1Neuroscience Program, School of Human Development, University of Texas at Dallas, Richardson, Texas 75083-0688; 2Coleman Laboratory, Departments of Otolaryngology and Physiology, Keck Center for Integrative Neuroscience, University of California at San Francisco, San Francisco 94143-0444; and 3Scientific Learning Corporation, Berkeley, California 94104-1075
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ABSTRACT |
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Kilgard, Michael P.,
Pritesh K. Pandya,
Jessica Vazquez,
Anil Gehi,
Christoph E. Schreiner, and
Michael M. Merzenich.
Sensory Input Directs Spatial and Temporal Plasticity in Primary
Auditory Cortex.
J. Neurophysiol. 86: 326-338, 2001.
The cortical representation of the sensory
environment is continuously modified by experience. Changes in spatial
(receptive field) and temporal response properties of cortical neurons
underlie many forms of natural learning. The scale and direction of
these changes appear to be determined by specific features of the
behavioral tasks that evoke cortical plasticity. The neural mechanisms
responsible for this differential plasticity remain unclear partly
because important sensory and cognitive parameters differ among these tasks. In this report, we demonstrate that differential sensory experience directs differential plasticity using a single paradigm that
eliminates the task-specific variables that have confounded direct
comparison of previous studies. Electrical activation of the basal
forebrain (BF) was used to gate cortical plasticity mechanisms. The
auditory stimulus paired with BF stimulation was systematically varied
to determine how several basic features of the sensory input direct
plasticity in primary auditory cortex (A1) of adult rats. The
distributed cortical response was reconstructed from a dense sampling
of A1 neurons after 4 wk of BF-sound pairing. We have previously used
this method to show that when a tone is paired with BF activation, the
region of the cortical map responding to that tone frequency is
specifically expanded. In this report, we demonstrate that
receptive-field size is determined by features of the stimulus paired
with BF activation. Specifically, receptive fields were narrowed or
broadened as a systematic function of both carrier-frequency
variability and the temporal modulation rate of paired acoustic
stimuli. For example, the mean bandwidth of A1 neurons was increased
(+60%) after pairing BF stimulation with a rapid train of tones and
decreased (25%) after pairing unmodulated tones of different
frequencies. These effects are consistent with previous reports of
receptive-field plasticity evoked by natural learning. The maximum
cortical following rate and minimum response latency were also modified
as a function of stimulus modulation rate and carrier-frequency
variability. The cortical response to a rapid train of tones was nearly
doubled if BF stimulation was paired with rapid trains of random
carrier frequency, while no following rate plasticity was observed if a
single carrier frequency was used. Finally, we observed significant increases in response strength and total area of functionally defined
A1 following BF activation paired with certain classes of stimuli and
not others. These results indicate that the degree and direction of
cortical plasticity of temporal and receptive-field selectivity are
specified by the structure and schedule of inputs that co-occur with
basal forebrain activation and suggest that the rules of cortical
plasticity do not operate on each elemental stimulus feature
independently of others.
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INTRODUCTION |
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Experiments conducted over the last
20 years have documented that cortical representations are continually
shaped by experience (Buonomano and Merzenich 1998;
Edeline 1999
; Gilbert 1998
; Katz and Shatz 1996
; Merzenich et al. 1996
;
Singer 1995
). Numerous studies have suggested that
experience-dependent plasticity provides the neural basis for the
substantial improvement in performance that typically develops with
extended practice on simple discrimination tasks. In animal models of
learning, both spatially and temporally based tasks lead to progressive
improvements in behavioral performance; however, the form of neural
plasticity that underlies these improvements can be quite distinct. For
example, receptive fields in the somatosensory cortex of New World
monkeys are substantially increased by training on temporal judgements,
while fine tactile manipulations decrease receptive-field sizes
(Jenkins et al. 1990
; Recanzone et al.
1992c
; Wang et al. 1995
). A more complete
description of the rules that transform sensory experience into useful
changes in the distributed cortical representation is needed
1) to relate the cellular rules of synaptic plasticity to
observed experience-dependent plasticity in large populations of
neurons, and 2) to clarify how these rules contribute to
both the flexibility and reliability of the integrated operation of
cell assemblies operating across the cortex.
Although it is clear that the degree and direction of cortical
plasticity depends on the behavioral paradigm used to produce it, it is
not yet clear what specific aspects of these different experiences are
responsible for the distinct forms of observed cortical
reorganizations. Studies of experience-dependent plasticity often
differ in a number of parameters likely to be important for determining
the form of plasticity, including modality, behavioral response, task
difficulty, task goal, motivation, duration of training, background
stimuli, and species (Bakin et al. 1992, 1996
;
Buonomano and Merzenich 1998
; Byl et al.
1996
; Diamond and Weinberger 1989
; Dimyan
and Weinberger 1999
; Edeline et al. 1993
; Glazewski 1998
; Recanzone et al.
1992b
-d
; Sakai et al. 1999
; Spengler et
al. 1997
; Wang et al. 1995
; Xerri et al.
1996
; Zohary et al. 1994
). Although several
studies have explored how parameters such as attention and task
difficulty affect plasticity (Ahissar and Hochstein
1997
; Ahissar et al. 1992
; Edeline and
Weinberger 1993
; Recanzone et al. 1992c
, 1993
),
relatively little is known about how specific features of behaviorally
important stimuli direct cortical reorganization. In this study, we
employ a powerful technique that mimics learning-induced plasticity to
document how simple alterations of the sensory input produce
substantially different forms of cortical plasticity (Juliano
1998
; Kilgard and Merzenich 1998a
,b
).
Activity of cholinergic neurons in the basal forebrain (BF) provides a
gate on plasticity mechanisms that allows the cortex to operate
specifically on behaviorally arousing stimuli (Hasselmo 1995; Singer 1986
; Weinberger
1993
; Woody 1982
). Nucleus Basalis (NB) neurons
provide the major source of cholinergic input to the neocortical mantle
(Mesulam et al. 1983
) (Fig.
1A) and contribute a
significant GABAergic input as well (Gritti et al.
1997
). These neurons project ipsilaterally to all of the
neocortex, as well as to the amygdala and the reticular nucleus of the
thalamus (Levey et al. 1987
; Mesulam et al.
1983
), and receive inputs from the amygdala, ventral tegmentum,
frontal cortex, hypothalamus, and from a number of brain stem nuclei
(Haring and Wang 1986
). NB neurons respond to both
aversive and rewarding stimuli of different modalities and can be
conditioned to respond to innocuous stimuli that become associated with
reward (Pirch 1993
; Richardson and DeLong
1991
; Whalen et al. 1994
).
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Lesion studies support the hypothesis that NB activity serves as a
reinforcement signal to guide cortical plasticity. Even the robust
cortical reorganization that follows digit amputation, nerve section,
or monocular deprivation can be blocked by NB lesions (Bear and
Singer 1986; Juliano et al. 1991
; Webster
et al. 1991a
). Highly selective lesions of only the cholinergic
neurons in the NB prevent the plasticity that results from whisker
trimming or follicle removal (Baskerville et al. 1997
;
Sachdev et al. 1998
; Zhu and Waite 1998
),
providing strong evidence that NB is necessary for cortical map reorganizations.
The role of NB activity in gating cortical plasticity was further
supported by experiments in auditory and somatosensory cortex of rats,
guinea pigs, cats, and raccoons, demonstrating that pairing electrical
activation of NB with sensory stimuli is sufficient to shift cortical
receptive fields (Bakin and Weinberger 1996; Bjordahl et al. 1998
; Edeline et al.
1994a
,b
; Hars et al. 1993
; Howard and
Simons 1994
; Kilgard and Merzenich 1998a
;
Tremblay et al. 1990
; Webster et al.
1991b
), and temporal response properties (Kilgard and
Merzenich 1998b
; Shulz et al. 2000
). Many of
these experiments showed that NB-induced plasticity is blocked by
atropine, a cholinergic antagonist. Introducing a 1-s separation
between the sensory input and NB activation also blocked NB-induced
plasticity (Metherate and Ashe 1991
, 1993
).
Collectively, these results indicate that NB activity serves as a
powerful modulator of cortical plasticity mechanisms.
Although cortical plasticity results from a medley of manipulations of sensory experience, it has generally been difficult to directly compare the results from such studies. In this study, the modality, species, behavioral state, and number of repetitions are the same across experimental groups that vary only in acoustic experience. The sound stimulus paired with BF activation was varied along a number of stimulus continua in different animals to explore how the structure and schedule of auditory input guides plasticity of spectral and temporal response properties. We also explored the effect of introducing a delay between tone onset and BF activation. Our results are consistent with previous studies of experience-dependent cortical plasticity in primates and document in greater detail how spectral and temporal features of the sensory input specify the direction and magnitude of receptive field and temporal response plasticity in primary auditory cortex (A1).
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METHODS |
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Implantation and stimulation
BF-stimulating electrodes were implanted in 38 pentobarbital anesthetized (50 mg/kg) rats (~300 g). Platinum bipolar-stimulating electrodes (SNE-200, Rhodes Medical Instruments, Woodland Hills, CA) were lowered 7.0 mm below the cortical surface 3.3 mm lateral and 2.3 mm posterior to bregma and cemented into place using sterile techniques approved under University of California at San Francisco and University of Texas at Dallas animal care protocols. Rats received prophylactic treatment with ceftizox antibiotic (20 mg/kg), dexamethazone (4 mg/kg), and atropine (1 mg/kg). Three bone screws were used to anchor the electrode assembly. Leads were attached to screws over the cerebellum and cortex so that the global electroencephalograph (EEG) could be monitored during BF activation in unanesthetized animals.
After 2 wk of recovery, tonal stimuli were paired with BF electrical
stimulation in a sound-shielded test chamber (5 days/wk) for 1 mo
(Table 1). Animals were placed in a 25 × 25-cm wire cage in the middle of 60 × 70-cm box lined with 3-in
acoustic foam. The cage was positioned 20 cm below the audio speaker. A small 4-pin connector attached to a swivel was used to record the EEG
and to deliver short current pulses to the stimulating electrode. Each
animal received 300-500 pairings of tones and BF stimulation per day.
Interstimulus intervals varied randomly from 10 to 30 s. Ten rats
received BF stimulation paired with a 70 dB SPL tone with a fixed
frequency (4, 9, or 19 kHz). In five rats, two different randomly
interleaved tone frequencies were paired with BF stimulation (4 and 14 or 9 and 19 kHz). In five rats, nine different randomly interleaved
tone frequencies were paired with BF stimulation (1.3, 2, 3, 4, 5, 7, 9, 11.2, and 14 kHz). In this group, tones were presented at 30-40 dB
above rat hearing threshold (Kelly and Masterton 1977)
to activate similarly sized neural populations. In four rats, a train
of six short 9-kHz tones presented at 15 pulses per second (pps) were
paired with BF stimulation. In 10 rats, trains of short tones applied
at a constant tone frequency which varied randomly from trial to trial (1.3, 2, 3, 5, 9, 14, or 19 kHz at 20-30 dB above threshold). The
repetition rate of the tones was fixed for each animal (5, 7.5, and 15 pps, n = 4, 2, and 4 rats, respectively). All tones had
3-ms onset and offset ramps. The tones paired with BF stimulation were
250 ms in duration except for the tone trains that were composed of
25-ms tones.
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To establish the specificity of BF pairing, several animals were also stimulated with tones that were not paired with BF stimulation. Half of the animals in the single-frequency group were also presented, on the same schedule, with two other tone frequencies not paired with BF stimulation (see Table 1). There were no unpaired stimuli delivered to the 9 kHz/15-pps rats. The multiple-frequency train groups heard one of each of the multiple-frequencies tone pips presented in isolation without BF stimulation as often as they heard each train that was paired with BF stimulation.
When a single unmodulated tone was used as the auditory stimulus (one frequency group), electrical stimulation began 50 ms after tone onset in half the experiments and 200 ms before tone onset in the other half (Fig. 1B). Although some forms of learning are very sensitive to the order of sensory and modulatory inputs, these two relative timings did not generate noticeably different plasticity effects, and the two groups are analyzed together in this study. In the multiple-carrier frequency groups, electrical stimulation began 50 ms after tone onset. When tone trains were used, stimulation occurred simultaneously with the onset of the fourth tone in trains. In four animals, 19-kHz tones were presented 10 s after each BF stimulation. BF stimulation consisted of 20 capacitatively coupled biphasic pulses (0.1-ms pulse width, 100 pulses/s).
The efficacy of BF activation was continuously monitored in every animal by quantifying BF-induced EEG desynchronization during slow-wave sleep. The current level (70-150 µA) for BF stimulation was chosen for each animal to be the minimum necessary to desynchronize the EEG for 1-2 s during slow-wave sleep. After observing many naturally occurring sleep-wake cycles in each animal using video monitoring, we determined a level of EEG power (from 1 to 5 Hz) that distinguished the two states (i.e., always below while awake and usually above while sleeping). We used custom software to compare low-frequency EEG power during a 2-s period immediately before and after every stimulation event and set the current level (70-150 µA) for BF stimulation to be the minimum level that desynchronized the EEG below the EEG threshold. Typically, rats were sleeping during 10-20% of stimulation events, and BF activation resulted in desynchronization for 75-95% of these events. Only trials with low-frequency EEG power above the threshold before stimulation were analyzed to determine effectiveness of BF activation. EEG desynchronization usually lasted 1-4 s. Tonal and electrical stimuli did not evoke any observable behavioral responses (i.e., did not cause rats to stop grooming, or awaken, if sleeping).
Electrophysiological recordings and analysis
This study is based on neuronal spike data collected from 2,616 microelectrode penetrations into the right primary auditory cortex in 52 adult female Sprague-Dawley rats. Surgical anesthesia was induced with pentobarbital sodium (50 mg/kg). Throughout the surgical procedures and during the recording session, a state of areflexia was maintained with supplemental doses of dilute pentobarbital (8 mg/ml ip). The trachea was cannulated to ensure adequate ventilation and to minimize breathing-related noises. The skull was supported in a head holder that left the ears unobstructed. The cisternae magnum was drained of CSF to minimize cerebral edema. After reflecting the temporalis muscle, auditory cortex was exposed and the dura was resected. The cortex was maintained under a thin layer of viscous silicon oil to prevent desiccation. The location of each penetration was reproduced on a ×40 digitized image of the cortical surface microvasculature.
The primary auditory cortex was defined on the basis of its
short-latency (8-20 ms) responses and its continuous tonotopy (preferred tone frequency increased from posterior to anterior, see
Fig. 2, A and B)
(Kilgard and Merzenich 1999). Responsive sites
that exhibited clearly discontinuous best frequencies and long-latency
responses, unusually high thresholds, or very broad tuning were
considered to be non-A1 sites. Penetration sites were chosen to avoid
damaging blood vessels while generating a detailed and evenly spaced
map. Voronoi tessellation (Matlab 5.2, MathWorks) was used to visualize
the topography of A1. Voronoi tessellation generates polygons from each
set of nonuniformly spaced recording sites such that every point within
each polygon was nearer to the sampled site for that polygon than to
any other site. The boundaries of the map were functionally determined
using nonresponsive and non-A1 sites.
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Recordings were made in a shielded, double-walled sound chamber (IAC).
Action potentials were recorded simultaneously from two Parylene-coated
tungsten microelectrodes (FHC, 250-µm separation, 2 M at 1 kHz)
that were lowered orthogonally into the cortex to a depth of ~550
µm (layers IV/V). The neural signal was filtered (0.3-8 kHz) and
amplified (10,000×). Action potential waveforms were recorded whenever
a set threshold was exceeded, allowing off-line spike sorting
using Autocut (Datawave) or Brainware (Tucker-Davis Technology) software. Although most responses in this study represented the spike activity of several neurons, single units were separated when
possible, confirming that single units exhibited tuning that was
qualitatively similar to multi-unit response samples. To minimize experimenter-induced sampling bias the experimenter was blind to the
frequency(ies) paired with BF stimulation.
In most experiments, acoustic stimuli were delivered to the left ear via a calibrated ear phone (STAX 54) positioned just inside the pinnae. In the experiments with nine carrier frequencies, stimuli were presented with a calibrated speaker positioned 10 cm from the left ear. Because this system resulted in somewhat lower low-frequency thresholds, all analyses of these animals was compared with recordings from six experimentally naïve rats using the same speaker and position. Frequencies and intensities were calibrated using a B&K sound-level meter and a Ubiquitous spectrum analyzer. Auditory frequency response tuning curves were determined by presenting 45 frequencies spanning 3-4.5 octaves centered on the approximate best frequency of the site, or 81 frequencies from 1 to 32 kHz. Each frequency was presented at 15 or 16 intensities ranging between 0 and 75dB (either 675 or 1,296 total stimuli). Tuning curve tones were randomly interleaved and separated by 500 ms. All tonal stimuli used during the acute phase of this study were 25-ms long, including 3-ms rise and fall times.
Rat A1 tuning curves were V-shaped and generally exhibited monotonic
intensity response functions (Kilgard and Merzenich
1999; Sally and Kelly 1988
). Tuning-curve
parameters were defined by an experienced blind observer using custom
software that displayed raw spike data without reference to the
frequencies and intensities that generated the responses. For each
tuning curve, best frequency, threshold, bandwidth (10, 20, 30, and 40 dB above threshold), and latency data were recorded (Fig.
1C). Characteristic frequency (CF) is the frequency that
evokes a consistent neural response at the lowest stimulus intensity.
The minimum latency was defined as the time from stimulus onset to the
earliest consistent response (defined by a blind observer) for any of
15 intensities of the three frequencies that were nearest the CF (45 stimuli). The end of response latency was defined as the time after
tone onset when the PSTH (peristimulus time histogram) created by
summing the responses to all of the tones within each site's tuning
curve returned to baseline. Statistical analysis was done using Matlab 5.2. The specific test used was an unpaired two-tailed
t-test, unless otherwise indicated. Error bars reflect
standard error of the mean.
To determine the repetition rate transfer function (RRTF) for each site, six tones (25 ms with 5-ms ramps, 70 dB SPL) were presented 12 times at each of 16 repetition rates (Fig. 2C). To minimize adaptation effects, repetition rates were randomly interleaved, and 2 s of silence separated each train. The 2-s interval between trains allowed the response strength to 0.5-pps trains to be approximated. The carrier frequency of the tones trains for defining the RRTF were set to be the frequency of the seven used in pairing that was closest to each site's best frequency. To facilitate comparison across recording sites, response amplitude was normalized using the number of spikes evoked at each site to a tone in isolation. The normalized RRTF was defined as the average number of spikes evoked for each of the last five tones in the train divided by the number of spikes evoked by the first tone in the train. Thus a normalized spike rate of one indicates that at the given repetition rate each of the tones in the train, on average, evoked the same number of spikes as the first tone. Values greater than one indicate facilitation, whereas values less than one indicate response adaptation. Only spikes occurring from 5 to 40 ms after each tone onset were used to calculate the RRTF. RRTF data could not be viewed on-line and were analyzed only after each experiment was completed. All analyses were automated and were therefore not subject to experimenter bias or error. The effect of BF pairing on mean RRTF across all conditions was determined with ANOVA. Pairwise comparisons were analyzed by Fisher's PLSD. Best repetition rate was defined for each site as the repetition rate that evoked the maximum number of spikes.
The accuracy of our stereotaxic implantation of chronic stimulating electrodes into BF was confirmed in eight animals using standard histological techniques. Due to the size of the stimulating electrode and diffuse nature of NB, implantation was highly reliable, as confirmed physiologically each day in every animal.
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RESULTS |
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This report describes the results of experiments conducted to investigate the mechanisms that allow different sensory experiences to generate distinct forms of cortical plasticity. In each experiment, a group of adult rats was exposed to a sound stimulus paired with electrical activation of the BF to gate cortical plasticity. Four weeks of such pairing several hundred times per day generated several distinct forms of cortical plasticity. We report here experience-dependent plasticity generated by 10 variations of tonal stimuli paired with identical BF activation in 38 adult rats (~2,000 A1 penetrations, Table 1). These results are compared with 14 experimentally naïve controls (663 A1 penetrations) and discussed in relation to several studies of experience-dependent plasticity resulting from extensive behavioral training. Because identical BF activation (strength, number of repetitions, time course, etc.) was used in each experimental group, we conclude that the differential plasticity documented in this study was a result of the differential auditory experience of each group.
To determine the effects of acoustic experience on receptive-field structure, response strength and latency, maximum following rate, and size of A1, we systematically varied four parameters of the acoustic stimuli paired with BF activation (Table 1). In the first experimental group, 10 rats heard a pure tone paired with BF activation. In the second group, 10 rats received BF stimulation paired with a tone whose frequency was randomly selected each trial to be one of either two or nine different frequencies (5 rats in each subgroup). This experiment was designed to test the effect of distributing the acoustic experience across the sensory epithelium (cochlea). In the third group, 14 rats received BF stimulation paired with trains of tones instead of a single tone to determine the parameters that shape temporal response plasticity. To study the effects of both repetition rate and carrier-frequency variability, this group was divided into four subgroups. All the rats in the first subgroup, heard trains of six 9-kHz tones presented at 15 pps paired with BF activation. In the other three subgroups, the carrier frequency of each tone train was varied from trial to trial (1 of 7 possible carrier frequencies). Rats in these three subgroups heard trains with a repetition rate of 5, 7.5, or 15 pps to demonstrate that the observed plasticity was specific to the paired repetition rate. In the fourth group of rats, a 10-s interval was inserted between the BF stimulation and the onset of a single 19-kHz tone to determine the importance of the relative timing of BF activation and acoustic input. After 4 wk of pairing, we employed well-established cortical mapping techniques to quantify changes in the spectral and temporal response properties of cortical neurons and reconstruct their distributed response.
Receptive field expansion and contraction
We have previously demonstrated that BF stimulation can
substantially alter the degree of frequency selectivity in A1 neurons (Kilgard and Merzenich 1998a). In this study, we
document in detail the relationship between sensory experience and
receptive-field size. Specifically, we observed that both the degree
and direction of bandwidth plasticity are determined by a combination
of the two acoustic parameters varied in this study: number of
different tone frequencies and repetition rate (Fig.
3). For example, pairing BF stimulation
with a 15-pps train of 9-kHz tones increased the mean bandwidth of A1
neurons by 60% (Fig. 3, a), while interleaving two different tone
frequencies decreased the mean bandwidth by 25% (Fig. 3, i). These
results mimic the findings in monkeys that several weeks of practice
with a modulated stimulus increases receptive-field size, while
discriminating between different tone frequencies decreases
receptive-field size (Recanzone et al. 1992c
, 1993
).
Although it was argued that the differential plasticity effects
observed in those studies contributed to the practice-induced improvements in task performance, the mechanisms that direct these opposite forms of plasticity remain unclear. In our experiments, varying the carrier frequency of the 15-pps tone trains resulted in
significantly less receptive-field expansion (P < 0.0001) compared with 15-pps trains with a fixed carrier frequency
(Fig. 3, a and b). We also presented tone trains with random carrier
frequency and 5- or 7.5-pps repetition rate as part of our experiments
on temporal plasticity (Kilgard and Merzenich 1998b
).
The systematic decrease in receptive field expansion as repetition rate
is decreased (Fig. 3, b-d) suggests that cortical plasticity rules
shape receptive field size dependent on input repetition rate.
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We also observed that unpaired (background) stimuli could influence receptive-field plasticity. Although receptive-field sizes were increased by 20% when a single tone was paired with BF activation, frequency selectivity was not affected in a different group of rats that received identical BF pairing with one tone frequency but also heard two additional frequencies that were not paired with BF stimulation (Fig. 3, e-g). This result supports the preceding interpretation that frequency variability tends to minimize receptive field expansion and also reveals an important role of background sounds in shaping cortical plasticity.
Strength of evoked response
The strength of the evoked response of A1 neurons could also be increased by BF-induced plasticity mechanisms. Of the 10 different sets of acoustic stimulation paired with BF activation, only 1 significantly altered the mean number of spikes evoked per tone. After pairing the 15-pps trains of 9-kHz tones, on average 3.7 ± 0.2 spikes per tone were recorded from each A1 penetration compared with 2.8 ± 0.1 spikes in experimentally naïve rats. This increased excitability was likely caused by the dramatic overlap of receptive fields due to the combination of map reorganization and receptive field broadening. Pairing multiple carrier frequencies with a slow repetition rate (in isolation, 5 or 7 pps) decreased the spontaneous firing rate (by ~30%; P < 0.05) compared with controls.
Previous studies using cholinergic modulation observed highly specific
changes in the number of spikes evoked by different tones within a
neuron's receptive field. In some cases, the neural response to
frequencies within one-fourth of an octave were facilitated, while the
responses to other nearby frequencies were inhibited (Bakin and
Weinberger 1996; McKenna et al. 1989
;
Metherate and Weinberger 1989
, 1990
). Most of the
analysis in this study is focused on the receptive field as a unit and
would not pick up changes in the response strength to frequencies
within the tuning curve. To determine if such precise effects resulted
from our long-term pairing of BF activation with tonal stimuli, the
number of spikes evoked as a function of frequency was also examined for every tuning curve. We observed no consistent peak at the paired
frequency in individual sites or in the population as a whole (data not
shown). Minimum stimulus thresholds also showed no consistent change as
a result of pairing BF stimulation with any of the auditory stimuli
used in this study.
Temporal response plasticity
Aspects of the sensory input had a significant effect on the
latency of A1 responses to tones (Fig.
4). Pairing a slow train of tones with
multiple carrier frequencies increased the average minimum response
latency by ~1 ms, while pairing a single tone with BF stimulation (1 at a time or in 15-pps trains) decreased onset latencies by ~1 ms.
Both of these sets of acoustic stimuli delayed the end of the cortical
response (Fig. 4B). The widening of the evoked response
after single tone pairing likely reflects the consequences of the
expanded cortical map in these animals. In contrast, the increase in
latency when the maximum repetition rate is decreased (Kilgard
and Merzenich 1998b) supports earlier observations that minimum
latency is correlated with maximum following rate (Schreiner et
al. 1997
). Pairing nine different tones with BF stimulation
generated a distinct form of temporal plasticity. The population
discharge (response synchrony) was significantly sharpened by an
increased onset latency combined with a decreased duration of the
cortical response. This result suggests that the statistics of the
sensory input determine whether spectral or temporal strategies are
used to sharpen the cortical representation of stimuli paired with BF
activation. This interpretation is strengthened by our observation that
pairing two different tones (intermediate between one and nine tone
pairing) caused no change in response latency. Thus our results
indicate that the temporal response properties of cortical neurons can
be substantially and systematically altered by spatial and temporal
features of the sensory environment.
|
We have previously reported that the maximum repetition rate that A1
neurons can respond to can be increased or decreased depending on the
rate of acoustic stimuli paired with BF activation (Kilgard and
Merzenich 1998b). Additional experiments indicate that
repetition rate is not the only stimulus feature that guides the
expression of temporal selectivity. Spectral variability also influenced whether maximum following rate was altered. We paired 15-pps
tone trains with BF activation in two groups of animals and quantified
the resulting plasticity in following rate by deriving RRTFs at every
site. The maximum following rate of cortical neurons was not altered by
pairing BF stimulation with 15-pps, 9-kHz trains (Fig.
5). The profound map reorganization that
resulted indicates that the mechanisms of cortical plasticity were
successfully engaged (Fig.
6A). In a different set of
rats, random carrier frequency 15-pps tone trains were paired with BF
activation to test whether temporal plasticity had been prevented by
the extent of map reorganization or whether the 9-kHz carrier frequency
had simply been a more salient feature than the repetition rate of 15 pps. Varying the carrier frequency caused the mechanisms of cortical
plasticity to significantly increase the cortical following rate (Fig.
5). Although this variation prevented the map reorganization that occurs when a fixed carrier is used (Fig. 6B), it is not
clear whether preventing map expansion or increasing the relative
saliency of the temporal modulation is a more accurate explanation.
Either way this result demonstrates that spectral and temporal
characteristics of sounds interact to control spectrotemporal
selectivity in cortical neurons. Thus it may be difficult to predict
cortical plasticity in response to complex stimuli (i.e.,
vocalizations) from studies of synaptic mechanisms or cortical
plasticity evoked by elemental stimuli.
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|
Expansion of functionally defined A1
In addition to increasing the percent of A1 that responded
to the paired tone frequency (Kilgard and Merzenich
1998a), pairing BF activation with a single frequency increased
the total area of A1 by 50% (Fig. 7).
Although this increase in A1 area was based on the well-established
functional definition that the primary auditory field has phasic,
short-latency responses to tones and a continuous tonotopy
(Kilgard and Merzenich 1999
; Sally and Kelly 1988
), we do not know the effect on anatomical definitions of primary auditory cortex (Roger and Arnault 1989
;
Romanski and LeDoux 1993
). The observation that the size
of A1 was not significantly increased by any of the other stimulus sets
paired with BF activation indicates that this form of cortical
plasticity is specific to certain forms of acoustic experience (Fig.
7). When the CF shift and overall expansion of A1 are considered
together, pairing a single tone with BF stimulation was able to
increase the number of A1 neurons responding to the paired tone by
threefold.
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Frequency map plasticity
Although map reorganizations occur in some forms of natural
learning (Xerri et al. 1994, 1996
), such reorganizations
typically result from sensory input that is restricted to one region of the topographic map. Learning still occurs in many situations where the
distribution of stimuli along the receptor surface precludes map
expansion as a possible mechanism. As expected, we observed no
significant map plasticity in any of the five rats that heard nine
different randomly interleaved unmodulated tones paired with BF
activation (data not shown). The increased frequency selectivity and
improved temporal synchronization of the cortical response described in
the preceding text supports the hypothesis that both receptive field
and temporal plasticity contribute to behavioral improvements when it
is not possible to increase the number of engaged neurons via map reorganization.
To test the importance of the temporal relationship between BF
activation and sensory input, we delivered BF activation with a 10-s
interval before 19-kHz tone presentation. Earlier studies showed that a
1-s separation between sensory input and BF activation caused no
short-term BF-induced plasticity (Metherate and Ashe 1991,
1993
). In our chronic preparation, a 10-s separation resulted in a general decrease in frequency tuning (BW10 was 120 ± 4% of controls, P < 0.001) but did not result in a specific
map expansion at the paired frequency (data not shown).
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DISCUSSION |
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Experimental manipulations of sensory experience can result in a
variety of changes in cortical responsiveness (Byrne and Calford
1991; Hubel and Wiesel 1970
). As a class, such
effects are generally called experience-dependent plasticity. Merzenich and colleagues observed that different forms of cortical plasticity developed during extended operant training of owl monkeys on several different tasks (Jenkins et al. 1990
; Recanzone
et al. 1992a
,c
). Although expansion and sharpening of cortical
representations of behaviorally relevant stimuli was a common theme
among these studies, the mechanisms that allow the cortex to adapt its
processing of sensory information to improve behavioral performance
remain unclear. It has been hypothesized that much of the information the cortex uses to determine how to reorganize itself is contained in
the sensory input and may even be relatively independent of specific
task goals (Ahissar and Ahissar 1994
; Merzenich
et al. 1990
).
This study represents our initial efforts to systematically vary the sensory input to elucidate the "rules" that allow sensory experience to shape both spectral and temporal responses of cortical neurons in adult animals. We used electrical stimulation of BF to activate cortical plasticity mechanisms and varied only the paired sensory stimuli to explore the relationship between the statistics of the sensory input and the class, direction, and magnitude of cortical reorganization. We report that stimulus repetition rate and spectral variability systematically alter a number of cortical response parameters, including characteristic frequency, frequency bandwidth, size of A1, cortical excitability, stimulus following rate, and response latency (Table 2). The observation that systematic changes in sensory input result in systematic changes in cortical information processing supports the hypothesis that simple rules govern experience-dependent cortical plasticity.
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BF-induced spectral and temporal reorganizations
Our results clarify how specific aspects of the sensory input influences neural selectivity. The receptive-field plasticity recorded in this study provides strong evidence that rules exist in the cortex to translate sensory input into substantive changes in cortical information processing. Frequency bandwidth was particularly sensitive to the auditory stimulus paired with BF activation. Bandwidth was increased by 60% or decreased by 25% simply by pairing different tonal stimuli with identical BF stimulation. Across the range of stimulus classes used in this study, bandwidth increased systematically with increasing repetition rate and decreased with increasing spectral variability (Fig. 8A).
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Merzenich and colleagues observed a similar relationship following
operant training of monkeys. Cortical receptive-field size was
decreased by practicing tasks with stimuli delivered to different locations on the receptor surface (cochlea or skin) and were increased by training on a task requiring detection of changes in the modulation rate of a stimulus delivered to an invariant skin location
(Jenkins et al. 1990; Recanzone et al.
1992a
,c
). The authors suggest that the observed
training-induced changes in receptive-field size were consistent with
the operation of Hebb-like synapses driven to change by temporally
coherent inputs in a competitive cortical network. Specifically, they
postulated that larger receptive fields are generated by the temporally
synchronous activity in response to low-frequency (10-20 Hz)
stimulation at an invariant location of the receptor surface, while
decreased receptive fields resulted from asynchronous cortical activity
in response to stimuli that move across or are applied at inconsistent
receptor locations (skin or cochlea). By systematically varying both
spectral variability and repetition rate, our study strengthens the
argument that receptive-field size is determined by the structure of
temporal correlations evoked by input sources and supports the
hypothesis that simple rules operate in the cortex to generate useful
changes in circuitry based on the statistics of sensory stimuli marked
by BF activity.
Our results also support earlier findings that receptive-field
plasticity effects are not always limited to the region of the map most
strongly activated by the training stimulus. Expansion of somatosensory
receptive fields following vibrotactile training using one digit was
observed on neighboring digits as well the trained digit
(Recanzone et al. 1992c). Statistical analysis of receptive-field size in rats that heard one frequency paired with BF
stimulation revealed that expansion also occurred in neurons with CFs
up to two octaves away from the paired frequency (data not shown).
BF stimulation has been shown to increase the number of stimulus-evoked
spikes (Bakin and Weinberger 1996; Edeline et al. 1994a
,b
; Tremblay et al. 1990
; Webster et
al. 1991b
). Of the seven classes of stimuli paired with BF
stimulation in this study, only 15-pps trains of 9-kHz tones caused a
significant increase in evoked response strength (spikes/tone).
Recanzone and colleagues also observed an increase in evoked responses
after training monkeys on a task that involved the analogous tactile
stimulus (a 20-Hz vibration of a single-digit segment). Thus our
findings are consistent with previous demonstrations that response
strength plasticity is dependent on particular features of sensory inputs.
Electrical activation of the BF paired with tonal stimuli was
sufficient to generate significant reorganization of the A1 frequency
map (Kilgard and Merzenich 1998a). The map
reorganization combined with a generalized expansion of A1 to generate
a threefold increase in the number of cortical neurons that responded
to the paired frequency. The direction of tuning curve shift was
determined by the frequency of the tones paired with BF stimulation,
while the magnitude of map expansion was determined by the degree of spectral variability of the paired tones. Our results support earlier
reports that BF activity generates the most precise cortical plasticity
when nearly simultaneous with cortical input (Metherate and Ashe
1991
, 1993
). This study extends previous reports that the
absolute size of a cortical zone could be expanded following some types
of behavioral training, if attentional resources were appropriately
engaged (Jenkins et al. 1990
; Merzenich et al.
1990
). We conclude that BF activity is sufficient to mimic
these cortical map expansions.
The maximum following rates of A1 neurons were decreased or increased
by pairing BF stimulation with 5- or 15-pps tone trains, respectively
(Kilgard and Merzenich 1998b). This report extends those
findings by demonstrating that the degree of spectral variability can
substantially alter the expression of temporal plasticity in auditory
cortex. Pairing a train of 9-kHz tones presented at 15 pps with BF
stimulation did not increase the maximum cortical following rate (Fig.
5). This result may explain why the cortical recovery time was not
altered by many weeks of training monkeys on a temporally based tactile
discrimination task that used spatially restricted input
(Recanzone et al. 1992d
). In that study, Recanzone and
colleagues argued that decreased latency and increased synchronization contributed to improved behavioral performance.
Finally, our finding that response latency is systematically altered by
certain classes of acoustic stimuli provides another potential
explanation for an earlier plasticity result using monkeys (Fig.
8B). Recanzone and colleagues observed that training monkeys to distinguish a tone standard from a range of other tone frequencies caused minimum latency to increase (Recanzone et al.
1993). As in that study, latency was increased in our study
when a range of frequencies was presented (Fig. 8B).
However, this increase in minimum latency was accompanied by a decrease
in the duration of the cortical response that generated a more
synchronous evoked response (and a decrease in spontaneous activity).
If the duration of the cortical response in the earlier study (not
reported) was decreased, an apparent distinction between the two monkey
studies may point to a commonality that more synchronous activation of cortical neurons results from extensive discriminative training. The
development of receptive field and temporal refinement when stimuli are
distributed across the receptor surface suggests that these mechanisms
may support behavioral improvements when map expansion is not possible.
Potential mechanisms that underlie the cortical plasticity documented
in this study include changes in network, synaptic, or cell intrinsic
properties. Although it is likely that effects at all three levels
contributed to the observed changes in spatial and temporal response
properties, the simplest explanation of our results is that sensory
experience differentially affected the balance of inhibition and
excitation. Blocking GABAergic receptors in the auditory cortex results
in receptive field expansion, increased response strength, and
decreased latency (Chen and Jen 2000; Wang et al.
2000
). In our experiments, pairing a 15-pps train of 9-kHz tones with BF activation caused very similar effects (Fig. 8, A and B, top right). Opposite changes in all
three response characteristics occurred when unmodulated tones of
varying carrier frequency were paired (Fig. 8, A and
B, bottom left). Recent computational models with
spike-timing-dependent synaptic plasticity have shown that such
selective mechanisms can also profoundly affect response latency
(Song et al. 2000
). Although additional experiments are needed to clarify the mechanisms that underlie these changes, the
systematic relationship between sensory experience and cortical plasticity documented in this study suggests that a comprehensive description of the rules that transform experience into useful changes
in the distributed cortical response is possible.
Although several studies have reported that the cortical plasticity
induced by a single episode of BF stimulation decays rapidly, other
studies have observed longer-lasting effects (Bjordahl et al.
1998; Dykes et al. 1990
; Edeline et al.
1994a
; Hars et al. 1993
; Rasmusson and
Dykes 1988
; Tremblay et al. 1990
; Webster et al. 1991b
). Shulz and colleagues recently demonstrated that in some situations acetylcholine-dependent plasticity is expressed only
in the presence of acetylcholine (Shulz et al. 2000
).
Both the speed of acquisition and volatility of these effects supports their argument that state-dependent levels of neuromodulators control
the expression of some forms of cortical plasticity. All of the data
presented in this study was collected 24-48 h after the last
electrical activation of BF. Our observation that BF-induced plasticity
endures for 1 day and is expressed even under anesthesia suggests
that structural changes may contribute to the expression and
maintenance of the changes in neural selectivity documented in this
study. Thus the duration and size of the plasticity effects generated
by repeated BF activation suggest that short-lived BF-induced plasticity can become long-lasting with extended repetition over the
course of days to weeks.
Technical considerations
Although several other studies using BF stimulation have
demonstrated that activation of cholinergic receptors is necessary for
BF-induced plasticity (Bakin and Weinberger 1996;
Edeline et al. 1994b
; Hars et al. 1993
;
Kilgard and Merzenich 1998a
; Metherate and Ashe
1991
), the role of acetylcholine has not been established in
this study. Although we suspect acetylcholine is involved in the
plasticity documented in this report, it is also likely that other
neurotransmitters released by NB neurons (including GABA) are important
regulators as well (Dykes 1997
; Gritti et al.
1997
). The aim of this study was to clarify how different
sounds paired with BF activation lead to different forms of cortical plasticity.
Two types of comparisons were used to quantify the effect of acoustic experience paired with BF activation. In the first, cortical responses from experimentally naïve rats were compared with responses from rats that had received 4 wk of BF stimulation. This across-animal design was necessary due to the difficulty with generating a detailed reconstruction of the cortical map before and after pairing in individual animals. Rats were randomly selected to serve as control or experimental animals. Thus the responses of A1 neurons in naïve animals should be equivalent to responses in experimental animals prior to BF stimulation, and significant differences with this group reflect experimentally induced plasticity. The second class of comparisons were between groups of rats that had received identical BF stimulation and differed only in their acoustic experience. These comparisons establish the specificity of the plasticity effects documented in this study and rule out the possibility that the observed changes in cortical responses arose due to BF implantation or other nonspecific effects.
Although BF activation occurred only in unanesthetized animals, the
neural responses analyzed in this study were collected under
barbiturate anesthesia. Cortical responses are unlikely to be identical
in awake and anesthetized animals; however, basic response features of
barbiturate anesthetized cortex, such as frequency tuning and
repetition rate transfer functions, are at least qualitatively similar
to the awake state (deCharms et al. 1998; Hars et
al. 1993
; Recanzone et al. 2000
; Shamma
and Symmes 1985
). In this study, all of the comparisons of
neural responses were between rats anesthetized in a similar manner.
Thus anesthesia is unlikely to be responsible for the differential
plasticity documented here.
Basal forebrain and learning
Nucleus basalis neurons located in the basal forebrain
respond vigorously to behaviorally important stimuli, either aversive or rewarding (Richardson and DeLong 1991). Our data are
consistent with the hypothesis that an important function of this
activity is to mark individual events as behaviorally relevant so that cortical plasticity mechanisms can improve the representations of
important stimuli (Ahissar and Ahissar 1994
;
Richardson and DeLong 1991
; Singer 1986
;
Weinberger 1993
). Although essential for effective
learning, identifying behaviorally important stimuli represents only an
initial step toward generating an improved cortical representation that
might be behaviorally useful. Individual neurons must not only alter
their response properties based only on their synaptic inputs; they
must do so in a concerted manner that leads to an improved distributed response.
Although it seems obvious that the representation of a tone would be
improved by increasing the number of neurons tuned for the tone's
frequency, in fact, the ideal solution depends entirely on what
information is needed from the stimulus. If an animal is conditioned
that a tonal stimulus predicts footshock, there is no way to know which
features of the stimulus will predict shock in the future (frequency,
duration, rise time, bandwidth, intensity, modulation rate, etc.). The
fact that animals generalize indicates that they do not assume that all
of the features are required. Evolution may have shaped brain circuitry
to make default guesses that are appropriate based on the evolutionary
history of the species (i.e., phyletic memory) (see Fuster
1995). These guesses may take the form of rules that operate
within the brain to extract stimulus features that are most likely to
contain relevant information.
A substantial amount of literature now demonstrates that different behavioral tasks result in different forms of representational plasticity in the cortex. Although the relationship between stimulus representation and information processing is far from clear, the similarity between the results in this study and earlier reports of cortical plasticity evoked by extended behavioral training suggests that the sensory input itself can provide much of the information about how to improve sensory representations. In this initial study we have focused on two stimulus features, repetition rate and spectral variability, and observed that each effected cortical plasticity in a systematic manner (Fig. 8). These results indicate that the cortex uses these features to guide several forms of cortical plasticity, including reorganization of feature maps, plasticity of spectral and temporal selectivity, expansion of a primary sensory cortical field, and increased strength of evoked responses. Additional experiments are needed to determine how other stimulus parameters shape representational plasticity.
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ACKNOWLEDGMENTS |
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We thank D. Rathbun, A. Cheney, N. Engineer, and R. Moucha for technical assistance; D. Buonomano, H. Mahncke, D. Blake, S. Nagarajan, P. Bedenbaugh, E. Ahissar, and H. Read for helpful discussion; and J.-M. Edeline, A. Doupe, A. Bausbaum, W. Martin, K. Miller, E. Knudsen, and two anonymous reviewers for insightful comments on the manuscript.
This work was supported by National Institute of Neurological Disorders and Stroke Grant NS-10414, Office of Naval Research Grant N00014-96-102, Hearing Research, Inc., and a National Science Foundation Predoctoral Fellowship (M. P. Kilgard).
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FOOTNOTES |
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Address for reprint requests: M. P. Kilgard, Neuroscience Program, School of Human Development, GR 41, University of Texas at Dallas, Richardson, TX 75083-0688 (E-mail: kilgard{at}utdallas.edu).
Received 21 September 2000; accepted in final form 9 January 2001.
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REFERENCES |
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