 15 cases, there were 6 instances of inhibition, a
biphasic response (inhibition followed by excitation) occurred in 2
cases. Three units showed no significant response. Figures S10 and
S11 summarize the results for all RA recordings. We have
observed no instance where a stack call was incorporated into a
song motif, even though many songs contain syllables that have a
stacked sonogram.
In sum, these results show not only that RA firing is associated
with stack calling, but also that the same units may be involved in
the production of both the unlearned stack call and the learned
song.
Discussion
All birds produce calls for communication [15]. Loud alarm and
so-called ‘‘long’’ (or ‘‘distance’’) calls are often produced anti-
phonically in a variety of mammalian and bird species [16–19].
We here demonstrate that vocal communication takes place
between male and female zebra finches using soft stack calls. Stack
call exchanges occur primarily within bonded pairs, suggesting
that the unlearned stacks are important in confirming the pair
bond, similar to behaviors like clumping and allopreening [20,21].
The selective responsiveness to the partner’s stack calls also
strongly suggests that zebra finches can distinguish between the
calls of different individuals in the group. In the case of the loud
learned contact calls used for long distance communication, it has
been shown that zebra finches do recognize their own partner
[18]. Bilateral communication patterns at the nest have been
shown to comprise of different soft call types, all designated as
‘‘tet’’ calls [22].
Since pair-bonded zebra finches live in larger social groups the
observed specific call exchanges among group members require to
learn the individual call signature of other group members and to
respond in a timed fashion to specific calls. Such timed responses
are not an automatism since we find that not all calls of the mate
are answered or are answers. Short-range contact calling could
help the animals to locate their partners in a flock. Why not all
stack calls of the mate are answered despite being uttered within
hearing range of the call receiver remains unknown.
RA is part of the so-called song control system and organizes the
motor output of this system. This nucleus is critical for the
production of song as well as learned aspects of distance calls in
male zebra finches [2,11]. We find that RA neurons are also active
preceding stack calls. We, therefore, speculate that the song system
plays a role in call-based communication between bonded
partners. This implies that partners are able to recognize each
others’ calls. Since zebra finches produce several thousand stack
calls per day (Figure 2B) call-based social communication seems to
be a major function of RA, next to song control.
The fact that RA is controlling innate calls as well as learned
vocalizations allows speculation about the evolutionary origin of
the song control system. Since vocal learning occurs only in three
not closely related avian taxa (songbirds, hummingbirds, parrots)
and since the closest relatives of the songbirds, the sub-oscine
passerines do not show vocal motor learning [23], it is
Figure 2. The detailed relationships between stack calls produced by pairs of zebra finches. A. The temporal relationship is shown as a
peristimulus-time-histogram (PSTH; upper graph), where the onset times of the male calls are aligned to the onset times of the female calls. The
horizontal lines in the PSTH are the 1% confidence intervals. In the raster diagram (lower panel) the female calls are shown as red dots. Each male call
is represented by a black dot. The probability of male stack calls occurring within half a second before or after the female stack is clearly and
significantly increased. Data from one pair kept in a soundbox: 8472 male stacks and 11047 female stacks were recorded in a 35 h period (5 days with
7 h of recording each, starting at 8:00 AM). Binwidth is 100 msec. B. Relative contributions of calls that were answered, were an answer, or were not
connected to any stack call of the partner. Data from 5 established pairs.
doi:10.1371/journal.pone.0109334.g002
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parsimonious to assume that production of innate sounds is the
evolutionary older situation.
RA is most clearly defined both morphologically and neuro-
physiologically in oscine songbirds. In suboscines areas analogous
to RA have been described [3], and show concentrations of RA-
like cells, but this cell group is not nearly as clearly delineated as in
songbirds. During songbird evolution, this RA-precursor could
have extended its role from the control of innate calls to learned
songs. Our finding that many RA neurons fire milliseconds before
innate calls are produced, supports this hypothesis. In particular,
since stack call controlling neurons are also involved in the
production of learned song syllables, RA is not composed of two
separate sub-circuits dedicated to either learned or innate sounds
but the same neurons do both, i.e. carry out an evolutionary basic
and a derived task. Further, the RA firing patterns suggest an
involvement not only in calling per se but also in precisely timed
call exchanges between partners, which requires learning.
The symmetry of the call exchange between males and females
is a further reason why RA and possibly the rest of the song system
might have evolved first as a brain area to control the exchange of
innate vocalizations. The song system is present in males and
females of all songbirds, even in species with non-singing females
such as the zebra finch; song areas such as RA are only composed
of less and smaller neurons in non-singing females that are
Figure 3. Charting calling relationships in a group. Four pairs of zebra finches. Based on the PSTH, metrics were calculated to describe the
strength of the correlation between stack calls. The calls were recorded with carry-on microphones. Indicated in the upper matrix (A) are the PSTH’s
from which the metrics were calculated. The animals to whose calls the PSTH’s were aligned are indicated along the left vertical side of the matrix.
The animals whose calls were counted in the histograms are indicated along the bottom of the matrix. The lower matrix (B) shows the strength of the
relationship regardless of symmetry. The matrix shows different possible calling relationships. For instance, pair 4 and pair 2 do not call with any other
animal in the aviary. In contrast, the male of pair 1 is answered not only by the female of pair 1, but also by the female of pair 3. The partners of pair 3
are the only pair that did not interact at all in our experiments with groups. The associations of the callers with themselves are grayed out because
they represent autocorrelations, whereas all the others are cross-correlations. Two sonograms of stack calls are shown in panel B. Duration in msec is
shown underneath the sonograms.
doi:10.1371/journal.pone.0109334.g003
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nevertheless functionally connected with the syringeal motor
neurons [24,25]. Further, singing in females occurs in very many
songbird families [26–28]. Together these data suggest that the
differentiation of singing and the song system in both sexes is the
ancestral situation. Thus, the ancestral function of a RA-precursor
should be in the control of a vocal behavior that occurs in both
sexes, such as call exchange between males and females.
Figure 4. Activity of RA neurons associated with calling and singing. All data from one representative example where the same RA unit fired
during song as well as before stack call production. The recording was 4 h. A. Properties of the recorded unit and the stack calling exchange of the
recorded male with his partner. Left: Interspike interval (ISI) histogram of the unit that was isolated after sorting. The histogram describes a neuron
that has a modal ISI of 30.4 msec, which is typical for RA neurons recorded in free moving finches. Center: PSTH of male and female stacks aligned on
the 137 male calls. Right: 196642 superimposed waveforms of the unit. B. RA activity associated with different stack call categories. RA unit firings
aligned to the onset of the stack call. The call is amplified x10 as compared with the song shown in C. Stack calls categorized as ‘‘answered’’ (green,
N = 44), ‘‘answer’’ (red, N = 28), or ‘‘no connection’’ (dark grey, N = 65) are associated with elevated RA firing before the call is produced. The RA
activity patterns are very similar and seem independent of the stack call category. The call has an average FM value of 24.3 which is well within the
range for stacks. The stack does not resemble any song syllable C. Binned activity of an RA neuron, aligned to 33 songs produced by this animal. The
pattern is aligned to the first of the three repeated syllables (arrow). Binwidth: 5 msec. During song production, the firing rate of the unit corresponds
with specific syllables.
doi:10.1371/journal.pone.0109334.g004
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The study of brain activity in awake birds has contributed
greatly to our understanding of bird song learning and production
[10,29,30]. Until now, recording in moving animals imposed
restrictions on the freedom of movement, because the animals
were tethered, which made it impossible to study neuronal activity
in social groups. With our lightweight radio transmitters we make
available a method that allows us to record the signal of deep brain
electrodes and individual vocalizations synchronously. This
enables us to relate individual signaling behavior with the
underlying neuronal pattern in a group of zebra finches living in
an aviary which provides insight into the evolutionary link
between innate call production and learned song.
Methods
Ethics statement
Both transmitter types, the surgical procedure to implant the
deep electrodes and bird maintenance in sound boxes and aviaries
were approved by the government of Upper Bavaria, ‘‘Sachgebiet
54 – Verbraucherschutz, Veterina¨rwesen, 80538 Mu¨ nchen’’ with
the record number: Az. 55.2-1-54-231-25-09. All further animal
husbandry or handling was conducted according to the directives
2010/63/EU of the European parliament and of the council of 22
September 2010 on the protection of animals used for scientific
purposes.
Animals
Experimental birds were adult male and female zebra finches
(Taeniopygia guttata) obtained from our breeding facility. In the
experiments with single pairs the birds were kept in wooden cages,
placed in custom-made, soundproofed boxes. The equipment of
each box comprised a microphone (type C2, Behringer, Willich-
Mu¨nchheide II, Germany), and a telescopic antenna for wireless
transmission.
We kept the zebra finches in a 14/10 Light/Dark cycle
(fluorescent lamps), 24uC and 60–70% humidity. The experiments
with social groups were performed in 26262 m aviaries that had a
perspex roof, and were equipped with branches, plastic trees and
perches. Crossed-yagi antennae were mounted over the aviary.
We used 74 animals (37 males, 37 females) in the behavioral
experiments. Six pairs were only observed when kept as pairs in
soundboxes. 31 pairs were observed in groups (Figure S7–9). Two
males and two females also performed in an electrophysiological
experiment (TG4 and TG12; Figure. S7, S11). 25 males carried
implanted electrodes for neuronal recordings. Each of these males
was accompanied by a female, 5 of which carried a wireless
microphone. One of the males also carried a wireless microphone.
The total number of animals adds up to 120.
Wireless sound recording
Wireless microphones, weighing 0.6 g, including the battery,
were developed in-house (Microphones: Knowles Electronics,
FG23329; Figure S3A). Silicon tubing was attached to the
microphone and two loops were formed, one around the neck,
and one around the base of the tail taking care to place it rostral of
the cloacal area. Behavioral effects of this backpack occur during
the first 24 hours after mounting the microphone. After one day of
adaptation the birds showed more self preening activity but apart
from that seemed to be unaffected in moving and behavior
(Movies S1–S3). The microphone faced towards the body to
enhance the specificity of the recording (Figure S4, Movie S4).
Crossed yagi antennae were used (Winkler Spezialantennen,
Kreuzdipol 300, directional antenna for 300 MHz, clockwise).
The frequency modulated radio signals were received using
AOR5000 communication receivers (AOR, Ltd., Japan) with the
audio bandwidth set at 12 KHz (–3dB). The signal was decoded as
FM with intermediate frequency bandwidth set at 110 KHz. In
addition we used AOR8600 receivers that were modified to have
an audio bandwidth of 12 kHz. Signals were either fed into an 8
channel audio A/D converter (M-Audio 1010; 22050 Hz) and
recorded using custom written software, or registered on a
DASH8X data recorder (Astro-Med, Inc., RI, USA) at 25 KHz.
Analysis was based on continuous recordings of all channels.
Sorting vocalizations
In order to analyze the temporal relationships between the
different vocalizations and their associated neuronal activity, the
sounds produced by the animals were classified and time-stamped
using segmentation followed by sorting. The sounds registered by
the wireless microphones were transmitted continuously. The
received audio signals were written to WAVE files that were stored
on hard disk. Each animal was recorded at least 4 h per day
during an average of 4 days [31]. From these sound files, sounds
were extracted using a trigger level set by the user. The sounds
were converted into sonograms assembled from 256 point fast
Fourier transforms (Intel libraries). This procedure produced a
large number of sonograms each describing a syllable, a call, or
any other supra-threshold sound. From the sounds the average
frequency, modal frequency, fundamental frequency (first peak),
Wiener entropy, duration, and their standard deviations were
calculated and the sounds were subsequently clustered. The
experimenter was free to select which of the above features to use
for clustering. Analysis was done using custom software written in
Delphi Pascal for Windows and C++ on Apple Macintosh. Sorting
was done using a k-means clustering algorithm (Hartigan, 1975)
starting with two clusters and splitting new clusters off, one at a
time. After clustering, we removed clusters that were not
vocalizations as can easily be concluded from inspection of the
Table 1. Association between neuronal activation, stack calls and song in 26 units in 25 pairs.
song
associated no association not present total
associated 16 0 7 23
stack no association 1 0 2 3
not present 0 0 0 0
total 17 0 9 26
Out of 17 units where both stacks and song were present in sufficient quantity to permit statistical analysis, 16 had firing patterns that were associated both with stacks
and song.
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sonogram. In addition, every cluster was viewed and mistakes were
corrected based on visual inspection. The result was stored as
bitmap pictures of all the vocalizations in each cluster (Figure S5).
Additional acoustic features were extracted using Sound Analysis
Pro software [31].
Normally, since the calls are very soft, there was no discernable
sound visible that could be attributed to other animals in the
aviary or soundbox. A further check of inadvertently recorded
vocalizations from an animal other than the focal individual is
provided by the fact that in such a case the vocalizations occurred
simultaneously in different channels, which was easily determined.
However this occurred rarely. Further, the frequency content of
the backpack microphone recording was biased to low frequencies,
whereas external signal leaks were characterized by a lack of these.
Analysis of vocalization patterns
After sorting of syllables and calls, their onset times were used to
determine the temporal association between the vocalizations of
the different animals, both when kept in pairs and in groups.
Cross-correlation was determined using peristimulus time histo-
grams [32] (PSTH). Records of the onset times of the different
vocalizations were used to construct the histograms where the
occurrences of calls (and syllables) of one animal were aligned to
specific vocalizations of another animal. Confidence limits were
constructed using 1000 runs with the source vocalization placed at
random times in stationary epochs of the recording. The strengths
of the calling associations were quantified by calculating a metric
as follows:
Response strength calculation is based on a PSTH consisting of
2680 bins of 50 msec. General response strength:
RS~NbeforezNafter

{NbasebeforezNbaseafter

NbeforezNafter

zNbasebeforezNbaseafter

where N
before
and N
after
are the counts in the 9 bins before and
after the start of the source event ( = call) and N
basebefore
and
N
baseafter
are the first and last 9 bins in the PSTH. Directionality is
calculated as follows:
Directionality~Nafter{Nbefore
NafterzNbefore
The above index was calculated for each combination of
vocalizations and this matrix was further analyzed in R [33].
PSTHs’ that had less than 160 occurrences overall ( =less than 1
per bin on average) were not used to calculate an index. Pearson’s
Chi-squared test for goodness of fit was used to determine whether
the interaction was significant at p,0.05. We tested the hypothesis
that the counts in the four periods used to calculate response
strength did not differ between periods. Only when the counts
were significantly different the response strengths were used in the
matrix.
When an index was not accepted, it was set to missing in the
matrix, and for plotting purposes it was set to zero.
Chronic recording of neuronal activity
To record the electrical activity of RA neurons in free moving
animals we have developed a lightweight (1.0 g) telemetry device
that wirelessly transmits (multi)unit brain activity and that has no
effect on locomotion and vocal activity two days after implantation
(Schregardus et al., 2006; Figure S3B). The transmitters used in
the current study are a further development of the device with
longer battery life (,7 days), more frequency stability and a longer
range at the same weight. Regular telescopic whip (Nagoya
Antenna, Taiwan) or tuned crossed yagi antennae (see above) were
used, that were connected to AOR 5000 or modified (see above)
AOR 8600 receivers.
Each event that was above threshold was captured by peak
detection and written into a 64 byte record as reported earlier
(Jansen and Ter Maat, 1992). Waveforms were then sorted using a
k-means sorting algorithm and further analyzed using custom
software.
Implantation of deep electrodes
The birds were anesthetized using isoflurane inhalation (0.8–
1.8% at 0.5l O2/min). The birds were kept warm using a heating
pad and wrapped in a thin gauze blanket. The skin of the head was
plucked, disinfected and treated with a lidocain (Xylocain Gel 2%,
AstraZeneca) containing cream. After a window was opened over
the bifurcation of the midsagittal sinus which served as reference, a
second window was then made over RA and the dura was opened.
A2MVtungsten electrode (FHC, Bowdoin, USA) was then
lowered into RA using a Luigs and Neumann SM-5-remote
control system manipulator. The reference electrode was a
platinum wire (0,025 mm, Goodfellow) inserted between skull
and dura mater. The connectors of reference and recording
electrodes were fixed in place using dental cement (Tetric evoflow
refill, Ivoclar Vivadent). The connectors serve as a support for the
transmitter (Figure S6).
During insertion of the electrode, electrical activity was
amplified using a DAM 80 (WPI, AC Differential Amplifier)
amplifier, and monitored online using a continuous update of the
ISI of Schmitt-triggered spikes. RA activity of projecting neurons
was relatively easily recognized by the typical ISI histograms of the
spikes [34]. In an initial series of experiments, the location of the
electrode was determined using electrolytic lesions. A lesion was
made at the recording site and every 500 mm when retracting the
electrode. There was a one-for one relationship between finding
the RA-typical ISI and the location of the lesion in RA. With 6
implanted males a lesion was made at the end of the experiment.
In all 6 cases, RA contained the lesion and the recordings
contained a unit that had the interspike interval distribution that is
typical for RA projection neurons [35].
Statistics
Analysis of acoustic parameters was carried out in JMP10 (SAS
Institute Inc, Cary NC, USA). Restricted maximum likelihood
(REML) with pair ID as a random factor, gender and call type as
fixed factors was used to compare acoustical parameters of calls
recorded from pairs in sound boxes. All other analyses were done
in JMP10 or R [33].
Supporting Information
Figure S1 Comparison of the intensities of song, contact
call and soft call. Recording from two pairs in a 16161m
aviary using a central microphone with an all-round sensitivity
pattern. Stretches of recording containing representative exem-
plars the various vocalizations were pasted together to illustrate the
relative sound pressure (dBFS, dB Full Scale) of the different
vocalizations. The soft call selection contains three tet calls (red
dots). The songs are from each of the two males.
(EPS)
Figure S2 Acoustical properties of answered an unan-
swered stack calls. For each of 5 pairs pitch, entropy and
duration were measured of all stack calls that were produced in a
Neural Basis of Social Calling in Songbirds
PLOS ONE | www.plosone.org 7 October 2014 | Volume 9 | Issue 10 | e109334
4 h period. These calls were subdivided into three categories:
answer, answered and no connection. Means and standard
deviations are shown. Each pair is shown in a different color.
No consistent trends are present in any of the three features.
Together, these results do not suggest that the three categories
have different acoustical properties.
(EPS)
Figure S3 Transmitters used in this study. A. Wireless
microphone. The device weighs 0.6 g, including the battery (size
10, pr70 hearing aid battery). Battery life is 12–14 days
transmitting continuously. Range is 5 m. We operate the device
without an external antenna in order to minimize interference
with behavior. B. The electrophysiology transmitter weighs 0.91 g
including two batteries (size 10, pr70). Battery life: 8 days. Range is
10 m. The examples in this figure have no batteries inserted. The
coin is a 1 eurocent coin, diameter: 16.25 mm. C. Circuit diagram
of the microphone transmitter. For more information about the
circuit diagram and printed circuit board layout please contact the
corresponding author.
(EPS)
Figure S4 Selectivity of microphone transmitter record-
ings. The wireless microphones were mounted with the
microphone facing towards the animal’s body. The outward
facing parts of the transmitter were covered either in cloth or
shrink tubing. A. The top panel shows an experiment with 3 pairs
in an aviary measuring 16161 m The wireless (individual)
microphone compares with a general microphone mounted in
the aviary. Whereas the general microphone records all calls and
other sounds made by the six animals, the wireless microphone
mounted on an individual selectively records one bird’s vocaliza-
tions. Note how the second (tet) call is obscured in the general
recording by the call of another individual (red arrows). B. The
bottom panel shows a pair of clippings from a 6 channel recording.
Sonograms are shown of both channels. Clearly, the vocalizations
of the partner animal can be separated from the animal’s own
vocalizations by setting a reasonable threshold, as well as by
viewing the frequency content of the sonograms. Low frequencies
are predominant in the loudest sonograms, probably because the
microphones operate in the near field, whereas the crosstalk is
characterized by the virtual absence of low frequencies.
(EPS)
Figure S5 Accuracy of syllable sorting. Example of a song
syllable sorted on the basis of mean, modal and fundamental
frequencies as well as their standard deviations, duration and
wiener entropy and its SD.
(TIF)
Figure S6 Surgery details. During fixation of the connectors
in the skull surface and the subsequent disconnection of the
electrodes from the input probe of the amplifier used during
implantation, it proved essential to prevent any kind of mechanical
stress on the electrodes. Although the battery life of the device
normally lasted longer than the experiment, sometimes the
batteries had to be exchanged. This involved removing the
transmitter from the implanted connectors, which could cause the
electrode to dislodge. To stabilize the construction, a pin was
cemented in with the electrode connectors. Holding this pin with
small pliers prevented movement of the electrodes and stress on
the skull when the transmitter was plugged in or removed.
(EPS)
Figure S7 Association matrices in five groups of 2 pairs
show significant interactions between males and fe-
males. The males and females are arranged according to
previous pairing in a soundbox. Pairwise interactions between
males or between females did not occur in our experiments. As an
example, TG4 has one pair that engages in mutual calling
(response strength * 100 is color coded), whereas the male of the
other pair answers to the calls of the female of the pair mentioned
previously as shown by the yellow color in the Directionality
matrix.
(EPS)
Figure S8 Association matrices in three groups of 3
pairs. To clarify the absence of calling among males as well as
among females, the males and the females are shown grouped
together for experiment AG2 in the small matrices.
(EPS)
Figure S9 Association matrices in three groups of 4
pairs. The small matrices under AG3 again show how vocal stack
exchanges are limited to contacts between the sexes.
(EPS)
Figure S10 Recordings from RA-implanted males. A
central microphone recorded vocalizations. The RA recordings
are arranged according to type of response. Significance was
assessed by randomizing times of occurrence in the relevant
sections of the recording and calculating the PSTH, and repeating
this 1000 times. Lower and upper limits were determined by the
lower and upper 5% of the counts for each bin. The response was
considered significant when the count was consistently outside
these limits for at least 10 msec. Absence of data indicates that
there were too few occurrences to produce a meaningful PSTH.
(EPS)
Figure S11 Recordings where calls can be unequivocally
attributed to individuals. There were two ways in which this
was achieved. 1.) Recordings from RA-implanted males. A central
microphone recorded all vocalizations. The female carried a
backpack microphone. In this way, female vocalizations in the
general microphone recording were identified. The other stack
calls were then ascribed to the male. In this case the stack calls
were associated with altered RA firing in one male, no RA-
modulation in the other. 2.) The other recording (TG12) was
performed with both females and RA-implanted males carrying a
backpack microphone.
(EPS)
Movie S1 Zebra finch pair with wireless transmitters.
(MOV)
Movie S2 Singing male with electrophysiology trans-
mitter.
(MOV)
Movie S3 Aggressive behavior of a male carrying an
electrophysiology transmitter.
(MOV)
Movie S4 Detailed view of audio transmitter mounted
on a female zebra finch.
(MOV)
Acknowledgments
We thank Henrik Brumm and Albertine Leitao for their comments on the
manuscript, Markus Abels, Willi Jensen and Eva Gumpp for expert
technical assistance, Diederik Schregardus and Maria de Boer for their
contribution in the early phase of the study. Special thanks are due to the
late Rene´ F. Jansen for the important part he played in this work. To him
we dedicate this manuscript.
Neural Basis of Social Calling in Songbirds
PLOS ONE | www.plosone.org 8 October 2014 | Volume 9 | Issue 10 | e109334
Author Contributions
Conceived and designed the experiments: ATM LT MG. Performed the
experiments: LT SS ATM. Analyzed the data: LT ATM. Contributed
reagents/materials/analysis tools: HS ATM. Wrote the paper: ATM LT
MG.
References
1. Nottebohm F, Stokes TM, Leonard CM (1976) Central Control of Song in
Canary, Serinus-Canarius. Journal of Comparative Neurology 165: 457–486.
2. Simpson HB, Vicario DS (1990) Brain pathways for learned and unlearned
vocalizations differ in zebra finches. J Neurosci 10: 1541–1556.
3. Liu WC, Wada K, Jarvis ED, Nottebohm F (2013) Rudimentary substrates for
vocal learning in a suboscine. Nature Communications 4.
4. Speirs EAH, Davis LS (1991) Discrimination by Adelie Penguins, Pygoscelis-
Adeliae, between the Loud Mutual Calls of Mates, Neighbors and Strangers.
Animal Behaviour 41: 937–944.
5. Poesel A, Dabelsteen T (2006) Three vocalization types in the blue tit Cyanistes
caeruleus: a test of the different signal-value hypothesis. Behaviour 143: 1529–
1545.
6. Zann RA (1996) The Zebra Finch; M. PC, editor. Oxford: Oxford University
Press.
7. Beckers GJL, Gahr M (2010) Neural Processing of Short-Term Recurrence in
Songbird Vocal Communication. PLoS One 5: -.
8. Morris D (1954) The Reproductive Behaviour of the Zebra Finch (Poephila-
Guttata), with Special Reference to Pseudofemale Behaviour and Displacement
Activities. Behaviour 6: 271–322.
9. Amy M, Sprau P, de Goede P, Naguib M (2010) Effects of personality on
territory defence in communication networks: a playback experiment with radio-
tagged great tits. Proceedings Biological sciences/The Royal Society 277: 3685–
3692.
10. Yu AC, Margoliash D (1996) Temporal Hierarchical Control of Singing in
Birds. Science 273: 1871–1875.
11. Hahnloser RH, Kozhevnikov AA, Fee MS (2002) An ultra-sparse code underlies
the generation of neural sequences in a songbird. Nature 419: 65–70.
12. Margoliash D (1997) Functional organization of forebrai n pathways for song
production and perception. Journal of neurobiology 33: 671–693.
13. Long MA, Jin DZZ, Fee MS (2010) Support for a synaptic chain model of
neuronal sequence generation. Nature 468: 394–399.
14. Schregardus DS, Pieneman AW, Ter Maat A, Jansen RF, Brouwer TJF, et al.
(2006) A lightweight telemetry system for recording neuronal activity in freely
behaving small animals. Journal of Neuroscience Methods 155: 62–71.
15. Marler P (2004) Bird calls - Their potential for behavioral neurobiology.
Behavioral Neurobiology of Birdsong 1016: 31–44.
16. Seyfarth RMC, D.L. (2010) Production, usage, and comprehension in animal
vocalizations. Brain and Language 115: 92–100.
17. Miller CT, Beck K, Meade B, Wang XQ (2009) Antiphonal call timing in
marmosets is behaviorally significant: interactive playback experiments. Journal
of Comparative Physiology a-Neuroethology Sensory Neural and Behavioral
Physiology 195: 783–789.
18. Mathevon N, Vignal C, Mottin S (2008) Mate recognition by female zebra finch:
Analysis of individuality in male call and first investigations on female decoding
process. Behavioural Processes 77: 191–198.
19. Soltis J, Leong K, Savage A (2005) African elephant vocal communication I:
antiphonal calling behaviour among affiliated females. Animal Behaviour 70:
579–587.
20. Silcox AP, Evans SM (1982) Factors Affecting the Formation and Main tenance
of Pair Bonds in the Zebra Finch, Taeniopygia-Guttata. Animal Behaviour 30:
1237–1243.
21. Caryl PG (1976) Sexual-Behavior in Zebra Finch Taeniopygia-Guttata -
Response to Familiar and Novel Partners. Animal Behaviour 24: 93–107.
22. Elie JE, Mariette MM, Soula HA, Griffith SC, Mathevon N, et al. (2010) Vocal
communication at the nest between mates in wild zebra finches: a private vocal
duet? Animal Behaviour 80: 597–605.
23. Kroodsma DE, Konishi M (1991) A Suboscine Bird (Eastern Phoebe, Sayornis-
Phoebe) Develops Normal Song without Auditory-Feedback. Animal Behaviour
42: 477–487.
24. Gahr M (2007) Sexual Differentiation of the Vocal Control System of Birds.
Genetics of Sexual Differentiation and Sexually Dimorphic Behaviors 59: 67–
105.
25. Lohmann R, Gahr M (2000) Muscle-dependent and hormone-dependent
differentiation of the vocal control premotor nucleus robustus archistriatalis and
the motornucleus hypoglossus pars tracheosyringealis of the zebra finch. Journal
of Neurobiology 42: 220–231.
26. Riebel K, Hall ML, Langmore NE (2005) Female songbirds still struggling to be
heard. Trends in Ecology & Evolution 20: 419–420.
27. Garamszegi LZ, Pavlova DZ, Eens M, Moller AP (2007) The evoluti on of song
in female birds in Europe. Behavioral Ecology 18: 86–96.
28. Price JJ, Lanyon SM, Omland KE (2009) Losses of female song with changes
from tropical to temperate breeding in the New World blackbirds. Proceedings
of the Royal Society B-Biological Sciences 276: 1971–1980.
29. Schmidt MF, Konishi M (1998) Gating of auditory responses in the vocal control
system of awake songbirds. Nature Neuroscience 1: 513–518.
30. Fee MS, Leonardo A (2001) Miniature motorized microdrive and commutator
system for chronic neural recording in small animals. Journal of Neuroscience
Methods 112: 83–94.
31. Tchernichovski O, Nottebohm F, Ho CE, Pesaran B, Mitra PP (2000) A
procedure for an automated measurement of song similarity. Animal Behaviour
59: 1167–1176.
32. Abeles M (1982) Quantification, smoothing, and confidence limits for single-
units’ histograms. J Neurosci Methods 5: 317–325.
33. Team RC (2013) R: A language and environment for statistical computing.
R Foundation for Statistical Computing, Vienna, Austria.
34. Hahnloser RH, Kozhevnikov AA, Fee MS (2006) Sleep-related neural activity in
a premotor and a basal-ganglia pathway of the songbird. Journal of
neurophysiology 96: 794–812.
35. Spiro JE, Dalva MB, Mooney R (1999) Long-ra nge inhibition within the zebra
finch song nucleus RA can coordinate the firing of multiple projection neurons.
J Neurophysiol 81: 3007–3020.
Neural Basis of Social Calling in Songbirds
PLOS ONE | www.plosone.org 9 October 2014 | Volume 9 | Issue 10 | e109334
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