Theta-contingent trial presentation accelerates learning rate and enhances hippocampal plasticity during trace eyeblink conditioning.

Abstract

Hippocampal theta activity has been established as a key predictor of acquisition rate in rabbit (Orcytolagus cuniculus) classical conditioning. The current study used an online brain--computer interface to administer conditioning trials only in the explicit presence or absence of spontaneous theta activity in the hippocampus-dependent task of trace conditioning. The findings indicate that animals given theta-contingent training learned significantly faster than those given nontheta-contingent training. In parallel with the behavioral results, the theta-triggered group, and not the nontheta-triggered group, exhibited profound increases in hippocampal conditioned unit responses early in training. The results not only suggest that theta-contingent training has a dramatic facilitory effect on trace conditioning but also implicate theta activity in enhancing the plasticity of hippocampal neurons.

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Theta-Contingent Trial Presentation Accelerates Learning Rate and
Enhances Hippocampal Plasticity During Trace Eyeblink Conditioning
Amy L. Griffin, Yukiko Asaka, Ryan D. Darling, and Stephen D. Berry
Miami University
Hippocampal theta activity has been established as a key predictor of acquisition rate in rabbit
(Orcytolagus cuniculus) classical conditioning. The current study used an online brain–computer
interface to administer conditioning trials only in the explicit presence or absence of spontaneous theta
activity in the hippocampus-dependent task of trace conditioning. The findings indicate that animals
given theta-contingent training learned significantly faster than those given nontheta-contingent training.
In parallel with the behavioral results, the theta-triggered group, and not the nontheta-triggered group,
exhibited profound increases in hippocampal conditioned unit responses early in training. The results not
only suggest that theta-contingent training has a dramatic facilitory effect on trace conditioning but also
implicate theta activity in enhancing the plasticity of hippocampal neurons.
There is little doubt that the hippocampus plays a key role in
learning and memory processes. However, the precise nature of
this involvement is the subject of much debate (for reviews, see
Eichenbaum, Otto, & Cohen, 1992; Redish, 2001). One avenue of
research has focused on the relationship between cognition and
one of the most striking and well-studied indices of hippocampal
activity, the theta rhythm. The theta rhythm is a 3–12-Hz
sinusoidal-like waveform found in the hippocampus and related
structures during a variety of cognitive behaviors in several species
(Adey, 1966; Asaka, Griffin, & Berry, 2002; Asaka, Seager, Grif-
fin, & Berry, 2000; Bennett, 1969; Berry, Seager, Asaka, &
Borgnis, 2000; Berry & Thompson, 1978; Givens & Olton, 1990;
Green & Arduini, 1954; Kaneko & Thompson, 1997; Lawson &
Bland, 1993; Markowska, Olton, & Givens, 1995; Mizumori,
Perez, Alvarado, Barnes, & McNaughton, 1990; Salvatierra &
Berry, 1989; Seager, Johnson, Chabot, Asaka, & Berry, 2002;
Tracy, Jarrard, & Davidson, 2001; Vanderwolf, 1971; Winson,
1978), including humans (Arnolds, Lopes da Silva, Aitink, Kamp,
& Boeijinga, 1980; Kahana, Seeling, & Madsen, 2001; Meador et
al., 1991; Raghavachari et al., 2001; Tesche & Karhu, 2000).
Although many of these investigations have demonstrated a strong
relationship between the presence of hippocampal theta activity
and learning rate, most have used lesion and pharmacological
techniques to permanently alter the activity of the hippocampus or
of its afferents in order to disrupt or enhance learning. Our labo-
ratory has recently developed a less invasive approach to demon-
strate the relationship between theta and learning: monitoring
naturally occurring theta activity and administering training trials
contingent on its presence or absence. This not only ensures
normal, physiological patterns of theta activity, but also permits
the typical, but potentially significant, alternations between theta
and nontheta that occur in undrugged intact animals. In a recent
study using a delay eyeblink conditioning paradigm, Seager et al.
(2002) found that rabbits that received training trials in the pres-
ence of theta learned twice as fast as those receiving trials in the
absence of theta. To control for differences in intertrial interval
(ITI) length and number of trials per session, each theta- and
nontheta-triggered animal was assigned a yoked control animal
that received the same ITIs and trials per day as its partner,
irrespective of the amount of theta activity present before each
trial. The results showed that theta-triggered animals learned mar-
ginally faster than their yoked control group, but that nontheta-
triggered animals learned significantly slower than theta-triggered
and both yoked control groups. This finding suggests that instead
of theta benefiting learning, nontheta is especially detrimental, at
least in tasks that do not require the hippocampus, such as delay
conditioning. These findings corroborated and extended work be-
gun in the 1970s that demonstrated a strong predictive relationship
between the amount of theta activity and subsequent learning rate
in delay nictitating membrane conditioning (Berry & Swain, 1989;
Berry & Thompson, 1978, 1979). However, questions remain
about the relative impact of theta versus nontheta on hippocampus-
dependent tasks and the relation between theta states and condi-
tioned unit responses in hippocampus.
The objective of the current study was to evaluate theta-related
modulation of both the early and asymptotic stages of acquisition
in trace eyeblink conditioning, a paradigm in which there is no
overlap between the conditioned stimulus (CS) and unconditioned
stimulus (US). We expected to observe behavioral differences
between the theta- and nontheta-triggered groups similar to those
Amy L. Griffin, Yukiko Asaka, Ryan D. Darling, and Stephen D. Berry,
Department of Psychology and Center for Neuroscience, Miami
University.
Amy L. Griffin is now at the Center for Memory and Brain, Boston
University. Yukiko Asaka is now at the Department of Psychiatry, Yale
University School of Medicine.
This project was supported by a Sigma Xi grant to Yukiko Asaka. We
would like to thank Elizabeth Shurell, Bradley Haverkos, Rachel Vesco,
Kevin Moran, and Cristina Hochwalt for their assistance with data collec-
tion and analysis. We would also like to thank Lynn Johnson for his
technical assistance and Matthew Seager for his helpful suggestions on an
earlier version of this article.
Correspondence concerning this article should be addressed to Stephen
D. Berry, Department of Psychology, Miami University, 216 Benton Hall,
Oxford, OH 45056. E-mail: berrysd@muohio.edu
Behavioral Neuroscience
2004, Vol. 118, No. 2, 403–411
Copyright 2004 by the American Psychological Association
0735-7044/04/$12.00DOI: 10.1037/0735-7044.118.2.403
403

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seen in the Seager et al. (2002) study, with the theta-triggered
group learning significantly faster than the nontheta-triggered
group. Whereas the cerebellum and related pathways are essential
to both delay and trace forms of eyeblink conditioning, hippocam-
pal influences seem to be parallel and modulatory, becoming
essential in more complex paradigms such as the trace paradigm.
Therefore, unlike the Seager et al. (2002) delay conditioning study,
we expected to see differences in the earliest stage of learning
(defined here as the trials up to the fifth conditioned response
[CR]), which is the stage thought to be most dependent on hip-
pocampal activation. In addition, the use of yoked control groups
in the current study allowed us to determine whether the presence
of theta before a training trial enhanced learning rate in this
hippocampus-dependent task, and/or whether nontheta is detri-
mental to learning as it is in delay conditioning. In addition to
observing behavioral differences between the theta- and nontheta-
triggered groups, we were interested in assessing whether theta
triggering accelerates learning rate and hippocampal unit firing
rate in this more difficult, hippocampus-dependent task (Moyer,
Deyo, & Disterhoft, 1990; Solomon, Van der Schaaf, Thompson,
& Weisz, 1986). To date, no studies have explored the relationship
between theta-contingent trial presentation and changes in hip-
pocampal unit responses. We expected to see robust hippocampal
unit responses developing earlier in the theta-triggered group than
in the nontheta-triggered group, especially during the trace period
of the trial (see McEchron & Disterhoft, 1999). Such a demon-
stration would provide a direct link between the presence of theta,
changes in the excitability of hippocampal neurons, and learning
rate. Characterization of these relationships would establish a
foundation and methodology for future investigations of a possible
connection between hippocampal oscillatory potentials and plas-
ticity throughout the essential and modulatory neural substrates of
eyeblink conditioning.
Method
Subjects
Subjects were 18 New Zealand White rabbits (Oryctolagus cuniculus)
supplied by Myrtle’s Rabbitry (Thompson Station, TN). All rabbits were
maintained on a 12-hr light–dark cycle, with training conducted during the
light phase. The rabbits were allowed free access to food and water in their
home cages. All procedures involving animals were approved by the
Miami University Institutional Animal Care and Use Committee.
Electrode Implantation
All rabbits were anesthetized with ketamine (50 mg/kg im) and xylazine
(10 mg/kg im) and implanted with bilateral hippocampal electrodes (size
00 insect pins insulated with Epoxylite [Epoxylite Corporation, Wester-
ville, OH], except 50–70 ?m at the tip). The electrodes were positioned
according to stereotaxic coordinates (Girgis & Shih-Chang, 1981; 4.5 mm
posterior to bregma, 5.5 mm lateral to the midline suture and approxi-
mately 3.0 mm ventral to dura) and by monitoring activity from the
electrode tip during implantation.
Training
After 5 days of postsurgical recovery and one 30–45-min session of
adaptation to the restraint apparatus and conditioning chamber, rabbits
began trace eyeblink conditioning. The paradigm, “trace 500” (L. T.
Thompson, Moyer, & Disterhoft, 1996) consisted of a 100-ms, 1kHz, 80
dB tone followed by a 500-ms trace interval and a 3-psi, 100-ms corneal
airpuff. Procedures for theta-contingent trial presentation have been de-
scribed previously (Seager et al., 2002). Briefly, neural activity from one of
the hippocampal electrodes was filtered (0.5–22.0 Hz) and then monitored
in real time by a software program (Labview, National Instruments Cor-
poration, Austin, TX) designed to compute a spectral ratio of the propor-
tion of theta (3.5–8.5 Hz) to nontheta (0.5–3.5 Hz and 8.5–22.0 Hz) for
640-ms scrolling time intervals, updated every 160 ms. For the theta-
triggered group (T?), trials were given only when the spectral ratio
exceeded 1.0 three times in a row (960 ms total pretrial duration). For the
nontheta-triggered (T?) group, trials were given only when the spectral
ratio fell below 0.3 three times in a row. These criteria for T? and T?
trials were developed during pilot experiments designed to maximize (or
minimize) the probability that theta would continue throughout the condi-
tioning trial. Yoked controls (Y? and Y?; matched to the T? and T?
rabbits by age and sex) were given the same ITI and trials per day as their
T?/T? counterparts irrespective of the amount of theta occurring before
each trial. Each session lasted for 90 min. Rabbits were trained until they
reached a behavioral criterion of eight CRs out of nine consecutive trials,
which is conventionally thought to be the point of asymptotic responding
(Gormezano, Prokasy, & Thompson, 1987). We also used a second learn-
ing criterion, the number of trials to the fifth CR, which is indicative of
initial acquisition of the CS–US contingency (Prokasy, 1972, 1987; R. F.
Thompson, Berry, Rinaldi, & Berger, 1979).
Hippocampal Unit Analysis
Multiple-unit activity from the electrodes was band-pass filtered (500–
5000 Hz, Krohn-Hite Model 3700 filter [Krohn-Hite Corporation, Brock-
ton, MA]) and passed through a window discriminator (DataWave Tech-
nologies, Broomfield, CO), which separated the largest spikes in the unit
activity from background activity (approximately 2.5:1 signal-to-noise
ratio). A computer sampled at the rate of 20 kHz, calculated the number of
spikes in each 10-ms bin for 100 bins and constructed daily average
peristimulus time histograms. The hippocampal neural activity in response
to conditioning stimuli was quantified by computing standard scores from
each rabbit’s daily histogram. Standard scores were computed by subtract-
ing the average bin height of the pre-CS period from the height of each of
the 100 bins and dividing by the standard deviation of the pre-CS period.
Each histogram was divided into four periods, with each period summa-
rizing neural responses during a portion of the training trial as follows:
Period 1, tone CS (100 ms, 10 bins); Period 2, stimulus-free trace period
(500 ms, 50 bins); Period 3, air US (100 ms, 10 bins); and Period 4, end of
trial (300 ms, 30 bins). A total score was calculated for each period for each
rabbit by adding together the individual standard score values for the
appropriate bins. To test differences between groups, a 2 ? 3 mixed design
analysis of variance was used for each period of the trial in which group
was a between-subjects variable and day was a within-subjects variable.
Histology
At the end of the experiment, rabbits were lightly anesthetized and a
small marking lesion was made by passing a 200 ?A, 10-s direct current
through each recording electrode (Grass Stimulator Model SD-9, Grass
Instruments, West Warwick, RI). Rabbits were then given an overdose of
sodium pentobarbital (Euthasol, 0.2205 mg/kg iv) and perfused intracar-
dially with saline (0.9%) and Formalin (10%) solutions. The brains were
removed, sectioned with a cryostat, embedded on gelatin-coated slides,
stained with Prussian blue to mark the locations of the electrode tips, and
counterstained with Safranin (Sigma, St. Louis, MO). Slides were exam-
ined with a compound microscope (Nikon, Japan) for verification of
electrode locations. Only rabbits with electrodes in CA1 (stratum oriens or
stratum pyramidale) were included in the study.
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Results
T? Trial Presentation
Examples of hippocampal slow-wave activity that triggered
trials received by rabbits in the theta-contingent (T?) and
nontheta-contingent (T?) groups are shown in Figure 1. It is clear
that the recording from the T? subject (see Figure 1A) is domi-
nated by sinusoidal theta activity at approximately 5 Hz, whereas
the recording from the T? subject is less rhythmic, with substan-
tial nontheta activity (see Figure 1B). Intermediate waveforms (see
Method section) would not have triggered trials in either the T? or
T? groups but were often seen during trials in the yoked controls.
Behavioral Results
As expected, rabbits that received training trials only while
exhibiting theta activity (T?) showed an accelerated learning rate
compared with those that received training trials in the explicit
absence of theta activity (T?). More specifically, we expected to
observe faster learning rates in the T? group for both the early
(fifth CR) learning criterion and the asymptotic (8/9) criterion.
Figure 2 shows the percentage of CRs for the first 4 days of
training for the T? and T? groups. It is clear that the T? group
gave a significantly greater percentage of CRs than the T? group
over the first four sessions of conditioning. This observation was
verified by a 2 ? 4 (Group ? Day) mixed design analysis of
variance, which showed a significant main effect of group, F(1,
7) ? 6.99, p ? .03. Figure 3 shows that, for the early stage (fifth
CR) criterion, the T? group (M ? 50.60, SD ? 21.18) learned
significantly faster than control rabbits yoked to the T? group
(M ? 181.40, SD ? 140.47), t(4) ? 2.33, p ? .04, and 4 times
faster than the T? group (M ? 212.00, SD ? 75.79), t(7) ?
?4.61, p ? .01. Similarly, the T? group learned significantly
more slowly than the T? yoked control group (M ? 141.25, SD ?
53.24), t(4) ? ?2.87, p ? .03. Figure 4 shows that for the
asymptotic (8/9) learning criterion, the T? group (M ? 180.20,
SD ? 128.70) learned significantly faster than yoked controls
(M ? 282.20, SD ? 187.04), t(4) ? 3.42, p ? .01, and the T?
group (M ? 399.25, SD ? 167.88), t(7) ? ?2.22, p ? .03. The
T? group did not differ significantly from their yoked controls
(M ? 363.25, SD ? 219.25), t(3) ? ?0.33, p ? .39. As expected,
because theta did not vary systematically between the two yoked
control groups, these groups did not differ significantly from each
other in learning rate for the fifth CR criterion, t(7) ? 0.54, p ?
.61, or the 8/9 criterion, t(7) ? ?0.60, p ? .57.
Hippocampal Unit Activity
In addition to behavioral differences between the T? and T?
groups, we expected to see rapidly developing and more dramatic
increases in hippocampal unit activity in the T? group compared
with the T? group. Most of the T? rabbits (4 out of 6) began
giving CRs on or before the 3rd day of conditioning. In contrast,
none of the T? rabbits had begun giving CRs at this point.
Therefore, we compared hippocampal firing rate in response to the
conditioning stimuli between the two groups over the first 3 days
of conditioning. As shown in Figures 5 and 6, the groups had
Figure 1.
trials in the theta-contingent (T?) and nontheta-contingent (T?) groups.
A: Notice that slow-wave activity during T? trials showed a predominance
of activity in the theta band. B: Conversely, hippocampal activity during
T? trials showed a mixture of frequencies higher and lower than the theta
band.
Examples of hippocampal slow-wave activity that triggered
Figure 2.
across the first 4 days of conditioning for the theta-contingent (T?) and
nontheta-contingent (T?) groups. The T? group gave a greater percentage
of CRs across the first 4 days of conditioning than the T? group (p ? .01).
Mean (? SEM) percentage of conditioned responses (CRs)
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similar unit responses on Day 1, but the T? group showed a
significantly greater increase in hippocampal unit firing rate across
days of conditioning than did the T? group, which developed
inhibitory responses. It is important that this increase occurred in
the trace period of the trial, the period during which the hippocam-
pus is thought to be most essential. Histograms of hippocampal
unit activity show that the T? and T? groups both exhibited
accelerations in unit firing rate during the US portion of the trial
(see Figure 5A and 5B). However, this conditioning-related activ-
ity began to move to the trace interval of the trial as early as the
2nd day of conditioning in the T? group, but was not evident in
the T? group even on the 3rd day of conditioning. In order to
quantify and perform statistical verification of this observation, we
computed standard scores of hippocampal unit activity in response
to the training stimuli and compared these values between the T?
and T? groups across the first 3 days of conditioning (see Figure
6). For the 100-ms tone period of the trial, there was a significant
main effect of group, F(1, 13) ? 15.61, p ? .01, showing that the
T? group had significantly higher unit standard scores than the
T? group in response to the tone CS. For the 500-ms trace period
of the trial, there was a significant Group ? Day interaction, F(2,
26) ? 5.77, p ? .01, with simple main effects tests revealing that
Figure 3.
conditioned stimulus contingency (fifth conditioned response [CR]) in the early stage of learning (Stage 1). A:
The T? group (n ? 5) reached the fifth CR behavioral criterion more than 4 times faster than the nontheta-
contingent (T?) group (n ? 4). ** p ? .01. B: The T? group took significantly fewer trials to reach the fifth
CR behavioral criterion than yoked controls. Conversely, the T? group took significantly more trials to reach
the fifth CR criterion than their yoked control group. *p ? .05, significantly different from zero.
Theta-contingent (T?) trial presentation reduces the number of trials required to establish the
Figure 4.
responding (eight of nine CRs). A: The T? group (n ? 5) reached the 8/9 behavioral criterion more than twice
as fast as the nontheta-contingent (T?) group, ** p ? .01. The T? group took significantly fewer trials to reach
the 8/9 behavioral criterion than yoked controls. B: However, the T? group reached criterion only slightly (and
not significantly) more slowly than their yoked controls, *p ? .05, significantly different from zero.
Theta-contingent (T?) trial presentation reduces the number of trials required to reach asymptotic
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the T? group had significantly higher unit standard scores than the
T? group on Days 2, t(13) ? 2.25, p ? .04, and 3, t(13) ? 3.95,
p ? .01, but not Day 1. The T? group showed significant inhib-
itory responses to the tone, t(6) ? 12.52, p ? .01, and the trace
period, t(6) ? 2.03, p ? .04, on Day 2. Similar results were seen
on Day 3, with significant inhibitory responses to the tone periods,
t(6) ? 10.21, p ? .01, and trace periods, t(6) ? 7.19, p ? .01.
There were no group differences (main effects or Group ? Day
interactions) for the air US portion of the trial or for the 300 ms
following US offset.
Discussion
Together, the pattern of results in the current study suggests that
the presence of theta activity during each conditioning trial en-
hances both the establishment of the CS–US contingency, as
revealed by the fifth CR criterion, and the length of time to reach
stable, asymptotic responding (8/9 CRs). Conversely, triggering
training trials when the hippocampal activity was explicitly low in
theta significantly delayed only the initial acquisition of the
CS–US contingency, although asymptotic responding developed
as quickly as in controls. In parallel with the enhancement of
acquisition rate, theta-contingent trial presentation also facilitated
the hippocampal conditioned unit response in the T? as compared
with the T? group, suggesting that the presence of theta activity in
the hippocampus may lead to increased plasticity of hippocampal
circuitry.
The demonstration that the two yoked control groups were
similar in learning rate verifies that the acquisition rate differences
Figure 5.
interval of the trial than the nontheta-contingent (T?) group. Standard scores of hippocampal unit firing rate
across the first days of conditioning in a representative rabbit from the T? group (left) and the T? group (right).
The arrows indicate tone (conditioned stimulus) and air (unconditioned stimulus) onset.
The theta-contingent (T?) group displayed a greater increase in hippocampal firing rate in the trace
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between the T? and T? groups were due to our theta manipula-
tion, and not due to variations in ITI length and number of trials
per day, which are known to affect learning rate (Gormezano,
1966). In fact, the yoked control group learning rates were inter-
mediate between those of the T? and T? groups, as would be
expected if these rabbits received some trials in theta and some
trials in nontheta. Post hoc analyses verified this mixture of theta
and nontheta trials in both control groups. These yoked control
results are essentially identical to those obtained for the yoked
control groups in the Seager et al. (2002) delay conditioning study.
In that study, there was also a highly significant difference be-
tween T? and T? treatments; however, the magnitude of the T?
difference (from controls) was greater than the T? difference. The
highly significant T? versus control difference in the current study
may reflect the more essential hippocampal role in processes
underlying trace versus delay conditioning. An intriguing possi-
bility is that theta is beneficial to many classical conditioning
tasks, whereas nontheta is especially detrimental to some (e.g.,
delay), a dissociation that may suggest ways to explore the rela-
tionship of theta to cognitive processes such as attention, aware-
ness, and incentive motivation that can modulate learning rates.
The major distinction between the behavioral results in the previ-
ous study using the delay paradigm (Seager et al., 2002) and the
current study is that there was not a significant difference between
the T? and T? groups in the number of trials to reach the fifth
CR. This measure is thought to reflect the early, contingency-
detection phase of learning (see Prokasy, 1972). The observation
that the theta-contingent trial presentation leads to a dramatic
initial learning enhancement in the trace, but not delay, paradigm
suggests that theta activity plays a different role in hippocampus-
dependent tasks than it does in tasks for which its influence is
primarily modulatory. Specifically, during trace conditioning, it is
more important for hippocampal theta to be present on early trials,
where theta may facilitate the formation of an initial association
between the conditioning stimuli. Conversely, in the delay para-
digm, the presence of hippocampal theta activity may be more
important in establishing stable responding.
The results of the present study serve as a further verification of
the relationship between hippocampal theta activity and learning
rate in a task that is known to depend on an intact hippocampal
formation. Although the existence of a correlation has been known
for many years, our recent development of a theta-contingent trial
presentation protocol has allowed us to directly demonstrate that
the presence of theta activity immediately preceding and continu-
ing into the training trials leads to faster acquisition of the eyeblink
response. This procedure has added support to the hypothesis,
derived from earlier correlational and invasive studies, that natu-
rally occurring theta activity plays a facilitory role in the estab-
lishment of a neural representation of the CS–US contingency.
Other findings support the notion that hippocampal theta may
coordinate cellular activity and plasticity in areas outside the
hippocampus proper (Dickson, Kirk, Oddie, & Bland, 1995; Frank,
Brown, & Wilson, 2001; Vertes, Albo, Viana Di Prisco, 2001),
suggesting more widespread hippocampal modulatory influences.
Although previous studies have demonstrated that hippocampal
neurons exhibit conditioning-related activity (see Berger, Rinaldi,
Weisz, & Thompson, 1983; Berger & Thompson, 1978; Oliver,
Swain, & Berry, 1993), this is the first investigation to compare
conditioned unit responses between theta- and nontheta-triggered
treatments. Our demonstration that the T? group showed a greater
acceleration of hippocampal firing rate early in conditioning
strengthens the idea that the presence of theta activity is associated
with greater plasticity of hippocampal neurons. However, the
group difference did not emerge until Day 2 of conditioning,
suggesting that theta enables a slowly developing plasticity in the
hippocampus that may allow the subject to learn faster. Moreover,
the observation that the difference between T? and T? groups in
firing rate occurred during the trace interval of the trial is consis-
tent with the assertion that the trace paradigm requires an intact
hippocampus, and suggests that the role of the hippocampus is to
bridge the time interval between the discontiguous events of the
trial (see Wallenstein et al., 1998). Our results are consistent with
Figure 6.
days of training for (A) the 100-ms tone period of the trial and (B) the
500-ms stimulus-free trace period. The theta-contingent (T?) group
showed more dramatic unit responses than the nontheta-contingent (T?)
group during both portions of the trial, especially on Days 2 and 3 of
training. In fact, the T? group showed significant inhibitory responses
during the tone and trace periods. Because we quantified the unit responses
by adding together the standard scores in each period, the difference in
scale between Panels A and B is indicative of the difference in period
duration.
Standard scores of hippocampal unit activity across the first 3
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a previous study (McEchron & Disterhoft, 1999) that examined
hippocampal single-unit activity during trace rabbit eyeblink con-
ditioning and found that learning-related acceleration of unit firing
rate first appeared in response to the US, and subsequently began
to occur earlier in the trial (in the trace interval). This pattern of
unit activity was then followed by behavioral learning, suggesting
that the hippocampal conditioned unit response occurs prior to the
appearance of stable conditioned responses, and more importantly,
that the increase in firing rate must occur in the trace interval of the
trial in order for behavioral learning to take place. Because
multiple-unit recordings were used to index hippocampal firing
rate, it is impossible to determine whether the group differences
reflect direct inhibition of hippocampal pyramidal cells or reduced
excitation. In fact, McEchron and Disterhoft (1999) reported that
single units show heterogeneous responses and that some neurons
actually show inhibitory responses after learning has reached as-
ymptotic levels. Future studies could address this issue by using
spike-sorting techniques to isolate single neurons and thus deter-
mine the firing properties of individual hippocampal neurons in
theta- and nontheta-triggered animals.
Although demonstrations of learning impairments due to the
disruption of pathways and structures that support the theta rhythm
are numerous, less common are studies that report facilitation of
learning due to increases in theta activity. One such study (Berry
& Swain, 1989) found that water restriction caused an increase in
theta activity and, consequently, in learning rate. It is important
that this enhanced learning rate was accompanied by increased
conditioned unit responses to training stimuli, which reflect rapidly
developing hippocampal plasticity in the eyeblink paradigm
(Berger et al., 1983). The water restriction study is a clear dem-
onstration that the motivational state of the animal (i.e., thirst) can
affect theta activity and thereby lead to changes in the plasticity of
hippocampal output neurons. It is by such a mechanism that the
hippocampus is thought to play a modulatory role in associative
learning through interaction with other brain systems, especially
the cerebellum (Clark, McCormick, Lavond, & Thompson, 1984).
Because theta was measured pretraining, the prior study could only
infer that the water-deprived animals had more theta during the
conditioning session itself. By using our theta-triggering tech-
nique, we were able to ensure that trials were received in the
presence or absence of theta, making a stronger case for theta’s
enhancement of conditioned unit activity and learning. There have
been several additional lines of research suggesting that theta leads
to an increase in hippocampal plasticity. Long-term potentiation is
maximally induced at theta frequency and during periods of theta
activity, suggesting a role for theta if, in fact, long-term potentia-
tion underlies hippocampal unit responses and behavioral learning
(Holscher, Anwyl, & Rowan, 1997; Larson, Wong, & Lynch,
1986; Pavlides, Greenstein, Gudman, & Winson, 1988; Thomas,
Watabe, Moody, Makhinson, & O’Dell, 1998). Relationships
among theta, learning, and conditioned hippocampal unit re-
sponses have also been shown in a fear-conditioning paradigm in
rats (Maren, DeCola, Swain, Fanselow, & Thompson, 1994).
In summary, a growing literature suggests that the presence of
high levels of theta may be optimal for learning as a result of its
proposed facilitation of hippocampal cellular plasticity, which
subsequently leads to modulation of the circuits necessary for the
acquisition and execution of the learned behavioral response. In
simpler paradigms that do not absolutely require an intact hip-
pocampal formation, such as delay eyeblink conditioning (Akase,
Alkon, & Disterhoft, 1989; Schmaltz & Theios, 1972; Solomon &
Moore, 1975), this modulation has a facilitory but nonessential
role (Berry & Seager, 2001). As in the current study, paradigms
such as trace eyeblink conditioning that require hippocampal cir-
cuitry demonstrate an even stronger theta–learning relationship. In
fact, using the presence or absence of theta activity as an indepen-
dent variable allows us to conclude that theta activity produces, or
is simultaneous with, optimal conditions for learning. Perhaps the
presence of theta is indicative of increased attention or awareness
of environmental stimuli, leading to better performance, especially
during difficult tasks such as trace conditioning. A recent study has
investigated the relationship between learning rate in human eye-
blink conditioning and the extent to which the participants were
aware of the stimulus contingencies and found that this reported
awareness enhanced learning rate (Manns, Clark, & Squire, 2000).
Our findings lend empirical support to recent theories and com-
putational models claiming a relationship between neurobiological
oscillatory potentials and learning-related plasticity (Buszaki,
2002; Singer, 1993; Hasselmo, Bodelon, & Wyble, 2002) and may
strengthen other models of hippocampal function (Gluck & Mey-
ers, 1997; Meyers et al., 1996; Schmajuk & DiCarlo, 1992).
An important next step is to investigate whether theta-
contingent trial presentation is able to attenuate learning impair-
ments. Recent laboratory results indicate that age-related learning
impairments can be ameliorated by triggering training trials during
theta activity, with aging animals in the T? group learning as
quickly as their younger counterparts (Asaka & Berry, 2004).
Another exciting possibility is the application of the theta-
contingent paradigm to human learning. There is growing evidence
that theta activity is present in human subjects during the perfor-
mance of cognitive tasks such as virtual maze navigation (Caplan,
Madsen, Raghavachari, & Kahana, 2001), working memory
(Raghavachari et al., 2001; Tesche & Karhu, 2000), and verbal
behavior (Arnolds et al., 1980; Meador et al., 1991). Whether such
learning can be optimized or deficits overcome by theta triggering
is a matter for future research. In addition, extending theta-
contingent training to human eyeblink classical conditioning
would facilitate comparisons to known hippocampal and cerebellar
substrates of human memory and their impairment by neurological
disorders such as Alzheimer’s disease (Woodruff-Pak, 1999). Fi-
nally, use of this methodology while recording simultaneous cel-
lular activity in hippocampal efferent targets will begin to address
the fundamental question of how the slow-wave state of the
hippocampus affects other learning-related circuits to predict and
modulate behavioral learning.
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Received May 15, 2003
Revision received September 17, 2003
Accepted September 29, 2003 ?
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