a r t ic l e s

Patterns across multiple memories are identified
over time© 2014 Nature America, Inc. All rights reserved.

Blake A Richards1–6, Frances Xia1,3, Adam Santoro1,4, Jana Husse1–4, Melanie A Woodin6,
Sheena A Josselyn1–4 & Paul W Frankland1–4
Memories are not static but continue to be processed after encoding. This is thought to allow the integration of related episodes
via the identification of patterns. Although this idea lies at the heart of contemporary theories of systems consolidation, it has
yet to be demonstrated experimentally. Using a modified water-maze paradigm in which platforms are drawn stochastically froma spatial distribution, we found that mice were better at matching platform distributions 30 d compared to 1 d after training.
Post-training time-dependent improvements in pattern matching were associated with increased sensitivity to new platforms thatconflicted with the pattern. Increased sensitivity to pattern conflict was reduced by pharmacogenetic inhibition of the medial
prefrontal cortex (mPFC). These results indicate that pattern identification occurs over time, which can lead to conflicts between
new information and existing knowledge that must be resolved, in part, by computations carried out in the mPFC.
Learning does not happen in a vacuum. Rather it occurs against the
backdrop of an animal’s cumulative experience. Related experiences
form knowledge structures referred to as schemata1–3 or categories4,5,
which then influence how new information is encoded. The degree towhich new experiences match existing structures determines whichbrain networks are engaged and the speed of memory consolidation6–10.
In particular, in both humans and rodents the mPFC appears to beimportant for mediating conflicts between new and old experiences
and updating existing schemata6,7,9,11.
Psychological and computational accounts suggest that theidentification of commonalities (i.e., patterns) across experiences isnecessary for the construction of schemata and categories3,12,13. This
process of identifying regularities in data may occur rapidly as patterns are encountered14 as well as in the immediate post-encoding rest
period15–18. However, it has been hypothesized that a similar process
persists over more extended time periods (e.g., weeks or months) after
initial encoding is complete. This long-standing idea19,20 forms thebasis of contemporary theories of systems consolidation21–25, which
propose that detailed episodic memories stored as individual patterns
in the auto-associative networks of the hippocampus are transformed
into semantic or schematized memories stored using distributed representations in the neocortex. Consistent with this, previous researchhas demonstrated that memories can generalize over time26,27 and that
remote memories may be less detailed28. However, there is no direct
evidence that pattern identification persists over an extended time
period after initial encoding. Moreover, how post-encoding pattern
identification then influences new learning is not known.
To address these issues we developed a new water-maze paradigm
in which mice are exposed to platform locations drawn stochasticallyfrom a specific spatial distribution. This design allowed us to assess
the extent to which mice could match the pattern defined by thespatial distribution 1 d or 30 d after training. Further, we examined
how mice reacted to a new training platform location when it waseither consistent or in conflict with the pattern. We found that after
training was complete, the passage of time improved the match
between mouse search paths and the pattern presented during training. This was associated with delay-dependent differences in thelearning of new platforms, with increased sensitivity to conflicting
platform locations 30 d after training. Increased sensitivity to a conflict between previous patterns and new information was reducedby pharmacogenetic inhibition of the mPFC. These results indicatethat a continued process of pattern identification occurs over time,
which can lead to conflicts between new information and existingknowledge structures that must be resolved, in part, by computations
carried out in the mPFC.
RESULTS
The passage of time improves pattern matching
According to contemporary theories of systems consolidation, thepassage of time allows for the identification of patterns across multiple
experiences (Fig. 1a). Whereas one consequence of this process is thatmemory of details for specific experiences may fade with time, one
benefit might be that the consolidated ‘composite’ memory is a betterreflection of the cumulative experience22,23. To assess this, we used amodified version of the delayed matching-to-place water-maze task29.
On each training day, we presented mice with four opportunities tofind a platform in a fixed location. However, across days we moved
the location of the platform, with each specific daily location selected

1Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada. 2Department of Psychology, University of Toronto, Toronto,

Ontario, Canada. 3Department of Physiology, University of Toronto, Toronto, Ontario, Canada. 4Institute of Medical Science, University of Toronto, Toronto, Ontario,
Canada. 5Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada. 6Department of Cell and Systems Biology, University ofToronto, Toronto, Ontario, Canada. Correspondence should be addressed to P.W.F. (paul.frankland@sickkids.ca).
Received 29 January; accepted 8 May; published online 1 June 2014; doi:10.1038/nn.3736

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Probability

Frequency

East

10
8

Pattern***
10

1 d 30 d
π/2

ea

na

π

)

M

s
3π/2
rad
n g u l a c a ti o n (
r lo

ea

nt

M

0

ou
C

0.1
0.2
0.3
Zone probability

2

1d

1
0

1d

0

2

nt

π

ou

2

2

Last platform
Platforms mean

π/2

3

6
4

3

30 d
C



d

At the longer delay, mice crossed specific platform locations less frequently (Fig. 1e). This suggests that behavior in the 30-d probe test
was driven less by any single day’s training experience and insteadreflected the cumulative training experience. Improved patternmatching depended on exposure to the pattern and did not emerge
spontaneously as a result of either decreased memory accuracy or
increased search-path entropy (Supplementary Fig. 5). Similarly,
time-dependent shifts in search strategy (e.g., swimming closer tothe walls of the pool or reduced searching for more recent platforms)
could not account for better pattern matching at the remote delay(Supplementary Figs. 6 and 7a,b).
Our data suggest that the passage of time allowed for improved
pattern matching. We next asked whether similar time-dependent
improvements would emerge after training on a more complex
distribution. In this case, we trained mice on a series of platforms
drawn from a weighted bimodal distribution, with uneven peaks
centered in the north and east quadrants of the pool. Platforms
were twice as likely to occur in the north quadrant (Fig. 2a, andSupplementary Figs. 1b and 2b), and therefore the best match tothe pattern would be to spend twice as much time in the north versus
east quadrant. At the 1-d delay, mice spent almost equal time in
the north and east quadrants. In contrast, at the 30-d delay, micespent roughly twice as much time in the north versus east quadrant,
which indicated that pattern matching improved over time (Fig. 2b).
Consistent with this, after a 30-d delay, but not a 1-d delay, the timethat mice spent searching in the zone around any given platformb

P(location)
North(Time in N)/(time in E)

30 d probecTime in platform zone (%)© 2014 Nature America, Inc. All rights reserved.

from a normal distribution in polar coordinates (Supplementary
Fig. 1a). Accordingly, the pattern emerged across multiple training
days but was not apparent on any single training day. If post-encoding
processing facilitates pattern identification, then some delay between
the completion of training and testing would allow mice to better
match the overall distribution of platforms (perhaps at the expenseof spending time at any single day’s location; Fig. 1b). To evaluate
the impact of a post-encoding delay, we presented mice with a probe
test (with the platform removed from the pool) either 1 d or 30 d
after the end of training (Supplementary Fig. 2a). To quantify theextent to which search paths on the probe test matched the patternof platform locations experienced during training, we calculated the
Kullback-Leibler divergence30 (DKL) between the search paths and theplatform distribution. The DKL is a measure of the difference between
two probability distributions; low DKL values indicate high patternmatching (Supplementary Fig. 3).
On any given training day, latency to locate the platform declined
across trials and escape latencies generally declined over the courseof training (Supplementary Fig. 4). During the probe test, the searchpatterns were sensitive to the delay between the completion of training and testing (Fig. 1c). Consistent with our prediction, there was astronger correspondence between search path and overall platform
distribution 30 d versus 1 d after the completion of training (indicated by a lower DKL value; Fig. 1d). These results indicate that thepassage of time, even in the absence of additional maze experience,
allowed mice to better match the overall distribution of platforms.

eMean platform crossingsd

DKL(P||Path) (nats)ca 1 d probe

P(platform location)

DistinctTime
1 d probe
30 d probe
Figure 1 Passage of time improves behavioral
Pattern
episodes(episodes)(pattern)match to pattern. (a) Diagram illustrating
hypothesized post-encoding transformationTimeof memories from information about distinct
episodes to information about the overall
statistical patterns in the experiences.
Mnemonic variable
Mnemonic variable(b) Schematic of the water-maze task wedesigned to test for post-encoding pattern***
identification, with gray circles indicating
1.0
1 d probe
30 d probe
P(location)
specific platform locations drawn from a6
0.8
probability distribution, and the heat map
40.6
showing the distribution. (c) Averaged searchpaths for both groups (1 d and 30 d probe)
0.4
2
and the platform distribution (right). Gray
0.2
circles indicate specific platform locations.
00(d) DKL values 1 d and 30 d after training
1d
30 d
1d
30 d
(t-test 1 d vs. 30 d: 1 d n = 52 mice, 30 d n = 40
mice, t90 = 2.22, P = 0.03). (e) Mean platform location crossings 1 d and 30 d after training (Welch’s t-test 1 d vs. 30 d: 1 d n = 52 mice, 30 d n = 40 micet89.61 = 3.10, P = 0.002). Data are for individual mice, with black line and shaded region indicating mean ± s.e.m. (*P < 0.05, **P < 0.005).

30 d

1
0

na

0

s
3π/2
rad
n g u l a c a ti o n (r lo)Figure 2 Pattern matching improves over time with alternate distribution
and measures. (a) Averaged search paths for both groups (1 d and 30 dprobe) and the platform distribution with 2:1 weighting in the north vs.
the east quadrant (right). Gray circles indicate specific platform locations.
(b) Time spent searching in the north quadrant (N) vs. the east quadrant(E) 1 d and 30 d after training (t-test vs. mean of 2: 1 d n = 12 mice,t11 = −13.05, P = 4.91 × 10−8; t-test vs. mean of 2: 30 d n = 12 mice,t11 = −1.73, P = 0.11). Data are for individual mice, with black line andshaded region indicating mean ± s.e.m. The dotted line indicates theratio that corresponds to the pattern of the platform distribution. (c) Timespent in 10-cm zones centered on each platform vs. platform probability
in the distribution 1 d and 30 d after training (Pearson’s: n = 9 platforms,
1 d r = −0.58, P = 0.1; 30 d r = 0.91, P = 0.0006; z-test 1 d vs. 30 d:z = 2.19, P = 0.001). Data are mean ± s.e.m. across mice. Dotted lines
indicate lines of best fit. (d) Mean angular positions of the mouse searchpaths 1 d and 30 d after training. Data are histograms, with last platform
and mean platform locations highlighted (***P < 0.0005).

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a

b

C
on

C
on

c

d

e

Time in conflict zone (%)

b

DKL(P||Path) (nats)ain pattern matching (Fig. 3a). To evaluate this idea, we modified thetraining protocol. As previously, we trained mice to find a new platform
location on each day, with locations for the first 8 d matching those in thefirst experiment (Supplementary Fig. 1c). However, when that training
was complete, we gave mice a set of test trials where they were presented
with a new platform location. We placed this new platform at the meanof the former distribution (‘consistent’) or in an area of the pool that hada low probability according to the distribution (‘conflicting’) (Fig. 3b andSupplementary Fig. 1c). To assess the impact of the passage of time, thesetest trials occurred either 1 d or 30 d after training. We then gave all groups
a probe test 1 d later (Supplementary Fig. 2c).
On each training day, latency to locate the platform declined across trials, and escape latencies generally declined over the 8 d of training (Fig. 3c).
Although there were no group differences in latency on the final day of
training, differences emerged on the test trials. In particular, mice took
longer to locate the conflicting (compared to the consistent) platform on
the final trial, and this difference was more pronounced 30 d after training(Fig. 3c). This suggests that over time mice became increasingly sensitive
to the difference between a new instance that was consistent with the pattern versus an instance that conflicted with the pattern.

Consistent

Improved pattern matching influences new learning
Only once a pattern has been identified is it possible to determine whether a
new event is consistent or in conflict with that pattern. As such, the sensitivity
to conflicts may increase as a function of time-dependent improvements

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Conflict

1 d 30 d
1d
Figure 4 Time-dependent sensitivity to
Last platform
Conflict
30 dpattern conflict with alternate distribution.
Consistent1d
30 d
Consist.
*15
3
Conflict*(a) Schematic of the experimental design60
to control for any possible effects of
50
2
10
displacement from the last training platform40
or the pool wall; consistent and conflict
30
5
1
20
platforms were equidistant from the last10
platform and equidistant from the wall.
000(b) Escape latencies for all groups on the
New platform
Consist. Conflict
1 d 30 d
day of exposure to the new platforms (two-way
ANOVA, 1 d vs. 30 d × consistent vs. conflict: (1 d, consistent) n = 12 mice, (30 d, consistent) n = 15 mice, (1 d, conflict) n = 22 mice,(30 d, conflict) n = 23 mice; 1 d vs. 30 d F1,68 = 0.46, P = 0.5, consistent vs. conflict: F1,68 = 0.77, P = 0.38; interaction F1,68 = 4.07, P = 0.047).
(c) Averaged search-path distributions from the probe test; small circles indicate specific platform locations. (d) DKL values 1 d and 30 d after training
for mice exposed to consistent and conflict platforms (two-way ANOVA 1 d vs. 30 d × consistent vs. conflict: 1 d vs. 30 d F1,68 = 3.96, P = 0.05,
consistent vs. conflict: F1,68 = 16.01, P = 0.001; interaction F1,68 = 1.34, P = 0.25; post hoc Tukey’s test, high P, 1 d vs. low P, 30 d: P < 0.05;
post hoc Tukey’s test, (30 d, conflict) vs. (1 d, consistent): P < 0.05, (30 d, conflict) vs. (30 d, consistent): P < 0.05). (e) Time spent searchingin a 10-cm radius zone around the conflict platform 1 d and 30 d after training (t-test (30 d, conflict) vs. (1 d, conflict): t43 = −2.41, P = 0.02).
The dashed line indicates the chance level of searching in a 10 cm radius circular zone. Data are mean ± s.e.m., or individual mice, with black line
and shaded region indicating mean ± s.e.m. (*P < 0.05).
Escape latency (s)© 2014 Nature America, Inc. All rights reserved.

correlated with the probability assigned to that zone by the
distribution (Fig. 2c).
The ‘recency’ of the last platform location (i.e., the shorter length oftime that has passed since the last location was encountered relative
to the other locations) may represent another factor that influences
search paths. To address this, we determined whether order of platform presentation during training influenced search behavior in theprobe test. Overall, platform order weakly influenced search behavior(Supplementary Fig. 7c). However, the last platform location did
influence search behavior. At the 1-d delay, we found that the focus of
the mouse search paths corresponded with the last platform location,
but at the 30-d delay, the focus shifted to the mean of the distribution(Fig. 2d). Therefore, probe-test performance is more influenced by
the specific memory for the most recent platform 1 d after training
but more so by the overall distribution at the 30-d delay.

Time in conflict zone (%)

DKL(P||Path) (nats)

f

fli
ct

e

si
st
.

d

Conflict

Escape latency (s)cConsistent

Probability

Frequency

P(platform location)

Equivalence
Pattern
Equivalence
Pattern
Figure 3 Sensitivity to difference between
Timeof new dataconsistent/conflictof new dataconsistent/conflictpattern consistency versus conflictvs.
vs.
increases over time. (a) Diagram illustratingTime
hypothetical difference between assimilating
new information into memory before andafter post-encoding pattern identification
has occurred. (b) Schematic of the waterConsistent
Conflict
Mnemonic variable
Mnemonic variable
maze task we designed to test for the effects
1dof post-encoding pattern identification on
Probe
1 d 30 d
**
30 d
1d
30 d
Conflictlearning new platforms. (c) Escape latencies
60**
Consistent
12
50(time to find the platform) for all groups
1.6
40
across training and on the day of exposure
1.2
830
to the new platforms (two-way analysis of
0.8
20
variance (ANOVA), 1 d vs. 30 d × consistent
4
0.4
10
vs. conflict, final trial latency: (1 d,00consistent) n = 24 mice, (30 d, consistent)0
1 2 3 4 5 6 7 8 New platform
1 d 30 dn = 11 mice, (1 d, conflict) n = 12 mice,
Training day(30 d, conflict) n = 12 mice; 1 d vs. 30 d
F1,55 = 11.42, P = 0.001; consistent vs. conflict F1,55 = 29.27, P = 1.41 × 10−6; interaction F1,55 = 7.54, P = 0.008). (d) Averaged search paths for
all groups from a probe test the next day; small circles indicate specific platform locations. (e) DKL values 1 d and 30 d after training for mice exposed
to consistent and conflict platforms (two-way ANOVA, 1 d vs. 30 d × consistent vs. conflict: 1 d vs. 30 d F1,55 = 11.74, P = 0.001; consistent vs.
conflict F1,55 = 24.07, P = 8.65 × 10−6; interaction F1,55 = 8.16, P = 0.006; post hoc Tukey’s test, (30 d, conflict) vs. (1 d, consistent): P < 0.001,(30 d, conflict) vs. (30 d, consistent): P < 0.001). (f) Time spent searching in a 10-cm radius zone around the conflict platform 1 d and 30 d after
training (t-test, (30 d, conflict) vs. (1 d, conflict): t16.11 = −3.29, P = 0.005). The dashed line indicates the chance level of searching in a 10 cm radius
circular zone. Data are mean ± s.e.m., or individual mice, with black line and shaded region indicating mean ± s.e.m. (**P < 0.005).

a r t ic l e s© 2014 Nature America, Inc. All rights reserved.

Figure 5 CNO inhibits spiking activity in hM4Di
infected neurons in the mPFC. (a) Illustrationof the regions targeted for AAV8-Camk2ahM4Di-mCitrine microinfusion (left).
Image of a representative infection in theanterior cingulate cortex (ACC), prelimbic cortex(PrL) and orbital cortex (Orb) (right).
(b) Example traces showing responses tobath perfusion of 1 µM CNO recorded via
whole-cell current clamp in infected (hM4Di+)
and uninfected (hM4Di−) neurons of ex vivo
mPFC slices. (c) Example traces showing
responses to injection of square pulsesof 200 pA current before and after CNO
bath application.

a

b
ACC

5 mV
hM4Di–

PrL

1 min
+

hM4Di

Orb
500 µmc

hM4Di–

1 µM CNO

hM4Di+40 mV

hM4Di–

hM4Di+

1 µM CNO

500 msLearning is driven by differences between
200 pA
expected and observed outcome (i.e., prediction error31). Therefore, if the passage of timeallows for refinement of an identified pattern, then presentation of whole-cell patch clamp recordings to characterize the effects of CNO
the conflicting platform at the 30-d delay should produce a larger in AAV-infected neurons. Application of 1 µM CNO hyperpolarized
prediction error and lead to greater learning. The probe test provided infected neurons (Fig. 5b) by −7.07 ± 2.09 mV (mean ± s.e.m) but
an opportunity to evaluate learning after the presentation of the new not uninfected neurons, which showed a change of −0.12 ± 0.81 mV
platforms. When we presented mice with the conflicting platform after treatment with CNO (t-test for hM4Di− (no hM4Di expression)
after a 1-d delay, they reverted to searching the former distribution versus hM4Di+ (hM4Di expression): hM4Di− n = 3 cells, hM4Di+
in the subsequent day’s probe test (Fig. 3d). In contrast, when we n = 4, t4 = 3.09, P = 0.04). This was associated with inhibition ofpresented mice with the conflicting platform after a 30-d delay, the spiking responses to 200 pA square current pulses (Fig. 5c), with amice altered their search strategy, as reflected by a significant increase 38.59 ± 9.32% reduction in the spike rate of infected neurons afterin the DKL value (Fig. 3e). These data suggest that after a 30-d delay, application of CNO, compared to a 3.03 ± 8.02% increase in uninmice were more sensitive to instances that violated the pattern that fected neurons (t-test versus mean of 1: hM4Di− n = 3, t2 = 0.38,
they had experienced during training, and this produced a high pre- P = 0.74; t-test versus mean of 1: hM4Di+ n = 4, t3 = −4.14, P = 0.03).
diction error and consequently supported new learning. In line with
After mice recovered from surgery, we trained them in the same
this interpretation, in the probe test mice searched for the conflict- modified water-maze protocol as in the previous experiment (Fig. 4
ing platform more if it was presented 30 d after training (Fig. 3f). and Supplementary Fig. 1d). On any given training day, latency to
Presentation of the consistent platform did not alter search paths on locate the platform declined across trials, similar to previous experithe probe test, regardless of the post-training delay (Fig. 3d,e). We ments (Supplementary Fig. 8). Either 1 d or 30 d after training, we
replicated these findings using a different distribution as well as con- presented mice with a platform that was either consistent or in conflict
flicting and consistent platforms that were equidistant from the edge with the pattern. To pharmacogenetically inhibit mPFC neurons, weof the pool (Fig. 4a–e and Supplementary Fig. 1d). Therefore, the injected the mice with CNO or vehicle 30 min before the mice learned
tendency for a conflicting platform to induce longer escape latencies, the new platform (Supplementary Fig. 9). We then probed the mice
as well as stronger learning, does not depend on specific distributions the next day in a drug-free state in order to evaluate how mPFC inhior particular platform locations but only on the passage of time.
bition had altered learning. After a 1-d post-training delay, CNO had
no effect on learning either the consistent or conflicting platforms.
The mPFC mediates learning of conflict with prior patterns
During the subsequent probe test, search paths in CNO and vehicle
The mPFC may be important for resolving conflicts between new groups did not differ in DKL or the time spent in the new platform
information and background knowledge11. Our data indicate zone (Fig. 6a–c).
that sensitivity to conflicts increases as a function of time andHowever, mPFC inhibition significantly impacted learning of
therefore predict a more pronounced role for the mPFC in con- a conflicting platform when there was a 30-d delay after training.
flict resolution at remote time points after training. To test this, we As expected, vehicle-treated mice presented with the conflicting
microinfused an adeno-associated virus (AAV) carrying a Camk2a- platform after 30 d altered their search behavior, shifting their focus
driven modified sequence encoding the human muscarinic M4 to the new platform in the subsequent probe test (Fig. 6d). In conreceptor, known as hM4Di, tagged with the fluorescent reporter trast, CNO-treated mice presented with the conflicting platform
mCitrine, into the mPFC and then trained mice in our water- divided their search between the training distribution and the newmaze paradigm. hM4Di is a Gi/o-coupled designer receptor exclu- platform in the subsequent probe test (Fig. 6d). This division between
sively activated by designer drugs (DREADD) that is insensitive to training distribution and the new platform was indicated by aendogenous ligands but activated by a synthetic ligand (clozapine- significant reduction in the DKL value and time spent searchingN-oxide (CNO)32). When bound to CNO, this Gi/o-coupled for the new platform in the CNO injection group compared to the
DREADD induces membrane hyperpolarization and inhibition of vehicle group (Fig. 6e,f). Injection of CNO during presentation of
spiking activity33.
the consistent platform did not alter search paths on the subsequent
Histological examination revealed robust, bilateral expression of probe test (Fig. 6d–f). Altogether, these results indicate that the mPFC
hM4Di in neurons in the mPFC, including the anterior cingulate has an important role in updating search behavior in response to newcortex and prelimbic cortex regions (with some limited infection in platforms that conflict with patterns encountered during training, but
the orbital cortex) (Fig. 5a). In a subset of mice, we performed ex vivo only after a 30-d delay.
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DISCUSSION
The hypothesis that there is a persistent process of pattern identification after encoding to combine and schematize memories is central
to contemporary theories of systems consolidation21–25. According
to these theories, statistically common elements across multiple
memories constitute patterns that can be used during consolidation
to construct more general knowledge about the world21–25. Here we
provided evidence supporting this hypothesis. Our data indicate thatmice trained in a water-maze task to find platforms drawn from astatistical distribution exhibited better pattern matching after a 30-ddelay compared to a 1-d delay. We observed this improvement in pattern matching for two distinct distributions, using different measuresof pattern matching. This time-dependent improvement in patternmatching was associated with increased sensitivity to conflicting platforms. Given that conflicts between new information and backgroundknowledge engage the mPFC, this suggested that the mPFC may havea more pronounced role in learning about conflicting platforms at
remote time points. In agreement with this, pharmacogenetic inhibition of the mPFC only affected the learning of conflicting platforms
after a 30-d delay. Altogether, our results support the idea that there isan extended process of pattern identification after encoding and that
identified patterns can influence new learning as a result of computations performed in the mPFC.
An important question that our studies do not directly address is:what is the nature of the process that continues after encoding, i.e.,
what leads to better pattern matching and greater sensitivity to patternconflicts at remote time points? One component of the transformation
may be degradation of the original memories. However, the timedependent improvement in pattern matching to the weighted bimodal
distribution in Figure 2 is inconsistent with a memory-degradation
process (reduced memory precision would not alter the ratio of timespent in north versus east quadrants). Another factor that is very
likely to be involved is recency34, where memory strength is related
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Veh.

CNO

Veh.

CNO

Veh.

CNO

Veh.

CNO

CNO

Veh.

CNO

Veh.

Time in new platform zone (%)

f

DKL(P||Path) (nats)eTime in new platform zone (%)

CNO

Veh.

CNO

Veh.

DKL(P||Path) (nats)

VehicleCNO
CNO

Vehicled© 2014 Nature America, Inc. All rights reserved.

cTraining platforms
Figure 6 mPFC inhibition impairs learning
1 d delay
Consistent
Conflictof conflicting platform after 30-d delay.
1.412(a) Averaged search-path distributions
1.2
from the probe tests after a 1-d delay; small
10
circles indicate specific platform locations.
1.08(b) DKL values from probe tests for mice
0.8
6
exposed to new platform (consistent or conflict)
0.6
and given either vehicle or CNO injections
4
0.4
1 d after training (two-way ANOVA, consistent
Chance2
0.2
vs. conflict × CNO vs. vehicle: (consistent,
vehicle) n = 7 mice, (consistent, CNO) n = 8,00Consistent
Conflict
Consistent
Conflict(conflict, vehicle) n = 5, (conflict, CNO)n = 4; consistent vs. conflict F1,20 = 17.02,
P = 0.0005; vehicle vs. CNO F1,20 = 0.6,
30 d delay
14
2.5
P = 0.45; interaction F1,20 = 0.05, P = 0.82).
*
12(c) Time spent searching in a 10-cm radius*
2.0
zone around the new platform for mice exposed10
to consistent and conflict platforms and given
1.5
8either vehicle or CNO injections 1 d after6
1.0
training (two-way ANOVA, consistent vs.
4
conflict × CNO vs. vehicle: consistent vs.
0.5
Chance2
conflict F1,20 = 0.67, P = 0.42; vehicle
vs. CNO F1,20 = 0.03, P = 0.87; interaction
00Consistent
Conflict
Consistent
ConflictF1,20 = 0.33, P = 0.57). (d) Averaged searchpath distributions from the probe tests after a
30-d delay. (e) DKL values as in b, 30 d after
training (two-way ANOVA, consistent vs. conflict × CNO vs. vehicle: (consistent, vehicle) n = 6 mice, (consistent, CNO) n = 8, (conflict, vehicle) n = 8,
(conflict, CNO) n = 13; consistent vs. conflict F1,31 = 14.54, P = 0.0006; vehicle vs. CNO F1,31 = 2.93, P = 0.1; interaction F1,31 = 3.43, P = 0.07;
post hoc Tukey’s test, (conflict, vehicle) vs. (conflict, CNO): P < 0.05). (f) Analysis as in c, 30 d after training (two-way ANOVA, consistent vs. conflict× CNO vs. vehicle: consistent vs. conflict F1,31 = 7.36, P = 0.01; vehicle vs. CNO F1,31 = 7.04, P = 0.01; interaction F1,31 = 0.83, P = 0.37; post hoc
Tukey’s test, (conflict, vehicle) vs. (conflict, CNO): P < 0.05). Data are mean ± s.e.m. (*P < 0.05).

to how recently an event has been experienced. We found that after
a 1-d delay search-path behavior was often centered at the positionof the last platform encountered, whereas after a 30-d delay searchpaths were centered at the mean of the platform distribution (Fig. 2d).
This suggests that one contribution to better pattern matching after
a 30-d delay was reduced dominance of the memory for the last platform. However, recency effects cannot explain all of our results. The
increased sensitivity of mice to the difference between a consistentand conflicting platform after a 30-d delay, measured by their escapelatencies (Fig. 3c), is the opposite of what a recency account would
predict: a reduction in the strength of the memories for the mostrecent platforms should have made it easier to learn a new platform in
a new location of the pool, not harder. Therefore, although a reductionin the focus on recent platforms is likely to contribute to improved
pattern matching over time, it is unlikely to be the sole factor driving
the behavior we observed.
What other processes might be involved in time-dependent pattern matching and sensitivity to pattern conflicts? It has been proposed that statistical regularities across memories are extracted via
hippocampal replay, which leads to the categorization of experiencesin the neocortex19,20 (an idea that subsequently has been elaborated
in computational models of systems consolidation 24). With this inmind, it is plausible that time-dependent improvements in patternmatching could reflect an active process of statistical learning. Given
that the patterns in platform locations that our mice were exposed towere explicitly statistical in nature, our data are consistent with thisinterpretation. In line with this, there is evidence that the brain canlearn statistical models and estimate the probability of different values
or events based on experience. Early psychological studies indicatedthat people can rapidly identify statistical patterns in visual stimuli3,
and computational models of sensory learning in the neocortex havelong proposed that it is statistical in nature35–37. These ideas have been
supported more recently by neuroimaging work in humans 14,38 and© 2014 Nature America, Inc. All rights reserved.

a r t ic l e s
in vivo recordings in primary sensory cortices of ferrets39. Likewise,
learning in sensorimotor integration tasks is predicted by Bayesianstatistical models40,41. Studies in rodents using multiple-choice behavioral tasks have also demonstrated that the neocortex can compute
estimates of expectation and confidence42,43. Finally, studies inhumans have shown that sleep facilitates statistical learning, possibly
via memory replay15–18. Therefore, the types of learning mechanisms
known to operate in the brain are well suited to identify statistical
patterns across multiple memories during consolidation.
Our finding that inhibition of the mPFC affects learning of conflicting platforms after a 30-d delay has interesting implications for rapid,
schema-based consolidation6,7,10. First, these data suggest that the roleof the mPFC in rapid consolidation may be limited to the learning of
new information that breaks established patterns11. It is possible that
consolidation of information that closely matches (or only slightly
deviates from) previous experiences could be achieved without thefunctions performed by the mPFC. Second, our findings leave open
the possibility that rapid, schema-based consolidation would not be
possible in tasks where initial learning is too rapid to permit timedependent pattern identification. Recent memories may not provide
sufficient scaffolding for quickly encoding information outside of
the hippocampus. The framework provided by the task we used here
provides an approach for investigating these issues in the future.
Methods
Methods and any associated references are available in the online
version of the paper.
Note: Any Supplementary Information and Source Data files are available in the
online version of the paper.
Acknowledgments
This work was supported by grants from the Canadian Institutes of HealthResearch to P.W.F. (MOP-77561), S.A.J. (MOP-74650) and M.A.W. (MOP-123466).
B.A.R. received financial support from a Banting Postdoctoral Fellowship via the
Natural Sciences and Engineering Research Council of Canada. B.A.R., F.X. and
J.H. received financial support from the Canadian Institutes of Health Research
Sleep and Biological Rhythms Training Program. A.S. received financial support
from the Hospital for Sick Children. We thank G. Hinton and M. Moscovitch forcomments on earlier drafts of this manuscript.
AUTHOR CONTRIBUTIONS
B.A.R., S.A.J. and P.W.F. conceived and designed the experiments. B.A.R., F.X.,
A.S. and J.H. performed the behavioral experiments. B.A.R. and M.A.W. performed
the patch clamp experiments. B.A.R. conducted the analyses. B.A.R., S.A.J. and
P.W.F. wrote the paper.
COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
Reprints and permissions information is available online at http://www.nature.com/
reprints/index.html.
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advance online publication

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ONLINE METHODS

Animals. All experiments conformed to the guidelines set forth by the CanadianCouncil for Animal Care and the Animal Care Committee at the Hospital for
Sick Children. 8–9-week-old male, wild-type mice were used in all experiments.
These mice were the F1 generation derived from crossing C57BL6n and 129SvEv
mice (breeding mice were sourced directly from Taconic). Mice were weaned at
21 d, and then housed 4–5 mice per cage. The housing room was maintainedon a 12 h–12 h light-dark cycle, with lights on during the day. All experiments
were performed in the morning between 9 a.m. and 12 p.m. One week before thecommencement of experiments mice were briefly anesthetized using isoflurane,
and their ears were punched with individual markings to differentiate cage mates.
Cages of mice were assigned to different experimental groups randomly. The
experimenter was not blinded regarding group assignments because this would
have required using different experimenters for training and probe tests, which
could introduce unwanted behavioral effects. In the 4 d before training, mice
were handled by the experimenter, two at a time, for 3 min each day. No mice
were excluded from any analyses in purely behavioral experiments (Figs. 1–4),
with the exception of one mouse that escaped the pool during the second dayof training and did not search for the platform afterward. Mice were excluded
from the pharmacogenetic experiments (Figs. 5 and 6) based on post-experiment
histology: only mice for whom viral microinfusion resulted in robust bilateralexpression of hM4Di-mCitrine in the appropriate location of the mPFC were
included (see below for more details on the exclusion criteria).
Behavioral apparatus. Experiments were performed in a water-maze pool, which
was 120 cm in diameter and 50 cm in depth. The pool was surrounded by whitecurtains with large red and black shapes to provide visual cues for navigation.
Ambient light in the room was kept at a constant, moderately low level across alldays of the experiments.
Each day, before mice were brought into the room, the pool was filled to 40 cm
with water and the temperature of the water was adjusted to 22 °C. The water
was rendered opaque using nontoxic, white children’s paint. Video tracking of
the mice was conducted using an Actimetrics WaterMaze system (Actimetrics)
at a rate of 8 frames/s. The tracking camera was mounted above the center ofthe pool and fixed to the ceiling. A custom-built platform with a double-jointed
rotating arm was used to ensure that the platform position could be adjusted toa specific position within the pool. The platform was circular (10 cm diameter).
The platform positions were preselected using Matlab 7.12 (MathWorks) andentered into the WaterMaze software. The video tracking device was then used
to confirm that the actual platform was set to the correct position on each day.
The platform was submerged ~5–8 mm below the water level.
Behavioral training and testing. The training procedure was based on the
delayed matching-to-place task in ref. 29. As in that task, the platform location
changed on each day of training, and all mice were trained for four trials on thisnew location. However, in this task the locations were selected from a statistical
distribution in polar coordinates. Specifically, we selected the distance of a given
platform from the center of the pool, d, and the angle of the arc connecting theplatform and the eastern cardinal direction in the pool, θ, using a variety of
distributions (Supplementary Fig. 1). In the first set of groups (Fig. 1) the platforms for the first 8 d of training were generated by selecting d from a normal
distribution with a mean of 35 cm and an s.d. of 10 cm, and selecting θ from a Von
Mises distribution (a circular normal distribution) with a mean of 0.75π radians
and a κ parameter of 5. The 9th platform for these groups was presented at themean of the selection distribution (Supplementary Fig. 1a). In the weighted
bimodal distribution groups (Fig. 2), d was again selected from a normal distribution with a mean of 35 cm and a s.d. of 10 cm, but now, θ was selected by first
randomly selecting one of two Von Mises distributions, one with a mean of 0.5π
radians, the other with a mean of 0 radians, and then selecting from the chosen
distribution. The selection of the two distributions was weighted 2:1 toward the first(0.5π radians mean), and both used a κ parameter of 8 (Supplementary Fig. 1b).
For the first set of groups presented with new platforms (Fig. 3), training
was conducted with the same eight platforms used in the first experiments, butduring the test trials either the same platform at the mean of the distribution waspresented (‘consistent’) or a different platform was presented that was low probability in the distribution (‘conflict’) (Supplementary Fig. 1c). For the second
set of groups presented with new consistent or conflict platforms (Fig. 4),

doi:10.1038/nn.3736

a new set of seven platforms was selected from the distribution used for the firstexperiments. The 8th platform was then placed along the northern axis of thepool, 35 cm from the center. The consistent and conflict platforms were thenplaced 35 cm from the center along either the western or eastern axis, respectively,in order to preserve the displacement between the final day of training and thetwo different new platforms (Supplementary Fig. 1d). In the mPFC inhibition
experiments (Fig. 6), the same platforms used in the second set of new platform
learning experiments were used. In the single platform groups (Supplementary
Fig. 5), the same platform at the mean of the first distribution was presented eachday. The platforms used and their specific order over the days of training for allgroups are shown in Supplementary Figures 1 and 2.
For each training trial, mice were taken from their cage and carried to the poolarea on the hand of the experimenter. Mice were then lowered into the pool by
their tail at one of the 4 cardinal locations (north, south, east and west) facing thewall of the pool. The start locations were selected randomly and were identicalfor all mice in any given experiment (Supplementary Fig. 2). The experimenterstarted the trial manually once he or she left the water-maze area. Training trials
ended automatically when tracking software determined that the mice had stayedon the platform for 1 s. Mice were then left on the platform for 15 s, at which
point they were retrieved by the experimenter. If a mouse jumped off the platform
before the end of 15 s, it was directed back to the platform by the experimenter.
Trials lasted for a maximum of 60 s. On the first day, mice often did not get on
the platform and were guided there by hand of the experimenter at the end of thetrial. On the first trial of each day, when mice sometimes did not find the newplatform in the allotted 60 s, the experimenter would place his or her hand on
the platform until the mouse came to the platform and climbed onto it. At thestart of each trial a short 320 Hz tone played, at the end of each trial a short
440 Hz tone played and during the intertrial interval a 220 Hz tone repeatedly
played once every 5 s. The tones marked the trials for the experimenter, but they
were unlikely to have affected behavioral results as they were below the hearing
range of mice44. During training, when mice were not in the water maze they
were kept in their cages in an area behind the curtains.
On the new platform trials, when the consistent or conflict platforms wereintroduced, the procedure followed the same protocol as on the training days. On
probe tests, the same procedure as on the training days was followed except thatthe platform was absent from the pool. The probe trials lasted for 60 s. In experiments 1, 2 and 3 (Figs. 1–3), mice underwent two probe trials and the searchpaths from both trials were used for data analysis (see below). At the end of thefirst probe trial, mice were removed from the pool by the experimenter at the 60 s
mark at whichever location they happened to be at in the pool. In experiments 4
and 5 (Figs. 4 and 6), mice underwent a single probe trial. A subset of data (24 mice)from the first groups given a 1-d delay (Fig. 1) was reused for the first new
platform analyses (Fig. 3) as these training procedures were identical and thesemice were trained by the same experimenter (Supplementary Fig. 2). Further,because the experimental procedures were identical in experiments 4 and 5, a
subset of the mice that were trained by the same experimenter included in theanalysis in Figure 4 (15 of 72 mice) were from the vehicle condition in Figure 6.
The specific training protocols and testing time lines for each experimental groupare displayed in Supplementary Figure 2.
In the pharmacogenetic experiments (Fig. 6, and Supplementary Figs. 8b,cand 9), mice received intraperitoneal injections of either clozapine-N-oxide(CNO) or a vehicle solution 30 min before the first trial of the new platform
learning. Mice in the CNO group received an injection of 5 mg/kg CNO, which
was first dissolved in 20 µl DMSO, then mixed into 380 µl 1× PBS. Mice in thevehicle group received an injection of 20 µl DMSO mixed with 380 µl 1× PBS. All
drugs and ingredients for these experiments were obtained from Sigma.
Viral microinfusions. 45 d before training with a new platform (Supplementary
Fig. 9), mice in the pharmacogenetic experiments underwent stereotaxic surgery
for viral microinfusion of AAV into the mPFC. AAV8-Camk2a-hM4Di-mCitrine
was purchased from the University of North Carolina Vector Core. Mice werepretreated with atropine sulfate (0.1 mg/kg, intraperitoneal), anesthetized withchloral hydrate (400 mg/kg, intraperitoneal) and placed in a stereotaxic frame.
Skin on top of the head was retracted, and holes were drilled in the skull bilaterally
above the mPFC (anteroposterior = +1.9, mediolateral = ± 0.3, ventral = −1.8 mm
from bregma) according to ref. 45. AAV (2.0 µl/side) was microinjected throughglass micropipettes connected via polyethylene tubing to a microsyringe

nature NEUROSCIENCE© 2014 Nature America, Inc. All rights reserved.

(Hamilton) at a rate of 0.1 µl/min. Micropipettes were left in place an additional
5 min after microinfusion to ensure diffusion of vector. Micropipettes were
slowly retracted, the incision closed, and mice treated with analgesic (ketoprofen,
5 mg/kg, subcutaneous) and 1 ml saline.
After the pharmacogenetic experiments mice were transcardially perfusedwith 1× PBS followed by 4% paraformaldehyde. Brains were fixed overnight(4 °C) and transferred to a 30% sucrose solution. Coronal brain slices (30 µm)
across the entire extent of the mPFC were collected using a cryostat (Leica
CM1850). Every second section was mounted on a gel-coated glass slide andcovered with a coverslip with Vectashield fluorescence mounting medium (Vector
Laboratories). Mosaic images of the mPFC in the slices were then obtained using
a confocal laser scanning microscope (LSM 710; Zeiss).
Images of AAV infection were scored via two judges blinded to the groups.
Each mouse received an infection quality score from each judge ranging from 0
to 3, where 0 indicated no infection or severe damage to the tissue, 1 indicatedinfection, but only unilateral, weak or in the wrong location, 2 indicated robust
bilateral infection centered on the prelimbic and anterior cingulate cortices,and 3 indicated ideal infection. Only data for animals whose score averaged acrossboth judges was 2 or greater were included in subsequent analyses. As a result, of
the 112 animals included in the original experiments, only 59 were included in
the data analyses shown in Figure 6.
Electrophysiology. A subset of mice (4 mice) microinfused with AAV were used
to test the effects of CNO on hM4Di-infected neurons. 4 weeks after microinfusion of AAV into the mPFC, these mice were perfused with cold modified
artificial cerebrospinal fluid (mACSF) containing the following (in mM):
180 sucrose, 25 sodium bicarbonate, 25 glucose, 2.5 KCl, 1.25 sodium phosphate,
2 MgCl2, 1 CaCl2, 0.4 sodium ascorbate and 3 sodium pyruvate, saturated with
95% O2/5% CO2 and with pH and osmolarity adjusted to 7.4 and 290–295 mOsm,
respectively. Brains were quickly removed and placed for 30 s in a chilled mACSF
slurry. The posterior portions of the brain were removed, and the posterior edgeof the brain was glued to a slicing stage with an agarose block placed behind
it. The stage was then placed in a slicing chamber filled with mACSF chilled
to a slurry and continuously oxygenated with 95% O2/5% CO2. Brain slices(350 µm) were prepared on a VT1000S vibratome (Leica) and slices recoveredfor 1 h at room temperature in a continuously oxygenated mixture of 25 ml ofmACSF mixed with 25 ml ACSF (see recipe below).
During recording, slices were placed in a recording chamber perfused with
continuously oxygenated ACSF containing the following (in mM): 125 NaCl,
25 sodium bicarbonate, 25 glucose, 2.5 KCl, 1.25 sodium phosphate, 1 MgCl2,
2 CaCl2, 0.4 sodium ascorbate and 3 sodium pyruvate, saturated with 95%
O2/5% CO2 and with pH and osmolarity adjusted to 7.4 and 290–295 mOsm,
respectively. Solution temperature was maintained at 36 °C with a TC-344B temperature controller and SH-27B in-line solution heater (Warner Instruments).
Whole-cell recording pipettes with tip resistances of 4–7 MΩ were pulled fromthin-walled borosilicate glass (World Precision Instruments, TW-150F) using a
Sutter Instruments P-87. Pipettes were filled with a potassium gluconate–based
internal solution containing the following (in mM): 130 potassium gluconate,
10 KCl, 10 HEPES, 0.2 EGTA, 4 ATP, 0.3 GTP and 10 phosphocreatine, with
pH and osmolarity adjusted to 7.4 and 290–295 mOsm, respectively. Whole-cellrecordings were performed using a Multiclamp 700B amplifier and digitizedusing an Axon Digidata 1440A (Molecular Devices). Recordings were made fromboth fluorescent and nonfluorescent neurons in the mPFC, visualized with an
Olympus BX51WI equipped with infrared differential interference contrast and
GFP epifluorescence. Recordings were performed in current-clamp with bridgebalancing. After 10 min to ensure patches had settled, a 1-s, 200-pA current pulse
was injected to induce spiking. After this, 10-min continuous recordings were
performed. 1 min into the continuous recordings, ACSF containing 1 µM CNO
was switched into the perfusion system, and the effects on resting membranepotential were observed. After this, a second 200-pA current pulse was injected
to induce spiking in order to compare spiking before and after CNO application.
All reagents used for electrophysiology were obtained from Sigma.
Behavioral analysis. All data analysis was fully automated. Data were analyzed in
Matlab using a custom-designed toolbox called the Matlab water-maze (MWM)
toolbox. This toolbox is freely available for download under a GNU Lesser Public
License agreement at Http://www.franklandlab.com/?page_id=306/. Additionally,nature NEUROSCIENCE

the analysis used the CircStat Toolbox46 and the kernel density estimation (KDE)
Toolbox (see below), which is also available under a GNU Lesser Public License
agreement at http://www.ics.uci.edu/~ihler/code/kde.html.
Path and platform data from each training, testing and probe trial were loaded
into Matlab directly from the Actimetrics software data files and used to estimate
escape latency, platform crossings, time in quadrants, time in different platform
zones, thigmotaxis, path entropy, mean angular search position and DKL.
Escape latency (Figs. 3c and 4b, and Supplementary Figs. 4 and 8) was defined
simply as the length of the trial. Platform crossings (Fig. 1e and Supplementary
Fig. 5c) were defined as the number of times during the probe test when themouse entered a region of the pool that had been occupied by a platform at
any time during training. Time in platform zone (Figs. 2c, 3f, 4e, 6c,f and
Supplementary Fig. 7a,c) was defined as the percentage of time during theprobe trials that the mice spent in a circular zone with 10 cm radius centered on
the platform. In experiments with two probe trials both platform crossings andtime in platform zone measurements were averaged across both probe trials.
Mean angular location (Fig. 2d and Supplementary Fig. 7b) was calculated byconverting the search paths into polar coordinates and taking the circular mean
via the function provided in the CircStat toolbox. Thigmotaxis was measured byexamining the percentage of time that the animals spent in the outer 10-cm ringof the pool (Supplementary Fig. 6).
Path entropy (Fig. 1f and Supplementary Fig. 5d) and DKL (Figs. 1d, 3e, 4d
and 6b,e) were estimated by calculating a probability density function from eachsearch path during the probe using a kernel density estimation technique. Searchpaths from both trials were used as the centers for circular two-dimensional (2D)
Gaussian kernels with bandwidths of 5 cm. These kernels were then used togenerate a probability density function estimate, Psearch (normalized across thespace of the pool), at each grid point in a 1 cm × 1 cm grid covering the pool. The
path entropy, Hsearch, was then calculated as:
H search = − ∑ Psearch (x )ln(Psearch (x ))where x ranges over space in the pool.
DKL provides an estimate of the difference between two statistical distributions30. It does so by providing a measure of the average number of bits that are
lost when information from a target distribution is encoded using an alternative
distribution. We used the DKL to measure the difference between the distributionof individual mouse search paths on the probe test and the distribution of theplatforms experienced during training. To estimate the DKL, we defined the target
distribution as the probability density function, Pplatform (Figs. 1c, 2a and 4a),which was estimated using kernel density estimation. This was done using eachtraining platform location as the center for circular 2D Gaussian kernels. The
bandwidth of the Gaussian kernels was optimized using a likelihood crossvalidation search technique (implemented in the KDE Toolbox). The resulting bandwidth was 11.35 cm for the unimodal distribution (Supplementary
Fig. 1a), 14.84 cm for the first new platform test distribution (Supplementary
Fig. 1c) and 10.02 cm for the second new platform test distribution(Supplementary Fig. 1d). It should be noted that these specific bandwidths were
not critical as analyses using different bandwidths produced similar results. Using
Pplatform and Psearch, DKL was calculated as: Pplatform (x ) 
DKL (Pplatform  Psearch ) = ∑ Pplatform (x )ln  Psearch (x ) An example illustrating the calculation of DKL for two different search paths isgiven in Supplementary Figure 3. The DKL measure is particularly high when
the search path is absent from a nonzero region of the Pplatform distribution.
Therefore, a higher mean DKL indicates that mice did not provide sufficient coverage in their search of areas which had a nonzero probability according to the
Pplatform distribution.
Electrophysiology analysis. Electrophysiology data was analyzed in iPython
using NumPy, ScyPy and custom scripts. The change induced by CNO in theresting membrane potential was measured as the difference between the median
membrane potential in the 1 min of continuous recording before CNO application, and the median membrane potential in the final 1 min window of the 10 minrecording after CNO application. Spike rate was estimated by first detecting

doi:10.1038/nn.3736

spikes via a thresholding of the derivative of the spike trace, then calculating the
spike rate as the number of spikes generated over the 1 s pulse of current. The
effect of CNO on spike rate was measured by taking the spike rate observed after
CNO application and dividing it by the pre-CNO spike rate.

44. Heffner, H.E. & Heffner, R.S. Hearing ranges of laboratory animals. J. Am. Assoc.
Lab. Anim. Sci. 46, 20–22 (2007).
45. Paxinos, G. & Franklin, K.B.J. The Mouse Brain in Stereotaxic Coordinates Ed. 2ndedn. (Academic, 2001).
46. Berens, P. CircStat: a matlab toolbox for circular statistics. J. Stat. Softw. 31, 1–21
(2009).

© 2014 Nature America, Inc. All rights reserved.

Statistical analysis. Statistical analyses were conducted in Matlab (MathWorks).
No statistical methods were used to predetermine sample sizes and exact group
numbers were determined by animal availability. However, we did ensure that
our sample sizes were similar to those generally employed in the field. For
the comparison of averages between two groups (e.g., Fig. 1d), data were tested
for normality using Lilliefors test. If data for either group were significantly
nonnormal (with α = 0.05), then a nonparametric Mann-Whitney U test wasused (e.g., Supplementary Fig. 5b). If data from neither group were significantly nonnormal, then the groups were tested for equality of variance using
an F test. If the two groups had significantly different variances (with α = 0.05),
then Welch’s t-test was used; otherwise a standard two-sample t-test was used.
For the comparison of the average of a single group to a specified mean (e.g.,
Fig. 2b) the data were tested for normality using Lilliefors test. If the data weresignificantly nonnormal (with α = 0.05), then a sign-rank test was used; otherwise a t-test was used. For the comparison of averages between groups presented
with consistent or conflict platforms at different delays, two-way, fixed-effect
ANOVAs with interaction terms were used (e.g., Fig. 2d). Lilliefors test of theresiduals from the models confirmed that they were not significantly nonnormal.
Post hoc comparisons between individual groups were done using Tukey’s test
to control for multiple comparisons (e.g., Fig. 3e). Correlations were performed
using Pearson’s correlation coefficient. P values for individual correlations were
calculated using transformation to a Student’s t distribution, and z-tests were used
to calculate the significance of differences between correlations. All statisticaltests were two-tailed.
A Supplementary Methods checklist is available.

doi:10.1038/nn.3736

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