J Neurophysiol 113: 1480 –1492, 2015.
First published December 4, 2014; doi:10.1152/jn.00611.2014.
Neural responses in songbird forebrain reflect learning rates, acquired
salience, and stimulus novelty after auditory discrimination training
Brittany A. Bell, Mimi L. Phan, and David S. Vicario
Rutgers University, New Brunswick, New Jersey
Submitted 14 August 2014; accepted in final form 3 December 2014
stimulus specific adaptation; lateralization; songbird; electrophysiology; individual recognition
IN SOCIAL SPECIES, INDIVIDUAL
recognition can make an important
contribution to mediating social interactions between individuals who encounter each other repeatedly as members of a
population. For example, birds use vocal signals in a range of
reproductive and social interactions (Zann 1996), and vocalizations can provide a basis for individual recognition. In a
songbird, the zebra finch (Taeniopygia guttata), young males
learn a single song early in life from adult tutors through a
process of vocal imitation with many parallels to speech
acquisition (Doupe and Kuhl 1999); although the copies are
good, they contain variations that make each song unique to the
individual (Immelmann 1969; Miller 1979b). Behavioral studies show that adult zebra finches are able to recognize the
unique songs and calls of individual conspecifics with which
they have interacted socially, e.g., a tutor or mate (Miller
1979a; Riebel 2000; Vignal et al. 2004, 2008). Thus adult
Address for reprint requests and other correspondence: B. A. Bell, 152
Frelinghuysen Rd., Piscataway, NJ 08854 (e-mail: bab255@scarletmail.
rutgers.edu).
1480
males form recognition memories of new songs that they hear,
although they no longer copy new vocal signals. Songs used in
various social contexts can become naturally associated with
significant behavioral outcomes (e.g., success or failures in
courtship and territorial defense). However, it is unknown how
the neural representation of each specific vocalization is updated by these social interactions.
In this study, we explored the hypothesis that operant training modulates long-term neural memories for reinforcementpredictive stimuli in auditory regions of the adult brain. We
combined established methods for creating and assessing auditory associations in the songbird brain to investigate how the
behavioral relevance of auditory stimuli is reflected in the
neural activity of sensory processing areas. First, zebra finches
were trained to recognize individual songs and their behavioral
relevance through operant conditioning, as a model of the way
particular songs may be reinforced during natural social interactions. Then, neurophysiological responses to reinforced, familiar, and novel songs were recorded in awake birds to
quantify the representations of familiar sensory stimuli and
determine how differential reinforcement during training affects those representations in separate brain areas. Although
sensory responses have traditionally been considered to be
fixed, they are increasingly understood to be modulated by
prior experience (Gentner and Margoliash 2003; Gilbert et al.
2009; Thompson and Gentner 2010; Weinberger 1998). Recordings were made in two auditory processing areas of the
songbird forebrain where neuronal memories can be detected
in immediate early gene and electrophysiological responses:
the caudomedial mesopallium (CMM) and caudomedial nidopallium (NCM) (Menardy et al. 2012; Woolley and Doupe
2008). CMM and NCM receive auditory projections from
primary auditory areas (Field L complex) and may be analogous to a secondary auditory cortex, or to superficial layers of
mammalian A1 (Karten 1991; Vates et al. 1996; Wang et al.
2010). Neurons in these areas respond more strongly to conspecific vocalizations than other sounds, showing a response
bias for stimuli that are behaviorally relevant to subjects (Chew
et al. 1995, 1996; Mello et al. 1992). In addition, during awake
neurophysiological recordings, neurons in NCM and CMM
undergo a process of stimulus-specific adaptation; responses
are robust to the initial presentations of each stimulus and then
decrease over subsequent presentations to reach an asymptote
(Chew et al. 1995; Smulders and Jarvis 2013). The rate at
which multiunit responses to song stimuli decrease over repeated presentations can be used to assess the familiarity of
stimuli (Chew et al. 1995; Phan et al. 2006). These songspecific changes in response to unreinforced exposure last
hours to days (Chew et al. 1996; Phan et al, 2006). Further-
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Bell BA, Phan ML, Vicario DS. Neural responses in songbird forebrain
reflect learning rates, acquired salience, and stimulus novelty after auditory
discrimination training. J Neurophysiol 113: 1480–1492, 2015. First published December 4, 2014; doi:10.1152/jn.00611.2014.—How do social
interactions form and modulate the neural representations of specific
complex signals? This question can be addressed in the songbird
auditory system. Like humans, songbirds learn to vocalize by imitating tutors heard during development. These learned vocalizations are
important in reproductive and social interactions and in individual
recognition. As a model for the social reinforcement of particular
songs, male zebra finches were trained to peck for a food reward in
response to one song stimulus (GO) and to withhold responding for
another (NoGO). After performance reached criterion, single and
multiunit neural responses to both trained and novel stimuli were
obtained from multiple electrodes inserted bilaterally into two songbird auditory processing areas [caudomedial mesopallium (CMM) and
caudomedial nidopallium (NCM)] of awake, restrained birds. Neurons
in these areas undergo stimulus-specific adaptation to repeated song
stimuli, and responses to familiar stimuli adapt more slowly than to
novel stimuli. The results show that auditory responses differed in
NCM and CMM for trained (GO and NoGO) stimuli vs. novel song
stimuli. When subjects were grouped by the number of training days
required to reach criterion, fast learners showed larger neural responses and faster stimulus-specific adaptation to all stimuli than slow
learners in both areas. Furthermore, responses in NCM of fast learners
were more strongly left-lateralized than in slow learners. Thus auditory responses in these sensory areas not only encode stimulus
familiarity, but also reflect behavioral reinforcement in our paradigm,
and can potentially be modulated by social interactions.
NEURAL RESPONSES AFTER AUDITORY DISCRIMINATION TRAINING
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Training
METHODS
Electrophysiology
Subjects
At the conclusion of operant training, subjects underwent surgical
preparation for the neural recording. Each bird was anesthetized
(1.5–2.0% isoflurane in oxygen), and a craniotomy was performed to
access the caudal forebrain around the bifurcation of the midsagittal
sinus. A recording chamber was formed out of dental cement, and a
metal pin was cemented to the skull for immobilizing the head during
subsequent electrophysiological recordings. Allowing for complete
recovery, 2 days later, the awake bird was comfortably restrained (in
a plastic tube) and the head pin was clamped to a stereotaxic apparatus
located in a soundproof booth (IAC, Bronx, NY). A multielectrode
microdrive (Eckhorn, Thomas Recording, Giessen, Germany) was
used to lower 16 tungsten microelectrodes (Type ESI2ec, impedance:
2– 4 M⍀, Thomas Recording) bilaterally into the brain, targeting areas
NCM and CMM (16 electrodes total, 4 in each area in each hemisphere). The microelectrodes were initially lowered to a depth of 500
␮m and then slowly lowered from this depth while novel songs were
played as search stimuli through a speaker placed directly (30 cm) in
front of subjects. Once robust responses to song were located on all
Adult male zebra finches (n ⫽ 11, ⬎120 days of age) that had been
reared in our aviary (on a 14:10-h light-dark cycle), but were naive to
discriminatory training, were used as subjects. For 5 days prior to start
of operant training, the birds were isolated and acclimated to a
custom-built wire chamber (45.72 ⫻ 29.21 ⫻ 27.94 cm) located
inside of a sound-attenuated box (inside dimensions: 82.55 ⫻ 33.66 ⫻
38.10 cm; outside dimensions: 91.44 ⫻ 40.64 ⫻ 48.26 cm), where
they lived and were trained. Birds were food deprived overnight on
days prior to training sessions (during which food was used as a
reward for correct responses) and then given food ad libitum at the
end of each daily training session; they had access to cuttlebone and
water throughout training. During weekends, when the subjects were
not training, they were given food ad libitum. All experiments were
performed in accordance with protocols approved by the Rutgers
University Institutional Animal Care and Use Committee (Protocol
Number 02-217).
Birds were trained 5 days a week for 6 h a day. After acclimation,
the subjects were shaped to peck a sensor, breaking an infrared beam,
to stimulate a food reward (as in Gess et al. 2011) using the ARTSy
program (David Schneider, Columbia University, New York, NY).
After 5 days of shaping, the birds began operant GO/NoGO training,
during which subjects had to peck the sensor to hear a stimulus, and
then respond correctly to that stimulus based on its GO/NoGO
categorization. The two stimuli used in each discrimination were
single-motif songs of unfamiliar male zebra finches; these stimuli
were randomly assigned to the GO and NoGO categories. Each
discrimination used a set of stimuli that were 50 –70% different from
one another and of similar length (as determined by an open source
software program, SAP 2011; Tchernichovski). During a trial, if the
GO stimulus was played, the correct response was to peck again for
a food reward; however, for a NoGO stimulus, the correct response
was to withhold pecking. Correct GO responses were rewarded with
access to birdseed for 6 s, and incorrect NoGO responses were
punished with the chamber lights being extinguished for 16 s. Each
trial was concluded if there was no response within 6 s. Subjects were
able to initiate trials immediately after one another, and thus could
perform unlimited trials to reach the accuracy criterion, defined as at
least 80% correct responses on two sequential sets of 100 trials. After
meeting criterion, subjects learned to discriminate a second pair of
stimuli. Before electrophysiological recording, birds were tested with
all four song stimuli in a randomly interleaved set and had to meet
criterion for both pairs of training stimuli.
Probe Trials
To investigate the behavioral strategies used to successfully perform the operant auditory discrimination, two novel unreinforced
stimuli (probe stimuli) were added to the paradigm in a subset of birds
(n ⫽ 4). After subjects reached criterion for both operant discriminations, correct GO responses and incorrect NoGO responses were
reinforced 80% of the time (rather than 100%) to prepare for probe
trials. After this, two probe stimuli, which were each equally different
(within 10% similarity, Tchernichovski, SAP 2011) from the GO and
NoGO stimuli, were added to the training trials. When these songs
played as trial stimuli, the behavioral responses they evoked were not
reinforced, either through reward or punishment. Each probe stimulus
was played 10 times in every 100 trials, for a week. High or low levels
of responding to probe stimuli can indicate a behavioral strategy in
which only one stimulus category is truly learned by the subject
during the discrimination learning.
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more, the expression of ZENK, an immediate early gene
related to learning and the formation of memories (Bolhuis et
al. 2001; Mello et al. 1992, 1995; Stripling et al. 2003), is
increased during operant training in both NCM and CMM of
zebra finches (Gentner et al. 2004). After training, increased
ZENK expression (as well as increased electrophysiological
activity) in CMM remains associated with playback of trained
stimuli, while ZENK expression (and electrophysiological activity) in NCM habituates as stimuli become familiar (Gentner
et al. 2004; Gentner and Margoliash 2003; Thompson and
Gentner 2010).
In addition, NCM shows lateralized neural responses to
auditory stimuli, and the direction of lateralization can be
affected by changes in the acoustic environment (Moorman et
al. 2012; Phan and Vicario 2010; Yang and Vicario 2012). This
lateralization of neural activity is of specific interest because
the human brain is also lateralized for language; both speech
production and perception are predominantly left hemispheric
processes, although both the mechanism and function of lateralized processing remain unclear. In NCM, hemispheric differences depend on developmental experience (Phan and Vicario 2010) and may be related to the quality of a songbird’s
auditory learning. When auditory information is blocked from
reaching one hemisphere by lesioning the thalamic auditory
relay nucleus of songbirds (nucleus ovoidalis), birds show
differential deficits in auditory discrimination learning according to which hemisphere received the lesion (Cynx et al. 1992).
Therefore, we hypothesized that auditory processing areas in
the two hemispheres might contribute differently to performance in the operant conditioning paradigm.
Multiunit and single-unit responses were analyzed to identify effects of discrimination learning on responses to familiar
and novel stimuli in NCM and CMM. We found individual
differences in the speed of acquisition of the auditory discrimination, as others have reported (Atienza et al. 2002; Guillette
et al. 2011; Katsnelson et al. 2011; Range et al. 2006; for
review, Weinberger 2011). Therefore, responses were further
analyzed to explore the relationship between speed of acquisition and the associated neural responses. The results shed
light on how reinforcement-predictive memories of conspecific
songs are represented by sensory neurons in the brain, altered
by experience, and accessed during recognition.
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NEURAL RESPONSES AFTER AUDITORY DISCRIMINATION TRAINING
electrodes, experimental sets of song stimuli were played while
recording multiunit neural responses (⫻19,000, band-pass filtered:
0.5–5 kHz; Spike 2 software, CED, Cambridge, UK). Stimulus sets
consisted of novel (3 stimuli), trained (GO and NoGO) and probe
songs equated for loudness (75-dB average; sampling rate: 44,444.4
Hz). Songs were presented for 25 repetitions each, in a shuffled order
(8-s interspike interval).
Histology
Isolation of Single Units
Single units were extracted from multiunit recording data, using
Spike 2 software (CED, Cambridge, UK). For each electrode channel,
an initial threshold for spike detection was set based on visual
inspection. The shapes of spikes that crossed the threshold were used
to create and update multiple waveform templates, each for spikes of
similar shape. Principal components analysis and interval histograms
were used to reclassify and group similar spikes together by their size
and shape. To be accepted as a single unit, ⱕ2% of the interspike
intervals had to be ⬍2 ms.
A
B
The neural response of each multiunit site to each stimulus repetition was quantified by subtracting the root mean square of activity
during a control period (0.5 s) before stimulus onset from the root
mean square of activity during stimulus playback. Absolute response
magnitude (ARM), an index of response strength, was defined as the
mean neural response on trials 2– 6. The response adaptation rate, an
index of “neuronal” memory, for each stimulus at each multiunit site,
was calculated as the slope of responses from trials 6 –25, divided by
the mean ARM over the same trials to normalize for the response
magnitude at each site. Sites were excluded from further analyses if:
1) they were not histologically verified to be within NCM or CMM;
or 2) responses to two out of the three stimulus categories (GO,
NoGO, novel) were not statistically different from responses during
the control period. The latter criterion resulted in exclusion of only 5
sites (2 in NCM, 3 in CMM) of 152 sites total (67 in NCM, 85 in
CMM).
Single-unit responses were quantified similarly to multiunit responses; spike rates were calculated by subtracting the spike rate (per
unit time) during 0.5 s before stimulus onset from the spike rate during
the stimulus period to get a response rate for each trial. The firing rates
used in single-unit analyses were the average response spike rates to
the first seven presentations of each stimulus. Response adaptation for
single units was quantified for each stimulus by taking the slope of the
regression line of spike-rate responses on trials 1–25 and dividing by
the average response magnitude for those trials, to normalize. To
compare single-unit responses to learned vs. novel stimuli, d= values
were calculated using a unit’s average firing rate and the trial-to-trial
variance in spike rates for stimulus trials (Green and Swets 1966;
Theunissen and Doupe 1998). A d= was computed by subtracting the
average firing rate (FR) response to one stimulus category (i.e., novel)
from the average FR response to another stimulus type (i.e., GO),
multiplying by 2, and dividing by the square root of the sum of the
variances for those two measures (as below).
d=Trials1⫺7 ⫽
2
៮ 兲 ⫺ 共FR
៮ 兲兴
关共FR
GO
Novel
兹共␴GO ⫺ ␴Novel兲
2
2
Three sets of d= values were computed for both NCM and CMM to
compare: 1) GO stimuli to novel stimuli; 2) NoGO stimuli to novel
stimuli; and 3) GO stimuli to NoGO stimuli. An additional set of d=
values was calculated by comparing single-unit spiking responses to
pairs of novel conspecific stimuli, to get a baseline measure of the
neural discriminability of an arbitrary pair of song stimuli in each
brain area.
Statistical Analyses
C
Fig. 1. Histology and electrode placement in caudomedial nidopallium (NCM)
and caudomedial mesopallium (CMM). Sites used for data analyses were
confirmed to be in either CMM (A) or NCM (B) by sending electrolytic current
(20 ␮A for 12 s) through electrodes at the conclusion of the electrophysiological experiment to produce lesions (scale bar ⫽ 500 ␮m). Brains were then
sectioned, and slices were stained with cresyl violet and visualized under a
light microscope to confirm placement. C: figure showing the boundaries of
avian auditory areas (NCM, L2, CMM). aNCM, rdNCM, rvNCM, cdNCM,
and cvNCM: apical, rostrodorsal, rostroventral, caudodorsal, and caudoventral
NCM, respectively. [From Sanford et al. (2010). Reprinted with permission
from Wiley.]
To test for statistical differences between groups, nonparametric
tests were performed when possible, and parametric analyses of
variance (ANOVAs) were used only when repeated-measures and
categorical factors (interactions) needed to be tested simultaneously.
All statistical tests were run two-tailed with the criterion for statistical
significance set at ␣ ⫽ 0.05. Multiunit ARM and adaptation rate
values were analyzed in both NCM and CMM on a site-by-site basis,
using repeated-measures ANOVAs, to isolate any differences in
responding based on familiarity of stimulus, valence of stimulus and
subject speed of learning. When overall significant effects were
detected, post hoc Bonferroni tests were conducted for comparisons of
interest to detect which groups were significantly different. Nonparametric Friedman’s tests were run on single-unit firing rate and adaptation rate data to test for effects of training on auditory responses.
Significant effects detected by Friedman’s tests were further investigated with post hoc Wilcoxon sign-rank tests (using Bonferronicorrected P values as criterion for significant effects). Effects of
training on single-unit d= values were tested using nonparametric
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At the conclusion of the neural recording, electrolytic lesions were
made at recording sites (20 ␮A for 12 s). Subsequently, subjects were
anesthetized with Nembutal and then perfused with saline followed by
4% paraformaldehyde. Postfixation, the brains were sectioned at 50
␮m using a vibratome (Series 1000), placed onto slides, and stained
with cresyl violet. Sections were visualized under a light microscope
to confirm lesions and reconstruct recording sites (Fig. 1). Data from
electrodes found to have been placed outside the areas of interest were
excluded from analyses.
Data Analysis
NEURAL RESPONSES AFTER AUDITORY DISCRIMINATION TRAINING
Distribution of Speeds to Reach Criterion
Number of Birds
4
3
Fast Learners
2
Slow Learners
1.20; P ⬍ 0.05, Fig. 3A). NCM sites responded more strongly
to positively reinforced GO stimuli than to punished NoGO
stimuli. However, responses to novel stimuli were intermediate, not significantly different from either of these reinforcement-predictive stimuli. There was also a significant main
effect of training on multiunit adaptation rates [F(2,104) ⫽ 7.45,
P ⬍ 0.001] in NCM. This effect was driven by the significantly
1
A
0
1483
B
NCM
90
CMM
85
80
75
70
65
60
RESULTS
Multiunit electrophysiological data were collected from 152
responsive recording sites histologically verified to be in NCM
(67 sites) or CMM (85 in CMM) in 11 adult male zebra
finches. A total of 86 single units were isolated offline from the
multiunit recordings: 43 units in NCM, and 43 units in CMM.
Effects of Training on Multiunit Auditory Responses in NCM
and CMM
Multiunit auditory responses in both NCM and CMM differed between stimuli reinforced during operant training and
stimuli that were novel to these subjects. Training affected both
ARMs and adaptation rates, but the effects differed between
NCM and CMM. ARMs (trials 2– 6) and adaptation rates
(trials 6 –25) were analyzed in ANOVAs using stimulus type
(GO, NoGO, and novel) as a repeated measure at each site.
Data from NCM and CMM were analyzed separately.
NCM. In NCM recordings, there was a significant main
effect of training on multiunit ARMs [F(2,104) ⫽ 3.12, P ⬍
0.05]. Bonferroni post hoc tests revealed that this effect was
driven by a significant difference between the mean (M) ARMs
to GO (M ⫽ 84.04 ⫾ 1.20) vs. NoGO stimuli (M ⫽ 78.43 ⫾
GO
ADAPTATION RATE
C 0.0
NoGO
Novel
D
GO
NoGO
Novel
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
E 100
F
80
ARMs
Kolmogorov-Smirnov tests to detect both differences in the central
tendencies, as well as the variances of these values. Finally, regressions were run on d= data to test the within-unit correlations between
responses to GO, NoGO and novel stimuli.
Due to the variance in subjects’ latencies to reach behavioral
criterion, subjects were median-split into two groups (slow and fast
learners) according to their average speed to acquire the auditory
discriminations (Fig. 2). Neurophysiological data were analyzed with
learning category as a factor, and direct comparisons were made
between the two groups to investigate whether learning speed was
related to neural responses to song in our two auditory processing
areas of interest. Then, Kolmogorov-Smirnov tests were run to compare the multiunit ARMs and adaptation rates of slow to those of fast
learners, as well as to test for lateralization of ARM and adaptation
rate measures within these groups. In addition, a regression was run to
test the hypothesis that probe trial responsiveness was related to the
following: multiunit rates of adaptation (familiarity) to trained stimuli,
or the speed with which subjects acquired auditory discriminations.
60
40
20
0
0
25
25
Trial
25
0
25
25
25
Trial
Fig. 3. Effects of training on absolute response magnitudes (ARMs) and
multiunit adaptation rates. The mean ARMs and adaptation rates of multiunit
recording sites in NCM and CMM for subjects (n ⫽ 11) in response to GO,
NoGO and novel auditory stimuli are shown. A: there was a significant main
effect of training on ARMs in NCM (P ⬍ 0.05, repeated-measures ANOVA):
responses were stronger for GO stimuli than NoGO stimuli (P ⬍ 0.05,
Bonferroni post hoc test). B: there was also a significant main effect of training
on ARMs in CMM (P ⬍ 0.0001, repeated-measures ANOVA). Here, novel
stimuli evoked lower ARMs than reinforcement-predictive (GO and NoGO)
stimuli did (P ⬍ 0.001 in both cases, Bonferroni post hoc test). In addition,
multiunit adaptation rates showed main effects of training in NCM (P ⬍ 0.001,
repeated-measures ANOVA) and CMM (P ⬍ 0.05, repeated-measures
ANOVA). C: in NCM, NoGO stimuli were adapted to more slowly than GO
(P ⬍ 0.01, Bonferroni post hoc test) and novel stimuli (P ⬍ 0.01, Bonferroni
post hoc test). D: in CMM, NoGO stimuli were adapted to more slowly than
GO stimuli (P ⬍ 0.05, Bonferroni post hoc test). Error bars depict withinsubjects standard error. ARMs for representative multiunit responses (on trials
6 –25) are shown for a novel, GO and NoGO stimulus for NCM (E) and CMM
(F). Regression lines depict the adaptation of neural activity at each recording
site. Brackets indicate significantly different comparisons.
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Fig. 2. Speed of subject’s acquisition of auditory discriminations. Individuals
vary in the speed with which they reach behavioral criterion (80% accuracy) on
the operant GO (pecking for food reward in response to one song stimulus)/
NoGO (withhold responding for another) task. The distributions of days to
reach criterion for discriminations 1 and 2, for all subjects, are shown. Subjects
were median-split into two groups: faster (n ⫽ 5, shown in black) and slower
(n ⫽ 6, shown in gray) learners, by their average speed to acquire both auditory
discriminations.
ARMs
Days
NEURAL RESPONSES AFTER AUDITORY DISCRIMINATION TRAINING
Effects of Speed of Learning on Multiunit Auditory
Responses
Subjects were divided into slow and fast learners, based on
a median split of the speed of acquisition of behavioral discrimination (Fig. 2). Slow learners (n ⫽ 6) took a greater
number of trials to reach criterion on two discriminations (M ⫽
5,660 ⫾ 1,210) than fast learners (n ⫽ 5, M ⫽ 2,980 ⫾ 606).
Neurophysiological responses were compared between 73 multiunit sites in fast-learning subjects and 79 multiunit sites in
slow-learning subjects. The multiunit results in both NCM and
CMM showed an interesting pattern of differences in auditory
responses between the learning groups.
ARMs and adaptation rates. The speed with which birds
learned the auditory discriminations was related to both the
strength of neural responses and the rate at which these
responses adapted during neurophysiological recording. In
both NCM and CMM, faster learners showed significantly
higher ARMs in Kolmogorov-Smirnov tests [M(NCM) ⫽
89.24 ⫾ 5.57, M(CMM) ⫽ 91.39 ⫾ 8.71] to all song stimuli than
slow learners [M(NCM) ⫽ 72.80 ⫾ 4.65, M(CMM) ⫽ 68.85 ⫾
4.53; D(NCM) ⫽ 0.21, P ⬍ 0.05, D(CMM) ⫽ .26, P ⬍ 0.01; Fig.
4, A and B]. In parallel, faster learners showed faster adaptation
[M(NCM) ⫽ ⫺0.57 ⫾ 0.079, M(CMM) ⫽ ⫺0.76 ⫾ 0.17] to all
song stimuli than slow learners [M(NCM) ⫽ ⫺0.32 ⫾ 0.040,
M(CMM) ⫽ ⫺0.15 ⫾ 0.039] in NCM and CMM [D(NCM) ⫽
0.23, P ⬍ 0.05, D(CMM) ⫽ 0.40, P ⬍ 0.001, Fig. 4, C and D].
NCM lateralization. In addition to the overall higher responses in faster learners, the speed to acquire auditory discriminations was related to the direction of lateralization of
auditory responses in NCM. Animals that learned faster
showed significantly higher ARMs in the left hemisphere (M ⫽
114.27 ⫾ 8.37) than in the right hemisphere (M ⫽ 64.21 ⫾
4.23) in response to all song stimuli (Kolmogorov-Smirnov,
D ⫽ 0.65, P ⬍ 0.0001, Fig. 5B). On the other hand, animals
Cumulative Frequency
A
C
B
NCM
1
0.5
0.5
0
0
50
100
Fast Learners
Slow Learners
150
200
ARMs
0
0
D
1
0.5
0
-3
CMM
1
50
100
150
200
0
1
ARMs
1
0.5
-2
-1
0
Adaptation Rates
1
0
-3
-2
-1
Adaptation Rates
Fig. 4. Speed of acquisition and neural responses in NCM and CMM. When
subjects were median-split into two groups, according to their speed to reach
criterion on the operant task, in NCM (A) and CMM (B), fast learners showed
stronger ARMs to all song stimuli than slow learners [NCM, P ⬍ 0.01; CMM,
P ⬍ 0.001, Kolmogorov-Smirnov (KS) test]. In addition, in NCM (C) and
CMM (D), fast learners showed faster adaptation to all song stimuli than slow
learners (NCM, P ⬍ 0.05; CMM, P ⬍ 0.001, KS test).
that learned the discriminations more slowly showed no significant difference between the hemispheres (right: M ⫽ 85.50 ⫾ 7.75;
left: M ⫽ 68.73 ⫾ 5.16) of NCM (Kolmogorov-Smirnov, D ⫽
0.18, P ⫽ 0.32, Fig. 5B). Multiunit adaptation rates did not
show this type of hemispheric interaction in NCM (fast: D ⫽
0.18, P ⫽ 0.62, slow: D ⫽ 0.21, P ⫽ 0.16). In comparisons of
CMM data, the fast- and slow-learning groups did not show
significant lateralization of ARMs [Kolmogorov-Smirnov
D(Fast) ⫽ 0.24, P ⫽ 0.25, D(Slow) ⫽ 0.18, P ⫽ 0.18] or
adaptation rates, although there was a strong trend for the slow
learners to have faster adaptation in the left hemisphere [D(Fast) ⫽
0.27, P ⫽ 0.14; D(Slow) ⫽ 0.22, P ⫽ 0.05].
Interaction between learning speed and training effects. The
speed with which the subjects learned the discriminations also
interacted with the effects of training on multiunit neural
responses. As detailed above, when repeated-measures ANOVAs
were run to test the effects of learning on ARMs and adaptation
rates, there were main effects of training on the ARMs and
adaptation rates in NCM and CMM. However, when learning
speed was used as a factor in the repeated-measures ANOVA,
there were also significant interactions between speed of learning and the reinforcement-predictive value of stimuli in CMM
ARM data [F(2,118) ⫽ 4.14, P ⬍ 0.05, Fig. 6A] and in NCM
adaptation data [F(2,102) ⫽ 4.18, P ⬍ 0.05, Fig. 6B]. Fast
learners showed slower adaptation to NoGO stimuli (M ⫽
⫺0.35 ⫾ 0.024) than novel stimuli (M ⫽ ⫺0.58 ⫾ 0.017) in
NCM (Bonferroni: P ⬍ 0.01). They also showed stronger
ARMs to NoGO stimuli (M ⫽ 97.1 ⫾ 2.15) than novel (M ⫽
⫺0.35 ⫾ 0.024) stimuli in CMM (Bonferroni: P ⬍ 0.01). In
contrast, slow learners showed stronger ARMs to GO stimuli
(M ⫽ 74.25 ⫾ 1.61) than novel stimuli (M ⫽ 62.72 ⫾ 1.95) in
CMM (P ⬍ 0.01). Therefore, if slower adaptation and stronger
ARMs are indicative of neural memories for learned songs (as
suggested by previous results), then these interactions between
learning speed and the strength of memory for GO and NoGO
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slower adaptation to NoGO stimuli (M ⫽ ⫺0.33 ⫾ 0.022) than
GO stimuli (M ⫽ ⫺0.43 ⫾ 0.020) and novel stimuli (M ⫽
⫺0.47 ⫾ 0.069; as shown by post hoc tests; Bonferroni P ⬍
0.01, Fig. 3C). Slower adaptation to NoGO stimuli would
suggest that subjects show a stronger memory for NoGO
stimuli than novel or GO stimuli, based on earlier study of
stimulus-specific adaptation (Chew et al. 1995; Phan et al.
2006; Fig. 3E for representative adaptation profiles).
CMM. In the CMM recordings, there was a significant main
effect of training on multiunit ARMs [F(2,120) ⫽ 11.05, P ⬍
0.0001]. Bonferroni post hoc tests revealed that this effect was
driven by significantly lower mean ARMs to novel (M ⫽ 69.46 ⫾
1.43) vs. both reinforcement-predictive stimuli [M(GO) ⫽ 80.27 ⫾
1.30; M(NoGO) ⫽ 77.77 ⫾ 1.22], as shown by Bonferroni post
hoc tests (P ⬍ 0.001, Fig. 3B). Multiunit adaptation rates in
CMM also showed a significant main effect of training [F(2,120) ⫽
3.77, P ⬍ 0.05]. As in NCM, this effect in CMM was driven by
slower adaptation to NoGO stimuli (M ⫽ ⫺0.32 ⫾ 0.017) than GO
stimuli (M ⫽ ⫺0.44 ⫾ 0.024), as shown by Bonferroni post
hoc tests (P ⬍ 0.05, Fig. 3D). Slower adaptation, suggesting a
stronger memory for NoGO compared with novel and GO
stimuli, may have developed through a behavioral strategy in
which subjects focus on learning one of the stimuli (NoGO) to
complete the auditory discrimination (see Fig. 3F for representative adaptation profiles).
Cumulative Frequency
1484
NEURAL RESPONSES AFTER AUDITORY DISCRIMINATION TRAINING
Percent of Correct Response
A
100
90
80
70
60
50
40
30
20
10
0
First Discrimination
Slow Learners
Fast Learners
0
5
10
15
20
25
30
35
40
Day of Training
B
NCM
Left
Right
100
ARMs
80
60
40
In NCM, there was a significant main effect of familiarity on
ARMs [F(3,72) ⫽ 6.23, P ⬍ 0.001]. Probe stimuli (M ⫽
77.50 ⫾ 5.82) evoked significantly weaker responses than GO
(M ⫽ 100.68 ⫾ 3.21), NoGO (M ⫽ 94.95 ⫾ 2.58) and novel
(M ⫽ 99.70 ⫾ 2.58) stimuli, as shown in Bonferroni post hoc
tests (P ⬍ 0.05 for all comparisons, Fig. 7A). Multiunit
adaptation rates in NCM, however, were unaffected by probe
familiarity [F(3,72) ⫽ 1.32, P ⫽ 0.27, Fig. 7C].
In CMM, there were significant main effects of familiarity
on both ARMs and adaptation rates [F(3,60) ⫽ 4.17, P ⬍ 0.01;
F(3,60) ⫽ 4.28, P ⬍ 0.01]. Bonferroni post hoc tests on CMM
ARMs showed that probe stimuli elicited significant lower
responses (M ⫽ 80.54 ⫾ 5.75) than both reinforcementpredictive stimuli, GO (M ⫽ 99.53 ⫾ 3.24) and NoGO (M ⫽
102.38 ⫾ 4.22; P ⬍ 0.05 for both comparisons, Fig. 7B).
Similarly, the rates of adaptation of neural activity in CMM
multiunits were significantly slower for probe stimuli (M ⫽
⫺0.26 ⫾ 0.14) than GO (M ⫽ ⫺0.66 ⫾ 0.059), NoGO (M ⫽
⫺0.64 ⫾ 0.050) and novel (M ⫽ ⫺0.64 ⫾ 0.039) stimuli (P ⬍
0.05 for all comparisons, Fig. 7D). The results for unreinforced
probe stimuli are consistent with previous studies which indicate that passively familiar song stimuli are adapted to more
A
20
CMM
100
0
Slow Learners
stimuli suggest that the two groups may use different behavioral strategies to complete the behavioral task. Fast learners
appear to attend more strongly to the NoGO stimulus, while
slow learners attend to GO to perform the discrimination.
80
GO
ARMS
Fast Learners
Fig. 5. Speed of acquisition and lateralization of NCM. A: learning curves,
showing the latency to reach behavioral criterion (80%) on the first discrimination, in examples from a fast and a slow learner. B: in NCM, fast learners
showed stronger ARMs in the left hemisphere compared with the right (P ⬍
0.001, KS test) for all stimuli. This asymmetry is the opposite from what has
been reported in naive birds (Phan and Vicario 2010). In contrast, slow learners
were not significantly lateralized. Lateral differences were not seen in CMM
recordings.
NoGO
60
Novel
40
20
0
B
Slow Learners
0
Fast Learners
NCM
In a subset of animals (n ⫽ 4), performance on unreinforced
probe trials was used to further explore the behavioral strategies employed in the auditory discriminations. High or low
levels of responding to probe stimuli are hypothesized to reflect
behavioral strategies in which only one stimulus category (GO
vs. NoGO) is selectively learned by the subject during the
discrimination training (cf., Morisaka and Okanoya 2008). For
example, subjects that respond to probes more than 50% of the
time should be more familiar with NoGO stimuli than GO
stimuli, while those that seldom respond to probes are more
familiar with GO than NoGO.
Individual subjects did show different tendencies to respond
to probes, and these data allowed relationships between behavioral and neural responses to be assessed. Analysis of neural
responses to probe stimuli demonstrated that the effects of
training caused changes in absolute responses and adaptation
rates that were not due (solely) to stimulus familiarity. Repeatedmeasures ANOVAs were run to compare neural responses to
reinforcement-predictive (GO and NoGO), novel and probe
stimuli, in both NCM and CMM.
ADAPTATION RATES
Behavioral and Neural Responses to Probe Stimuli
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
Fig. 6. Speed of acquisition and resultant neural representations. Repeatedmeasures ANOVAs compared responses to GO (solid bars), NoGO (open bars)
and novel (hatched bars) stimuli to test the effects of learning on ARMs and
adaptation rates in NCM and CMM. There were main effects of training on the
ARMs and adaptation rates in NCM and CMM. However, there were also
significant interactions between speed of learning and stimulus reinforcementprediction on the CMM ARM data (P ⬍ 0.05; A) and the NCM adaptation data
(P ⬍ 0.05; B). Bonferroni post hocs revealed that fast learners showed slower
adaptation to NoGO stimuli than novel stimuli in NCM (P ⬍ 0.01), as well as
stronger ARMs to NoGO stimuli than novel in CMM (P ⬍ 0.01). Slow
learners showed stronger ARMs to GO stimuli than novel stimuli in CMM
(P ⬍ 0.01). Error bars denote within-subjects standard error values.
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120
1485
NEURAL RESPONSES AFTER AUDITORY DISCRIMINATION TRAINING
A
B
NCM
CMM
110
ARMs
100
90
80
70
60
C
GO
NoGO
Novel
Probe
D
GO
NoGO
Novel
Probe
0.0
-0.2
-0.4
-0.6
-0.8
slowly than novel stimuli, due to stimulus-specific adaptation
(Chew et al. 1995; Phan et al. 2006).
When the difference between GO and NoGO adaptation
rates for each bird was plotted against that bird’s behavioral
responses on probe trials, there was a suggestive correlation
between these two measures [r(4) ⫽ 0.90, P ⫽ 0.10, Fig. 8A].
Unexpectedly, birds with slower adaptation to GO stimuli than
to NoGO stimuli (more familiarity) also responded to probe
trials more frequently (as GO stimuli). Conversely, subjects
that adapted to NoGO stimuli more slowly than GO stimuli
responded to probe stimuli infrequently. Furthermore, when
the relationship between speed to acquire auditory discriminations and response to probe stimuli was similarly assessed,
there was a nonsignificant trend for the animals that learned
more quickly to respond to the probe stimuli as NoGO stimuli,
while slower learners responded to probes as GO stimuli
[r(4) ⫽ 0.87, P ⫽ 0.13, Fig. 8B].
Effects of Training on Single-Unit Auditory Responses in
NCM and CMM
The effects of training on single-unit firing rates and adaptation rates were assessed with repeated-measure nonparametric Friedman’s tests.
Firing rates and adaptation rates. Although previous studies
have shown that a majority of NCM sites show stimulusspecific adaptation (Chew et al. 1995), the single units isolated
in this experiment were heterogeneous populations of both
adapting and nonadapting neurons (Fig. 9). Single units recorded in NCM showed no effect of training on firing rates
[␹2(2, N ⫽ 43) ⫽ 0.59, P ⫽ 0.76, Fig. 10A], or on adaptation rates
[␹2(2, N ⫽ 43) ⫽ 1.81, P ⫽ 0.40, Fig. 10C]. However, in CMM,
although the responses to GO, NoGO and novel stimuli did not
differ in their firing rates (Fig. 10B), single units did adapt to
trained stimuli differently than they adapted to novel stimuli
[␹2(2, N ⫽ 43) ⫽ 3.40, P ⫽ 0.18; ␹2(2, N ⫽ 43) ⫽ 12.47, P ⬍ 0.01].
This effect was driven by faster single-unit adaptation to novel
stimuli (M ⫽ ⫺0.024 ⫾ 0.005) than to either GO stimuli (M ⫽
⫺0.013 ⫾ 0.004) or NoGO stimuli (M ⫽ ⫺0.015 ⫾ 0.020), as
shown by post hoc Wilcoxon signed-rank tests (P ⬍ 0.01 and
P ⬍ 0.05, respectively, Fig. 10D).
d= comparisons for single-unit responses. In a further analysis, d= values were calculated from single-unit spiking activity
(see METHODS) to test how responses to reinforcement-predictive stimuli differed from novel (unreinforced) stimuli. Kolmogorov-Smirnov tests were performed to compare the baseline
d= values (between pairs of novel songs; see METHODS) to d=
values that compared responses to trained songs to those
evoked by novel songs, in each structure. A KolmogorovSmirnov test was also performed for each structure comparing
baseline d= values to those attained by comparing responses to
GO stimuli to those evoked by NoGO stimuli.
In NCM, firing-rate responses to GO stimuli were more
discriminable from responses to novel stimuli than responses to
two novel stimuli (baseline) were from one another (D ⫽ 0.30,
P ⬍ 0.05, Fig. 11A). On the other hand, firing rate response to
NoGO stimuli was no more discriminable from responses to
novel than baseline (D ⫽ 0.14, P ⫽ 0.78, Fig. 11B). Firing-rate
responses to GO stimuli also trended toward being more
discriminable from responses to NoGO stimuli than responses
to two novel songs were from one another (D ⫽ 0.27, P ⫽
0.062, Fig. 11C), according to a Kolmogorov-Smirnov test.
In CMM, however, no differences were detected when the d=
values for learned songs were compared with baseline levels of
discriminability for song stimuli in that structure. Baseline d=
values were not significantly different than d= values calculated
to compare responses to GO or NoGO to those evoked by
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Fig. 7. Effects of passive familiarity on ARMs
and multiunit adaptation rates. The mean
ARMs and adaptation rates of multiunit recording sites in NCM and CMM for subjects
exposed to probe stimuli (n ⫽ 4) in response
to GO, NoGO, novel and probe auditory stimuli are shown. A: there was a significant main
effect of familiarity on ARMs in NCM (P ⬍
0.001, repeated-measures ANOVA): responses
were stronger for GO, NoGO and novel stimuli than passively familiar probe stimuli (P ⬍
0.05 for each Bonferroni post hoc). B: there
was also a significant main effect of familiarity on ARMs in CMM (P ⬍ 0.01, repeatedmeasures ANOVA): in this structure (like in
NCM), passively familiar probe stimuli
evoked lower ARMs than reinforcement-predictive (GO and NoGO) stimuli did (P ⬍ 0.05
in both cases, Bonferroni post hoc). In addition, multiunit adaptation rates showed a main
effect of familiarity in CMM (P ⬍ 0.001,
repeated-measures ANOVA). D: probe stimuli were adapted to significantly slower than
GO, NoGO and novel stimuli (P ⬍ 0.05 for all
Bonferroni post hocs). C: in NCM, however,
adaptation rates for probe stimuli were not
significantly different than those to the other
stimuli (P ⬎ 0.05, repeated-measures
ANOVA). Error bars denote within-subjects
standard error.
ADAPTATION RATE
1486
NEURAL RESPONSES AFTER AUDITORY DISCRIMINATION TRAINING
Percentage of Probe Responses
A
100%
B
80%
P427
60%
O481
B473
40%
O475
20%
0%
-0.4
-0.2
0
0.2
0.4
Familiarity to GO
Adaptation Difference
1487
and NoGO (with enhancement or reduction) more often than
would be predicted by chance [␹2(3, N ⫽ 43) ⫽ 16.26, P ⬍ 0.01,
Fig. 12B]. In addition, when correlations were run to test the
relationships between the d= responses to GO relative to the d=
responses to NoGO, the units in both NCM and CMM showed
significant positive correlations [r(NCM) (43) ⫽ 0.55, P ⬍
0.001; r(CMM) (43) ⫽ 0.49, P ⬍ 0.001; Fig. 12]. However,
when correlations were run to test the relationship between d=
values to the reinforcement-predictive and novel stimuli, there
was no relationship between responses to GO and novel stimuli
or NoGO and novel stimuli, in either brain area [NCM: r(GO)
(43) ⫽ ⫺0.088, P ⫽ 0. 57; r(NoGO) (43) ⫽ 0.059, P ⫽ 0.71;
CMM: r(GO) (43) ⫽ 0.045, P ⫽ 0.78; r(NoGO) (43) ⫽ ⫺0.017,
P ⫽ 0.92; Fig. 12].
20
Days to Criterion
P427
O481
16
14
O475
12
10
10%
B473
20%
30%
40%
50%
60%
70%
80%
90%
Percentage of Probe Responses
Fig. 8. Familiarity of trained stimuli correlates with performance on probe
trials. A: the difference between speed of multiunit adaptation to GO and
NoGO stimuli (difference in familiarity) trended toward correlation with
behavioral responses to probe trials. Slower adaptation to GO stimuli (indicating familiarity) appears as a positive value on the x-axis. Subjects that were
more familiar with (adapted to more slowly) GO stimuli responded more often
to unreinforced probe stimuli as GO stimuli, and vice versa for NoGO stimuli
[r(4) ⫽ 0.8957, P ⫽ 0.104]. B: in addition, there was a trend for the animals that
learned more quickly to respond to the probe stimuli as NoGO stimuli, while
slower learners responded to probes as GO stimuli [r(4) ⫽ 0.868, P ⫽ 0.13].
novel songs [D(GO) ⫽ 0.23, P ⫽ 0.17; D(NoGO) ⫽ 0.14, P ⫽
0.77, Fig. 11, D and E]. In addition, neural responses to GO
stimuli were no more different from responses to NoGO
stimuli than responses to two novel songs were from one
another (D ⫽ 0.14, P ⫽ 0.77, Fig. 11F).
Within-unit correlations. The lack of significant differences
detected in the single-unit dataset, as well as the high level of
variance in responses (as assessed by firing rates, adaptation
rates and d= values) suggested further analysis on the heterogeneity of the NCM and CMM single-unit populations recorded. It had already been established that the units isolated
differed in the levels to which their firing rates adapted to
auditory stimuli; therefore, tests were run to investigate the
degree to which units differed in their response to trained
songs, relative to novels. ␹2 analyses were used to determine
whether the individual single units showed enhanced or reduced responses (using d= values, relative to novel) to both GO
and NoGO stimuli more often than would be predicted by
chance.
␹2 were run on the counts of units that showed either
enhanced responses to both GO and NoGO, reduced responses
to both GO and NoGO, or opposite effects for GO and NoGO.
In NCM the ␹2 was not significant [␹2(3, N ⫽ 43) ⫽ 1.93, P ⫽
0.59]. However, CMM units tended to respond similarly to GO
Auditory processing areas NCM and CMM both showed
effects of training on neural responses to playback of conditioned vs. novel songs. The data thus support the general
hypothesis that operant training contributes to long-term neuronal memories for reinforcement-predictive stimuli observed
in sensory regions of the adult brain. However, the patterns of
differential neural activity for these stimuli differed between
the two brain regions, between the two learning groups, and
also between hemispheres.
Multiunit Responses to Operantly Trained and Novel Stimuli
In NCM, GO and NoGO stimuli elicited significantly different levels of responding from one another, but neither
category evoked higher or lower responses than novel stimuli.
In contrast, in CMM, training elicited higher absolute responding to all reinforcement-predictive stimuli (both GO and
NoGO) with respect to novel stimuli. The training experience
affected the neural responses to auditory stimuli in NCM and
CMM in different ways, supporting the hypothesis that the two
auditory areas serve different roles in auditory processing. As
NCM and CMM have reciprocal projections, it is likely that
they interact heavily with one another (Aiya et al. 2011).
However, our results showed that CMM responses were more
often influenced by the reinforcement-predictive values of
auditory stimuli, while NCM responses were driven by stimulus familiarity, which was different for GO and NoGO stimuli
in a way that reflected the behavioral strategies used by
individual subjects. Similar differences between NCM and
CMM were reported by Gentner and colleagues, based on
ZENK and neurophysiological studies of operantly trained
starlings (Gentner et al. 2004; Gentner and Margoliash 2003;
Thompson and Gentner 2010).
The adaptation rate results showed a complex pattern of
effects. Although there were effects of operant training on the
speed with which NCM and CMM multiunit sites decreased
responding over repeated stimulus presentations, the effects
were different than those seen for ARMs. In both areas, NoGO
stimuli were adapted to the most slowly: in NCM both GO and
novel stimuli showed faster adaptation than NoGO stimuli, and
in CMM adaptation was significantly faster for GO stimuli than
for NoGO stimuli. Contrary to what was observed for ARMs,
the significant differences in multiunit adaptation rate were
driven by slow adaptation to one of the stimuli. If these results
are interpreted in terms of stimulus-specific adaptation as a
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DISCUSSION
18
1488
NEURAL RESPONSES AFTER AUDITORY DISCRIMINATION TRAINING
NCM
Control
B
Response
Amplitude (mvolts)
Amplitude (mvolts)
A
0.1
- 0.1
0
250
C
500
750
1000
1250
1500
D
Response
0.1
-0.1
0
250
500
E
750
1000
1250
F
Novel
Novel
Novel
GO
GO
GO
GO
NoGO
NoGO
NoGO
NoGO
1750
ms
1
Trial
25
1
Trial
25
1
Time (ms)
Fig. 9. Response patterns of single units in NCM and CMM. Representative single-unit activity isolated from multiunit recordings in NCM (A) and CMM (B)
is shown. A and B: bottom traces show the waveform of a novel song stimulus, middle traces show the multiunit response to that stimulus and top traces show
the isolated single unit and its waveform. Single-unit firing rates were calculated by taking the neuron’s spike rate during the 500-ms control window and
subtracting it from the spike rate during the response window. Raster plots show the responses of two NCM (C and D) and two CMM (E and F) single units
to example novel, GO and NoGO stimuli. The unit shown in C adapts and is the same as that shown in A, above; and the unit shown in E adapts and is the same
as that shown in B, above. Units shown in D and F do not reliably adapt. The populations of single units isolated in both NCM and CMM contained both adapting
(C: novel ⫽ ⫺0.163, GO ⫽ ⫺0.502, NoGO ⫽ ⫺0.745; E: novel ⫽ ⫺0.880, GO ⫽ ⫺0.736, NoGO ⫽ ⫺0.844) and nonadapting sites (D: novel ⫽ ⫺0.448,
GO ⫽ 0.290, NoGO ⫽ ⫺0.387; F: novel ⫽ 0.703, GO ⫽ 0.778, NoGO ⫽ 0.791). Responses from trials 1–25 (y-axis) are plotted for each stimulus. The
sonogram of the stimulus is represented above each plot along the x-axis (time).
measure of familiarity, the songs that are adapted to most
slowly are those that are the most familiar to subjects. Therefore, it is possible that subjects were more familiar with trained
songs of negative valence (reinforced with punishment), perhaps due to the cognitive tactics used by subjects. However,
familiarity alone does not explain the entirety of either the
ARMs or adaptation rate effects, because passively familiar
probe stimuli showed responses that differed from learned
songs in both NCM and CMM.
Single-unit Responses to Trained and Novel Stimuli
Overall, single-unit responses to trained stimuli were neither
higher nor lower than responses to novel stimuli in NCM and
CMM. However, when d= values were used to compare responses recorded in NCM, single-unit responses to GO stimuli
were more discriminable from responses to novel stimuli than
baseline. In CMM, d= comparisons showed no differences. In
light of the multiunit data, these results were unexpected
because both GO and NoGO showed higher ARMs than
novel, in CMM. However, the scarcity of learning effects
seen in the single-unit population may be due to statistical
limits of our single-unit sample size, or to the heterogeneity
of unit responses. Because subpopulations of neurons may
show differential responses to learned songs, further analyses were conducted to test the correlations between responses to GO and NoGO stimuli for each single unit.
Results showed that, in both NCM and CMM, there were
significant correlations between a single neuron’s responses
to GO and NoGO. In addition, although a single unit showed
either enhanced or reduced responses to both learned songs
more often than would be predicted by chance, responses to
those stimuli were not correlated with that unit’s responses
to novel stimuli. This suggests that the neurons of NCM and
CMM show differential activity to novel stimuli, and these
responses are changed (by operant training) in a way that
causes reinforcement-predictive stimuli to evoke similar
responses, which can be used to signal important stimuli to
downstream neural structures.
Although responses in some units are enhanced while others
are reduced, within each unit both reinforcement-predictive
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Novel
1500
Trial
25
1750
ms
CMM
Control
NEURAL RESPONSES AFTER AUDITORY DISCRIMINATION TRAINING
20
15
10
GO
0
NoGO
Novel
D
GO
NoGO
Novel
-0.01
-0.02
-0.03
Fig. 10. Effect of training on single-unit firing rates and adaptation rates. A and
B: in both NCM and CMM, training had no effect on single-unit firing rates
[nonsignificant (ns), Friedman’s within-subjects test]. C: in NCM, there was no
effect of training on single-unit adaptation rates (ns, Friedman’s withinsubjects test). D: in CMM, however, there was an effect of training on
single-neuron adaptation rate (P ⬍ 0.001, Friedman’s within-subjects test).
Novel stimuli were adapted to faster than both GO stimuli and NoGO stimuli
(P ⬍ 0.01 and P ⬍ 0.05, respectively, Wilcoxon signed-rank test).
stimuli (GO and NoGO) are responded to similarly. However,
responses to different stimulus classes are variable and uncorrelated. These results suggest that the positive relationship
between responses to GO and NoGO stimuli develops over the
course of training, due to the learning process. Subpopulations
of neurons include those that show enhancement to behaviorally relevant stimuli, those that show suppression to behaviorally relevant stimuli and those that discriminate and respond to
the two behavioral valences in different ways. In fact, in NCM,
the neurons that showed enhancement to both GO and NoGO
tended to be those recorded in the left hemisphere, while the
neurons that showed suppression to both tended to be those that
were recorded in the right [␹2(3, N ⫽ 32) ⫽ 2.17, P ⫽ 0.14].
Therefore, the overall activity of the multiunit populations of
NCM and CMM neurons may better represent the role of those
structures in a circuit that processes relevant stimulus associations than the activity of any individual unit does. This finding
supports the need for further neurophysiological studies of
sensory processing, both at the multiunit and single-unit level.
Relationships Between Probe Trial Responses, Operant
Behavior and Learning Speed
There were neurophysiological differences between subjects
that mastered the task quickly and those that did not. In both
structures NCM and CMM, animals that learned faster exhibited higher ARMs and faster multiunit adaptation to all song
stimuli than animals that learned more slowly. This effect of
learning speed on auditory responses may reflect enhanced
neural plasticity in the faster learning individuals; these individuals not only showed stronger neural responses, but also
faster adaptation of those responses, possibly related to memory formation for novel stimuli. The neural differences between fast- and slow-learning subjects in ARMs and adaptation
A1
D1
NCM
0.5
0
-8
0.5
-4
0
GO/Novel
Baseline
4
8
B1
-4
0
4
8
-4
0
4
8
0
4
8
0.5
-4
0
NoGO/Novel
Baseline
4
8
0
-8
F1
C1
0.5
0
-8
0
-8
E1
0.5
0
-8
CMM
0.5
-4
0
d'
GO/NoGO
Baseline
4
8
0
-8
-4
d'
Fig. 11. The d= values of single-unit spiking responses to trained and novel
stimuli. Plotted here are the cumulative distribution frequencies of d= values in
NCM and CMM comparing single-unit spike rate responses to trained stimuli
(GO/NoGO) to those of novel songs (A, B, D, and E) and GO stimuli to those
of NoGO stimuli (C and F). The d= values calculated by comparing single-unit
spiking responses to two novel conspecific songs (novel-novel) are also plotted
with a dotted line for each area, as a baseline measure of discriminability of
arbitrary song stimuli in that structure. A: in NCM, d= values comparing
responses to GO and novel stimuli were significantly different than baseline
(D ⫽ 0.30, P ⬍ 0.05, KS test). D, E, and F: in CMM, however, there were no
significant differences in d= values of reward predictive and novel stimuli (ns,
KS test).
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ADAPTATION RATE
C
rates may, in fact, have contributed to how those groups
differed in their speeds to reach criterion. This is consistent
with results from a classical conditioning experiment showing
that ZENK induction patterns differ between fast and slow
learners (Jarvis et al. 1995). Late in learning, slow learners
show higher levels of ZENK induction than fast learners,
suggesting slower adaptation of this immediate early gene.
This may correspond to the slower rate of neural adaptation we
have observed in slow learners.
The learning speed effects can also be interpreted as reflecting the behavioral strategies they used to acquire discriminations. When learning speed was included as a factor in repeatedmeasures analyses of training effects, there were interactions
between training and learning speed in NCM adaptation data,
as well as CMM absolute response data. These interactions
suggest that individual subjects showed a stronger memory for
one song valence or the other, favoring the stimulus they
attended to more often to complete the auditory discrimination.
In CMM, although the group data suggested that both GO and
NoGO were responded to more vigorously then novel stimuli,
only fast learners showed a significant difference between
NoGO and novel stimuli, while slow learners showed different
responses to GO and novel stimuli. Fast learners showed
stronger memories for NoGO stimuli, while slow learners
showed stronger memories for GO stimuli.
Responses on probe trials presented during operant training
in a subset of birds provided a test of the behavioral strategies
Cumulative
Frequency
CMM
Cumulative
Frequency
B
NCM
25
Cumulative
Frequency
FIRING RATE
A
1489
1490
NEURAL RESPONSES AFTER AUDITORY DISCRIMINATION TRAINING
A
NCM Single Units
8
Enhancement
GO and Novel R2 = 0.008
6
NoGO and Novel R2 = 0.004
To
GO:
11
2
GO and NoGO R = 0.303
4
To
NoGO:
7
2
0
-8
-6
-4
-2
0
2
4
6
8
Reduction
-2
To
GO:
7
-4
To
NoGO:
12
11
-6
B
-8
CMM Single Units
8
GO and Novel R2 = 0.002
Enhancement
NoGO and Novel R2 = 0.000
6
2
GO and NoGO R = 0.237
To
GO:
5
4
20
To
NoGO:
4
2
0
-8
-6
-4
-2
0
2
4
6
8
Reduction
-2
-4
To
GO:
4
14
To
NoGO:
5
-6
-8
used by individual birds to perform auditory discriminations.
When the neural data and probe trial data were analyzed
together, there was a trend toward a correlation between the
relative rate of adaptation (to GO and NoGO) and the rate of
response to probe trials. The neural results suggest that novel
probe stimuli were placed into the category (GO or NoGO)
with which subjects were more familiar, based on adaptation
rates. For example, subjects that showed slower adaptation to
NoGO than to GO songs (indicating greater familiarity of the
NoGO songs) also showed fewer behavioral responses to the
probe trials during training (indicating they treated unfamiliar
probes as NoGO stimuli). The opposite relationship was seen
in birds that showed slower adaptation to GO songs.
An additional factor contributes to the interpretation. There
was a nonsignificant correlation between speed of behavioral
acquisition and probe responsiveness: the animals that learned
more slowly were more likely to respond to probes as if they
were GO stimuli. The data with probe stimuli are consistent
with the interpretation that faster learners attend to NoGO
stimuli to perform the auditory discrimination, while slower
learners use identification of GO stimuli as their primary
strategy. There are likely individual differences, between the
subjects, in motivation and the internal value of reward and
punishment. However, even without a complete explanation of
these results, our data suggest that learning, and the plastic
neural changes that occur during the learning process, reflect
subject variables specific to each individual.
Lateralization
Although the effects of training were similar in left and right
NCM for the majority of the analyses conducted, fast- and
slow-learning groups did show significant differences in the
degree to which their ARMs were lateralized in NCM. ARMs
were significantly higher in the left NCM than in the right for
fast learners, while slow learners exhibited a trend in the
opposite direction, a pattern which is typical of naive birds
(right higher than left, Phan and Vicario 2010). Ongoing
research in the laboratory has further suggested the rightlateralization of auditory responses, seen in zebra finches, can
be reversed by changes in the acoustic environment; the left
hemisphere of NCM is activated by novel auditory experience,
and neurogenesis in this area is involved in successful songlearning early in a songbird’s life (Tsoi et al. 2014; Yang and
Vicario 2015). Consequently, the experience of successful
learning may cause responses in the left hemisphere of NCM to
increase, while responses in the right hemisphere of NCM
decrease; alternatively subjects that are more left lateralized for
auditory responses may learn auditory discriminations more
easily. Although the reason for the interaction between NCM
lateralization and the speed of learning cannot be resolved with
these data, this result does suggest that the two hemispheres of
NCM serve different roles in auditory discrimination learning.
Therefore, further study of the relationship between auditory
learning, lateralization and neurogenesis in the songbird may
help to identify variables that produce successful processing of
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Fig. 12. A single-unit’s d= values for GO
songs are correlated to that unit’s d= values to
NoGO stimuli, but not to d= values for novel
songs. A and B: Venn diagrams show the
number of NCM and CMM units that show
enhanced or reduced responses to both GO
and NoGO (relative to novel), compared with
the number of units that respond differently
to GO and NoGO stimuli. Units that show a
decrease in responsiveness to positively reinforced GO stimuli commonly show an increase in responsiveness to negatively reinforced NoGO stimuli, while those units
which show reduced activity in response to
GO also show reduced responses to NoGO
(black data points) in both NCM (r ⫽ 0.5507,
P ⬍ 0.001; A) and CMM (r ⫽ 0.4873, P ⬍
0.001; B). Responses to novel stimuli are not
correlated to responses to GO (gray) or
NoGO (white) stimuli, in NCM or CMM. B:
although the population acts as a heterogeneous mix of decreases and increases in neural activity in response to the playback of
learned songs, a single unit shows the same
direction of effect for GO and NoGO stimuli
more often than would be predicted by
chance (␹2 ⫽ 16.26, N ⫽ 43, P ⬍ 0.01).
13
NEURAL RESPONSES AFTER AUDITORY DISCRIMINATION TRAINING
ACKNOWLEDGMENTS
Comments and assistance were provided by Efe Soyman, Marina Sharobeam, and the rest of the Vicario Laboratory.
GRANTS
This work was funded by National Institute on Deafness and Other
Communications Disorders Grants DC-008854 and DC-013174.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
Author contributions: B.A.B., M.L.P., and D.S.V. conception and design of
research; B.A.B. performed experiments; B.A.B. analyzed data; B.A.B.,
M.L.P., and D.S.V. interpreted results of experiments; B.A.B. prepared figures;
B.A.B. drafted manuscript; B.A.B., M.L.P., and D.S.V. edited and revised
manuscript; B.A.B., M.L.P., and D.S.V. approved final version of manuscript.
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