Cerebral Cortex, May 2019;29: 2183–2195
doi: 10.1093/cercor/bhz022
Advance Access Publication Date: 23 February 2019
Original Article

Learning Promotes Subﬁeld-Speciﬁc Synaptic Diversity
in Hippocampal CA1 Neurons
Y. Sakimoto1, J. Mizuno2, H. Kida1, Y. Kamiya4, Y. Ono5
and D. Mitsushima 1,2,3
1

Department of Physiology, Yamaguchi University Graduate School of Medicine, Yamaguchi 755-8505, Japan,
Department of Physiology and Neuroscience, Kanagawa Dental University, Kanagawa 238-8580, Japan, 3The
Research Institute for Time Studies, Yamaguchi University, Yamaguchi 753-8511, Japan, 4Uonuma Institute ofCommunity Medicine, Niigata University Medical Hospital, Niigata 951-8520, Japan and 5Department ofElectronics and Bioinformatics, Meiji University School of Science and Technology, Tokyo 101-8301, Japan
2

Address correspondence to Dai Mitsushima, Department of Physiology, Yamaguchi University School of Medicine, 1-1-1 Minami-Kogushi Ube,
Yamaguchi 755-8505, Japan. Email: mitsu@yamaguchi-u.ac.jp
orcid.org/0000-0002-6931-7714
Y. Sakimoto and J. Mizuno equally contributed each other

Abstract
The hippocampus is functionally heterogeneous between the dorsal and ventral subﬁelds with left–right asymmetry. To
determine the possible location of contextual memory, we performed an inhibitory avoidance task to analyze synaptic
plasticity using slice patch-clamp technique. The training bilaterally increased the AMPA/NMDA ratio at dorsal CA3–CA1
synapses, whereas the training did not affect the ratio at ventral CA3–CA1 synapses regardless of the hemisphere. Moreover,sequential recording of miniature excitatory postsynaptic currents and miniature inhibitory postsynaptic currents from thesame CA1 neuron clearly showed learning-induced synaptic plasticity. In dorsal CA1 neurons, the training dramaticallystrengthened both excitatory and inhibitory postsynaptic responses in both hemispheres, whereas the training did notpromote the plasticity in either hemisphere in ventral CA1 neurons. Nonstationary ﬂuctuation analysis further revealed
that the training bilaterally increased the number of AMPA or GABAA receptor channels at dorsal CA1 synapses, but not at
ventral CA1 synapses, suggesting functional heterogeneity of learning-induced receptor mobility. Finally, the performanceclearly impaired by the bilateral microinjection of plasticity blockers in dorsal, but not ventral CA1 subﬁelds, suggesting a
crucial role for contextual learning. The quantiﬁcation of synaptic diversity in speciﬁed CA1 subﬁelds may help us todiagnose and evaluate cognitive disorders at the information level.
Key words: AMPA receptor, contextual learning, GABAA receptor, self-entropy, synaptic plasticity

Introduction
The hippocampus is functionally heterogeneous between thedorsal and ventral subﬁelds (Fanselow and Dong 2010), withleft–right asymmetry (Shinohara et al. 2013). Dorsal subﬁelds
seem to serve cognitive functions, whereas ventral subﬁelds
correspond to the affective hippocampus (Moser and Moser

1998). Moreover, the acquisition of some hippocampaldependent tasks seems to require the left–right asymmetry of
the hippocampal circuit (Goto et al. 2010). Using an inhibitory
avoidance (IA) task with a hippocampus-dependent contextual
learning paradigm (Izquierdo et al. 1998), we previously foundthat contextual learning requires synaptic plasticity for both

© The Author(s) 2019. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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ORIGINAL ARTICLE

2184| Cerebral Cortex, 2019, Vol. 29, No. 5

Materials and Methods
Animals
Young male Sprague-Dawley rats (postnatal 28–31 days of age)
were used. After weaning, same sex groups of 2–3 rats werehoused in plastic cages (length 25 cm, width 40 cm, height
25 cm) at a constant temperature of 23 ± 1 °C under a constantcycle of light and dark (light on: 8:00 A.M. to 8:00 P.M.). But, therats were individually housed at least 24 h prior to the experiment to avoid any episodic experience. Food (MF, Oriental
Yeast Co. Ltd, Tokyo Japan) and tap water were available adlibitum in all experimental periods. All animal housing and surgical procedures followed the guidelines of the Institutional
Animal Care and Use Committee of Kanagawa Dental University
and Yamaguchi University. The guidelines comply with the
Guide for the Care and Use of Laboratory Animals published by

the National Institute of Health (NIH Publication No. 85-23,
revised 1996).

Inhibitory Avoidance Task
The IA training apparatus (length: 33 cm, width: 58 cm, height:
33 cm) was a 2-chambered box consisting of a lighted safe side
and a dark shock side separated by a trap door (Fig. 1A;
Mitsushima et al. 2011, 2013). For training, rats were placed in
the light side of the box facing a corner opposite the door. Afterthe trap door was opened, the rats could enter the dark box atwill. The latency before entering the novel dark box was measured as a behavioral parameter (latency before IA learning).
Soon after the animals entered the dark side, we closed thedoor and applied a scrambled electrical foot-shock (2 s, 1.6 mA)
via electriﬁed steel rods in the ﬂoor of the box. The rats were
kept in the dark compartment for 10 s before being returned totheir home cage. Untrained control rats were not moved fromtheir home cages.
Thirty minutes after the procedure described above, the rats
were placed in the light side. The latency before entering thedark box was measured as an indicator of learning performance (latency after IA learning).

Drug Injection
Under sodium pentobarbital anesthesia (30–50 mg/kg, i.p.), a
stainless-steel guide cannula (outer diameter, 0.51 mm) was
implanted stereotaxically into the just above the target regionof the dorsal or ventral hippocampus. The experiment was performed 1–3 days after the implantation. After cannula implantation, a stylet was inserted into the guide until drug injection.
On the day of the experiment, the stylet was replaced with
1.0 mm longer injector without restraint animals in their home
cage (outer diameter 0.31 mm). The coordinates of dorsal CA1
were 3.0 mm posterior to bregma, 2.0 mm lateral to the midline,
and 3.8 mm below the surface of the skull. The coordinates ofventral CA1 were 4.2 mm posterior to bregma, 5.5 mm lateral tothe midline, and 5.5 mm below the surface of the skull.
Approximately 20 min before the IA learning procedure,saline, NMDA receptor antagonist (3 μg/μL per side, d-AP5, Sigma
Chemical Co., St. Louis, MO), or nicotinic α7 receptor antagonist(40 μg/μL per side, methyllycaconitine citrate, Sigma) was directly
injected into the CA1 through ﬁne ﬂexible silicone tubing (o.d.
0.5 mm, Kaneka Medix Co. Osaka, Japan) without restraining theanimals. We used AP5 to block AMPA receptor-mediated plasticity at excitatory synapses (Morris et al. 1986; Park et al. 2004),
and also used Mla to block GABAA receptor-mediated plasticity

Figure 1. Inhibitory avoidance (IA) task. (A) IA training apparatus. (B) Foot-shock increased the latency to entering the dark box. The number of rats in each group is
shown at the bottom of each bar. Error bars indicate + standard error of the mean (SEM). **P < 0.01 versus before the training.

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excitatory and inhibitory inputs at CA1 synapses (Mitsushima
et al. 2011, 2013). However, there is no synaptic evidence toprove the location of encoded memory within a broad CA1
area.
First, we analyzed learning-induced synaptic plasticity in 4
CA1 subﬁelds to analyze the learning-induced synaptic plasticity. Second, we sequentially recorded miniature EPSCs(mEPSCs) and miniature IPSCs (mIPSCs) in the same neuron to
specify the CA1 subﬁeld of learning-created synaptic diversity.
Considering that each presynaptic vesicle contains approximately 2000 glutamate (Ryan et al. 1993; Hori and Takahashi2012) or 2500 GABA molecules (Telgkamp et al. 2004; Pugh andRaman 2005), the mE(I)PSC analysis allows for the quantiﬁcation of postsynaptic currents and plasticity. Nonstationary ﬂuctuation analysis further revealed subﬁeld-speciﬁc evidence oflearning at a single-channel level. Moreover, by analyzing theappearance probability of the synaptic strength in each neuron,
we proposed a new approach to quantify learning-induced synaptic diversity as self-entropy increases after the training. The
learning clearly increased the cell-speciﬁc self-entropy levels in
dorsal, but not ventral CA1 subﬁelds, and local blockade of
the synaptic plasticity blocked the learning in dorsal but notventral CA1 subﬁelds, suggesting contributory CA1 subﬁelds
at the information level. Since learning is known to modulate
both excitatory and inhibitory synaptic plasticity in key brain
areas such as hippocampus (Mitsushima et al. 2013), amygdala (Lin et al. 2011; Ganea et al. 2015), or cortical areas(Ghosh et al. 2015, 2016; Kida et al. 2016), this approach may
help us to diagnose and evaluate cognitive disorders in multiple brain regions.

Learning Promotes Synaptic Diversity in CA1

at inhibitory synapses. Mla is known to block acetylcholineinduced strengthen of GABAA receptor-mediated postsynaptic
current in CA1 pyramidal neurons (Mitsushima et al. 2013;
Townsend et al. 2016).

We recently published detailed technical protocol of slice-patch
clamp technique for analyzing learning-induced synaptic plasticity with a short demonstration movie (Kida et al. 2017). Using
the technique, we examined learning-induced synaptic plasticity in dorsal or ventral CA1 neurons.
One hour after the paired foot-shock, rats were anesthetizedwith pentobarbital and acute brain slices prepared (Mitsushima
et al. 2011, 2013). We used naïve rats for untrained group,
which were injected with the same dose of anesthesia in theirhome cage. The results in unpaired or walk-through controls
were reported previously (Mitsushima et al. 2013). For thewhole-cell recordings (Kida et al. 2016), the brains were quickly
perfused with ice-cold dissection buffer (25.0 mM NaHCO3,
1.25 mM NaH2PO4, 2.5 mM KCl, 0.5 mM CaCl2, 7.0 mM MgCl2,
25.0 mM glucose, 90 mM choline chloride, 11.6 mM ascorbic
acid, 3.1 mM pyruvic acid) and gassed with 5% CO2/95% O2.
Coronal (target CA1 area: AP −3.8 mm, DV 2.5 mm, LM ±2.0 mm) or horizontal brain slices (target CA1 area: AP −5.2 mm,
DV 7.7 mm, LM ± 5.8 mm) were cut (350 μm, Leica vibratome,
VT-1200) in dissection buffer and transferred to physiologicalsolution (22–25 °C, 114.6 mM NaCl, 2.5 mM KCl, 26 mM NaHCO3,
1 mM NaH2PO4, 10 mM glucose, 4 mM MgCl2, 4 mM CaCl2, pH
7.4, gassed with 5% CO2/95% O2). The recording chamber was
perfused with physiological solution containing 0.1 mM picrotoxin and 4 μM 2-chloroadenosine at 22–25 °C. For the mEPSC
and mIPSC recordings, we used the physiological solution containing 0.5 μM TTX to block Na+ channels.
Patch recording pipettes (4–7 MΩ) were ﬁlled with intracellular solution (127.5 mM cesium methanesulfonate, 7.5 mM CsCl,
10 mM Hepes, 2.5 mM MgCl2, 4 mM Na2ATP, 0.4 mM Na3GTP,
10 mM sodium phosphocreatine, 0.6 mM EGTA at pH 7.25).
Whole-cell recordings were obtained from CA1 pyramidal neurons from the rat hippocampus using an Axopatch 700 A ampliﬁer (Axon Instruments). The whole-cell patch-clamp data werecollected with Clampex 10.4, and the data were analyzed using
Clampﬁt 10.4 software (Axon Instruments).

| 2185cells. The electrically evoked EPSC amplitudes were measured
from the peak of the postsynaptic current to the basal current
level immediately before electrical stimulation. The stimulus
intensity was increased until a synaptic response with anamplitude >−10 pA was recorded. AMPA/NMDA ratios were calculated as the ratio of the peak current at −60 mV to the current
at +40 mV 150 ms after stimulus onset (40–60 traces averaged
for each holding potential).

Miniature Postsynaptic Current Recordings
Miniature excitatory postsynaptic currents (mEPSCs) are thought
to correspond to the responses elicited by the presynaptic releaseof a single vesicle of glutamate. In contrast, miniature inhibitory
postsynaptic currents (mIPSCs) are thought to correspond of
GABA. Increase in the amplitudes of mEPSCs and mIPSCs reﬂect
postsynaptic transmission strengthening, while increase in
the event frequency reﬂects increases in the number of functional synapses or the presynaptic release probability.
For the miniature recordings, the mEPSCs (−60 mV holding
potential) and mIPSCs (0 mV holding potential) were recorded
sequentially for 5 min in the same CA1 neuron. The miniature
events were detected using the software, and the events above
10 pA were used for the analysis. We recorded at least for 5 min
to determine the events frequency of mEPSCs or mIPSCs. The
amplitudes of the events were averaged to obtain the mean
amplitude. Bath application of an AMPA receptor blocker(CNQX, 10 μM) or GABAA receptor blocker (bicuculline methiodide, 10 μM) consistently blocked the mEPSC or mIPSC events,
respectively.

Nonstationary Fluctuation Analysis
AMPA receptor-mediated evoked EPSCs and GABAA receptormediated mIPSCs were analyzed by nonstationary ﬂuctuation
analysis (Ghosh et al. 2015; Ono et al. 2016). To isolate ﬂuctuations in current decay due to stochastic channel gating, themean waveform was scaled to the peak of individual E(I)PSCs.
The requirements for such analysis include a stable current
decay time course throughout the recording and an absence ofany correlation between the decay time course and peak amplitude. The relationship between the peak-scaled variance andthe mean current is given by the following equation:

σ 2 = iI − I 2 / N + bl

The AMPA/NMDA Ratio
The AMPA/NMDA ratio is conventional way to evaluate postsynaptic plasticity at glutamatergic excitatory synapses. Sinceconcomitant increases in both components may not change
the ratio, further analysis of AMPA evoked responses is necessary to elucidate the receptor-speciﬁc plasticity (i.e., I/O curvefor evoked EPSCs, amplitude of miniature AMPA receptormediated current, or further ﬂuctuation analysis of the current). The recording chamber was perfused with physiologicalsolution bubbled with the gas mixture and maintain the temperature at 22–25 °C. Then, we added 0.1 mM picrotoxin to thesolution to block the GABAA-mediated response. We also added
4 μM 2-chloroadenosine to stabilize the evoked neural response.
The patch recording pipettes were ﬁlled with the intracellularsolution for voltage-clamp recordings. The resistance of therecording pipette in the aCSF was between 4 and 7 MΩ.
To analyze the function of CA3–CA1 synapses, bipolar tungsten stimulating electrodes (Unique Medical Co., Ltd., Tokyo,
Japan) were placed in CA1 ~200–300 μm lateral from recorded

where σ2 is the variance, I is the mean current, N is the numberof channels activated at the peak of the mean current, i is theunitary conductance, and bl is the background variance. In our
experiments, 31–69 EPSCs and 14–133 IPSCs were analyzed
from selected epochs in each of the cells in which there was nocorrelation between current decay (63% decay time) and peak
amplitude (P > 0.05, Spearman’s rank-order correlation test).
The weighted mean channel current can be estimated by ﬁtting
the full parabola or initial slope of the relationship. All theanalysis was done using MATLAB software (MathWorks, MA,
USA). The number of channels was further divided by the corresponding value of mean E(I)PSC amplitude to obtain the single channel current.

Self-Entropy Analysis
We used standard spreadsheet software (Excel 2010, Microsoft
Co., Redmond, WA, USA) to calculate the self-entropy per neuron. First, we obtained 4 miniature parameters (mean mEPSC

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Electrophysiological Recordings

Sakimoto et al.

2186| Cerebral Cortex, 2019, Vol. 29, No. 5n⎛ x − Xi ⎞1⎟
Pn (x ) =∑ K⎜
nh i = 1 ⎝ h ⎠where K is a smooth function called the Gaussian kernel function and h > 0 is the smoothing bandwidth that controls theamount of smoothing. We chose Silverman’s reference bandwidth or Silverman’s rule of thumb (Silverman 1986; Sheather
2004). It is given by the following equation:h = 0.9An−1/5
where A = min (standard deviation, interquartile range/1.34).
By normalizing integral value in untrained controls, we foundthe distribution of appearance probability at any point. Then,
we calculated the appearance probability at selected points. Alldata points for probability in untrained and trained rats wereconverted to self-entropy (bit) using the Shannon entropy concept, deﬁned from the Information Theory (Shannon 1948).
To calculate using spreadsheet software, the data of 4 miniature parameters were summarized in 4 different sheet, andwe obtained the bandwidth (h) of individual parameter in
untrained group using a formula [=0.9 STDEV (neuron 1, neuron
2,,, neuron N)/COUNT (neuron 1, neuron 2,,, neuron N) ^ (1/5)].
Then, using the data of untrained group, we calculated the distribution of appearance probability as follows:• Probability distribution of ﬁrst data of a parameter (neuron 1)
was calculated using a formula [=EXP(−(((data of neuron 1 −any point)/h)^2/2))/SQRT (2 * PI())].
• Also, probability distribution of second data of the parameter(neuron 2) was calculated using the formula [=EXP(−(((data ofneuron 2 − any point)/h)^2/2))/SQRT(2 * PI())].
• Similarly, probability distribution of N data of the parameter(neuron N) was calculated using the formula [=EXP(−(((data ofneuron N − any point)/h)^2/2))/SQRT(2 * PI())].
• Sum all probability distribution from neuron 1 to N, and theintegral value was normalized to 1.
Based on the probability distribution, we calculated individual
appearance probability of all recorded neurons. Then, the appearance probability of the neuron was converted to the self-entropy
using Shannon’s formula (= −LOG [appearance probability of
the neuron, 2]) (Fig. 3F). For graphic expression, the distribution
was visualized 2-dimensionally in the R software environment(R Foundation for Statistical Computing, Vienna, Austria) (Figs 3C,
G,L,P and 4B,F,J,N).

Statistical Analysis
We used the paired t test to analyze IA latency and unpaired t
test to analyze estimated open channel numbers. The AMPA/NMDA ratio, mEPSC, mIPSC, and self-entropy were analyzed
using 2-way factorial ANOVA in which the between-group factors
were laterality and training. We used one-way factorial ANOVA

to evaluate the difference in miniature responses between dorsal
and ventral synapses. The Shapiro–Wilk test and F-test were used
for normality and equality of variance, respectively. Because theself-entropy data had large variations within a group, we performed log (1 + x) transformation prior to the analysis(Mitsushima et al. 1994). P < 0.05 was considered signiﬁcant.

Results
Inhibitory Avoidance Task
To investigate a possible location of the contextual memory in
4 CA1 subﬁelds, rats were subjected to an IA task (Fig. 1A;
Izquierdo et al. 1998; Mitsushima et al. 2011, 2013). In this learning paradigm, rats were allowed to cross from a light box to adark box, where an electric foot-shock (1.6 mA, 2 s) was delivered. Half an hour after the IA task, we measured the latency inthe illuminated box as contextual learning performance. Withpaired foot-shock, the latency was much longer after training
than before the training (t11 = 14.0, P < 0.0001).

AMPA/NMDA Ratio
To specify the subﬁelds where the contextual learning drives
AMPA receptors into CA3–CA1 synapses, we measured the
AMPA- to NMDA-type glutamate receptor response ratio in thedorsal or ventral hippocampus of both hemispheres.
Figure 2A shows experimental design and the recording
location of dorsal CA1 neurons. At dorsal CA3–CA1 synapses,
2-way ANOVA revealed a signiﬁcant main effect of training(F1,44 = 12.637, P = 0.0009), but the main effect of laterality(F1,44 = 0.007, P = 0.93) or interaction (F1,44 = 0.156, P = 0.70) was
not signiﬁcant (Fig. 2B,C). Figure 2D shows experimental design
and the recording location of ventral CA1 neurons. At ventral
CA3–CA1 synapses, the main effects of training (F1,39 = 0.960,
P = 0.22), laterality (F1,39 = 1.641, P = 0.21), and interaction(F1,39 = 0.022, P = 0.88) were not signiﬁcant (Fig. 2E,F). These
results suggest that the training bilaterally strengthened AMPA
receptor-mediated CA3–CA1 synapses in dorsal CA1 neurons,
regardless of the hemisphere, but not in ventral CA1 neurons.

Miniature Postsynaptic Currents in Dorsal CA1 Neurons
To further analyze the learning-dependent synaptic plasticity, werecorded mEPSC or mIPSC in the presence of 0.5 μM TTX on bothsides of the dorsal hippocampus (Fig. 3A). By changing the membrane potential, we sequentially recorded mEPSCs (at −60 mV) andmIPSCs (at 0 mV) from the same neuron as reported previously(Mitsushima et al. 2013). The postsynaptic currents are thought tocorrespond to the response elicited by a single vesicle of glutamate
or GABA. In contrast, the number of synapses is known to affectthe frequency of the events (Pinheiro and Mulle 2008).
At dorsal CA1 synapses, the strength of AMPA receptormediated excitatory inputs versus GABAA receptor-mediatedinhibitory inputs was measured in each neuron and plotted
2-dimensionally (Fig. 3B). The Kernel analysis revealed the
distribution of appearance probability (Fig. 3C). Althoughuntrained rats exhibited low and narrow distribution range,
IA-trained rats had a broad distribution suggesting a diversity ofsynaptic input onto CA1 neurons. For mEPSCs, 2-way ANOVA
revealed a signiﬁcant main effect of training (F1,104 = 18.780, P <0.0001), but the main effect of laterality (F1,104 = 2.237, P = 0.14) orinteraction (F1,104 = 0.998, P = 0.32) was not signiﬁcant (Fig. 3D).
For mIPSCs, 2-way ANOVA revealed a signiﬁcant main effect oftraining (F1,104 = 44.627, P < 0.0001), but the main effect ofDownloaded from https://academic.oup.com/cercor/article-abstract/29/5/2183/5364044 by Yamaguchi Univ. - Medical Library user on 07 November 2019

amplitude, mean mIPSC amplitude, mean mEPSC frequency,
and mean mIPSC frequency) in individual CA1 pyramidal neurons. Then, we determined the distribution of appearance
probability of 4 miniature parameters separately using 1dimensional Kernel density analysis. Geometric/topographic
feature of the appearance probability was calculated using
Kernel density analysis. Let X1, X2,…, Xn denote a sample ofsize n from real observations. The Kernel density estimate of P
at the point x is given by the following equations:Learning Promotes Synaptic Diversity in CA1

Sakimoto et al.

| 2187

Experimental design and horizontal section of ventral CA1 for patch-clamp analysis. (E) The AMPA/NMDA ratio and distribution in ventral left and (F) right CA1 neurons. IA training did not affect the ratio of ventral CA3–CA1 synapses. Green squares indicate the recorded CA1 subﬁelds in the dorsal and ventral hippocampus.
Upper insets show representative traces. The number of cells in each group is shown at the bottom of each bar. Vertical bar = 40 pA; horizontal bar = 50 ms. Error bars
indicate + SEM. *P < 0.05, **P < 0.01 versus untrained.

laterality (F1,104 = 0.724, P = 0.40) or interaction (F1,104 = 0.299, P =0.59) was not signiﬁcant (Fig. 3E). These results suggest that thetraining bilaterally strengthened both excitatory and inhibitory
synapses onto dorsal CA1 neurons, regardless of the hemisphere.
The balance of excitatory/inhibitory (E/I) inputs was obtainedby dividing the mean mEPSC amplitude by the mean mIPSC
amplitude of the same neuron. For the E/I balance of miniatureamplitudes, the main effect of training (F1,104 = 0.203, P = 0.65),
laterality (F1,104 = 2.469, P = 0.12), or interaction (F1,104 = 0.002,

P = 0.96) was not signiﬁcant (Fig. 3H). Thus, the training did
not affect the balance of mEPSC versus mIPSC amplitudes,
suggesting the balance of excitatory versus inhibitory input
strength onto dorsal CA1 neurons.

Self-Entropy in Dorsal CA1 Neurons
Based on the information theory of Shannon (1948), we calculated appearance probability of the mean amplitudes of

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Figure 2. AMPA/NMDA ratio in 4 CA1 subﬁelds. (A) Experimental design and coronal section of dorsal CA1 for patch-clamp analysis. (B) The AMPA/NMDA ratio andcumulative distribution in dorsal left and (C) right CA1 neurons. The trained rats had signiﬁcantly higher ratios at dorsal CA3–CA1 synapses than untrained rats. (D)2188| Cerebral Cortex, 2019, Vol. 29, No. 5tion of appearance probability at any point. (D) Mean amplitudes of mEPSCs and (E) mIPSCs in dorsal CA1 neurons. IA training signiﬁcantly increased both amplitudes inboth hemispheres. (F) The self-entropy of each dot and (G) visualized density in dorsal CA1 neurons. (H) E/I balance of miniature amplitudes and (I) mean self-entropy
per dorsal CA1 neuron. (J) Representative traces in ventral left and right CA1 neurons. (K) 2-Dimensional plot of the mean mEPSC and mIPSC amplitudes in ventral CA1
neurons and (L) and visualized distribution of appearance probability at any point. (M) Mean amplitudes of the mEPSCs and (N) mIPSCs in ventral CA1 neurons. IA training affected neither of them, regardless of the hemisphere. (O) The self-entropy of each dot and (P) visualized density in ventral CA1 neurons. (Q) E/I balance of miniatureamplitudes and (R) mean self-entropy per ventral CA1 neuron. E = mEPSC; I = mIPSC, vertical bar = 20 pA; horizontal bar = 200 ms. Gray indicates untrained groups andblack is trained groups. The number of cells in each group is shown at the bottom of each bar. Error bars indicate ± SEM. *P < 0.05, **P < 0.01 versus untrained.

mEPSCs and mIPSCs. First, we found the distribution of
appearance probability in untrained controls (Fig 3C, left),
and then we analyzed the appearance probability of allrecorded neurons one-by-one. Each probability of single

neuron was calculated as the self-entropy and plotted
2-dimensionally (Fig. 3F). For example, a point with highappearance probability (around the mean level of mE(I)PSC
amplitude) indicates low self-entropy, whereas a point with

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Figure 3. Diversity of mEPSC/mIPSC amplitudes and self-entropy per neuron after training. (A) Representative traces of mEPSCs and mIPSCs in dorsal left and right CA1
neurons. mEPSCs and mIPSCs were sequentially recorded from the same CA1 neuron in the presence of tetrodotoxin (0.5 μM). (B) 2-Dimensional plot of the amplitudesof mean mEPSC and mIPSC. The circle or square plot indicates the data from a right or left CA1 neuron, respectively. (C) Kernel density analysis visualized the distribu-

Learning Promotes Synaptic Diversity in CA1

Miniature Postsynaptic Currents in Ventral CA1
Neurons
Conversely in ventral CA1 neurons, IA training did not affectthe miniature responses (Fig. 3K). For mEPSCs, the main effectsof training (F1,103 = 0.002, P = 0.96), laterality (F1,103 = 0.0002, P =0.99), and interaction (F1,103 = 1.748, P = 0.19) were not signiﬁcant (Fig. 3M). For mIPSCs, the main effects of training (F1,103 =0.052, P = 0.82), laterality (F1,103 = 0.833, P = 0.36), and interaction (F1,103 = 1.025, P = 0.31) were not signiﬁcant (Fig. 3N). The
Kernel analysis visualized the distribution of appearance probability (Fig. 3L). Thus, the training did not affect the amplitudes
in either hemisphere. These results suggest that the training
strengthened neither excitatory nor inhibitory synapses onto
ventral CA1 neurons, regardless of the hemisphere.
For the E/I balance of miniature amplitudes, the main effects oftraining (F1,103 = 0.244, P = 0.62), laterality (F1,103 = 0.069, P = 0.79),
and interaction (F1,103 = 2.287, P = 0.13) were not signiﬁcant(Fig. 3Q). The training did not affect the balance of mEPSC versus
mIPSC amplitudes, suggesting the balance of excitatory versus
inhibitory input strength onto ventral CA1 neurons.

Self-Entropy in Ventral CA1 Neurons
Using the distribution of appearance probability in untrained
controls (Fig 3L, left), we calculated the self-entropy of allrecorded neurons one-by-one (Fig. 3O). We found all recorded
neurons exhibited different self-entropy each other. In ventral
CA1 neurons, self-entropy in the excitatory synapse did notexhibit a signiﬁcant main effect of training (F1,103 = 0.001, P =0.97), laterality (F1,103 = 0.356, P = 0.55), or interaction (F1,103 =0.930, P = 0.34). Similarly, self-entropy in the inhibitory synapse
did not exhibit a signiﬁcant main effect of training (F1,103 =1.284, P = 0.26), laterality (F1,103 = 1.158, P = 0.28), or interaction(F1,103 = 2.023, P = 0.16). Thus, the training did not affect the
self-entropy in either hemisphere, and the visualized density
distribution was shown in Figure 3P. The average levels of selfentropy were 15.0 ± 0.2 bits (untrained) and 14.9 ± 0.3 bits (IAtrained) per single CA1 neuron (Fig. 3R).

Frequencies of the mE(I)PSC Events in Dorsal CA1
Neurons
The number of functional synapses is known to affect the frequency of the mEPSC/mIPSC events. At dorsal CA1 synapses,| 2189the frequency of mEPSC versus mIPSC events was measured ineach neuron and plotted 2-dimensionally (Fig. 4A). The Kernel
analysis revealed the distribution of appearance probability(Fig. 4B). Although untrained rats exhibited low and narrow distribution range, IA-trained rats had a broad distribution suggesting a diversity of the number of functional synapses onto asingle CA1 neuron. For mEPSCs, 2-way ANOVA revealed a signiﬁcant main effect of training (F1,104 = 6.942, P = 0.0097), but
the main effect of laterality (F1,104 = 0.023, P = 0.88) or interaction (F1,104 = 0.035, P = 0.85) was not signiﬁcant (Fig. 4C). For
mIPSCs, 2-way ANOVA revealed a signiﬁcant main effect oftraining (F1,104 = 13.893, P = 0.0003), but the main effect of laterality (F1,104 = 1.760, P = 0.19) or interaction (F1,104 = 0.054, P =0.82) was not signiﬁcant (Fig. 4D). These results suggest that thetraining increased in the number of excitatory and inhibitory
synapses onto dorsal CA1 neurons in both hemispheres.
The balance of excitatory/inhibitory (E/I) frequency was
obtained by dividing the mean mEPSC frequency by the meanmIPSC frequency of the same neuron. For the E/I balance of miniature frequency, the main effect of training (F1,104 = 0.198, P =0.66), laterality (F1,104 = 0.149, P = 0.70), or interaction (F1,104 =
0.78, P = 0.38) was not signiﬁcant (Fig. 4G). Thus, the training did
not affect the balance of mEPSC versus mIPSC frequency, suggesting the balance of the number of excitatory versus inhibitory
synapses onto dorsal CA1 neurons.

Self-Entropy of the Frequency in Dorsal CA1 Neurons
Using the distribution appearance probability in untrained controls (Fig. 4B, left), we analyzed the appearance probability at
selected points. The probabilities at all data points were calculated as the self-entropy and plotted 2-dimensionally (Fig. 4E).
Although all recorded neurons exhibited different self-entropy
each other, the Kernel analysis further visualized the density
distribution (Fig. 4F). IA training dramatically increased theamount of information per dorsal CA1 neurons (Fig. 4H).
Self-entropy in the mEPSC frequency exhibited a signiﬁcantmain effect of training (F1,104 = 7.944, P = 0.0058), but the main
effect of laterality (F1,104 = 0.019, P = 0.89) or interaction (F1,104 =0.067, P = 0.80) was not signiﬁcant (Fig. 4E). Similarly, selfentropy in the mIPSC frequency exhibited a signiﬁcant main
effect of training (F1,104 = 6.753, P = 0.0107), but the main effectof laterality (F1,104 = 0.061, P = 0.81) or interaction (F1,104 = 0.001,
P = 0.97) was not signiﬁcant (Fig. 4E). Thus, the training clearly
increased the self-entropy of dorsal CA1 neurons in both hemispheres, and the Kernel analysis further visualized the density
distribution (Fig. 4F). The average level was 11.5 ± 0.2 bits in
untrained rats, whereas the trained rats showed 54.5 ± 21.4 bits
per single CA1 neuron (Fig. 4H).

Frequencies of the mE(I)PSC Events in Ventral CA1
Neurons
IA training did not affect the frequency in ventral CA1 neurons. The frequency of mEPSC versus mIPSC events was measured in each neuron and plotted 2-dimensionally (Fig. 4I).
Ventral CA1 neurons exhibited relatively wide distribution
range in both untrained and IA-trained rats, and the Kernel
analysis showed the distribution of appearance probability(Fig. 4J). For mEPSCs, the main effects of training (F1,103 =0.017, P = 0.90), laterality (F1,103 = 2.144, P = 0.15), and interaction (F1,103 = 0.22, P = 0.64) were not signiﬁcant (Fig. 4K). For
mIPSCs, the main effects of training (F1,103 = 0.866, P = 0.35),

Downloaded from https://academic.oup.com/cercor/article-abstract/29/5/2183/5364044 by Yamaguchi Univ. - Medical Library user on 07 November 2019very rare probability (a deviated point of mE(I)PSC amplitude) indicates high self-entropy.
We found that all recorded neurons exhibited different selfentropy each other (Fig. 3F). In the dorsal CA1, self-entropy inthe excitatory synapse exhibited a signiﬁcant main effect oftraining (F1,104 = 9.322, P = 0.0029), but the main effect of laterality (F1,104 = 1.229, P = 0.27) or interaction (F1,104 = 0.879, P = 0.35)
was not signiﬁcant (Fig. 3F). Similarly, self-entropy in the inhibitory synapse exhibited a signiﬁcant main effect of training(F1,104 = 21.393, P < 0.0001), but the main effect of laterality(F1,104 = 1.205, P = 0.27) or interaction (F1,104 = 0.007, P = 0.93)
was not signiﬁcant (Fig. 3F). The Kernel analysis further visualized the density distribution (Fig. 3G). Thus, the training clearly
increased the self-entropy of dorsal CA1 neurons in both hemispheres. The average level was 13.4 ± 0.2 bits in untrained rats,
whereas IA-trained rats showed 30.3 ± 8.0 bits per single CA1
neuron (Fig. 3I).

Sakimoto et al.

2190| Cerebral Cortex, 2019, Vol. 29, No. 5

neurons. The circle or square plot indicates the data from a right or left CA1 neuron, respectively. (B) Kernel density analysis visualized the distribution of appearance probability. (C) Mean frequencies of mEPSCs and (D) mIPSCs in dorsal CA1 neurons. IA training signiﬁcantly increased both frequencies in both hemispheres. (E) The selfentropy of each dot and (F) visualized density in dorsal CA1 neurons. (G) E/I balance of miniature frequencies and (H) mean self-entropy per dorsal CA1 neuron. (I) 2Dimensional plot of the self-entropy of the frequencies in ventral CA1 neurons. The circle or square plot indicates the data from right or left CA1 neuron, respectively. (J)Kernel density analysis visualized the distribution of appearance probability. (K) Mean frequencies of mEPSCs and (L) mIPSCs in ventral CA1 neurons. Right side exhibited
signiﬁcantly wider variation of mEPSC frequency than left side. IA training affected neither of them, regardless of the hemisphere. (M) The self-entropy of each dot and (N)visualized density in ventral CA1 neurons. (O) E/I balance of miniature frequencies and (P) mean self-entropy per ventral CA1 neuron. E = mEPSC; I = mIPSC. Gray indicates
untrained groups and black is trained groups. The number of cells in each group is shown at the bottom of each bar. Error bars indicate ± SEM. *P < 0.05, **P < 0.01 versus
untrained.

laterality (F1,103 = 0.922, P = 0.34), and interaction (F1,103 =0.273, P = 0.60) were not signiﬁcant (Fig. 4L). Thus, the training affected frequency of neither mEPSC nor mIPSC regardless of the hemispheres. These results suggest that thetraining did not affect the number of excitatory and inhibitory synapses onto ventral CA1 neurons, regardless of thehemisphere.
For the E/I balance of miniature frequency, the main effectsof training (F1,103 = 0.75, P = 0.39) and interaction (F1,103 =0.086, P = 0.77) were not signiﬁcant (Fig. 4O), but right side of
CA1 neurons exhibited higher E/I balance of the frequency
than left side (F1,103 = 4.865, P = 0.03). The results suggest that
CA1 neurons receive more excitatory inputs in the right hemisphere than in the left hemisphere, providing a synaptic evidence of laterality. Meanwhile, the training did not affect thebalance of mEPSC versus mIPSC frequency, suggesting the balance of the number of excitatory versus inhibitory synapses
onto ventral CA1 neurons.

Self-Entropy of the Frequency in Ventral CA1 Neurons
Although the recorded neurons exhibited different self-entropy
each other, self-entropy in the mEPSC frequency did not exhibit
a signiﬁcant main effect of training (F1,103 = 0.055, P = 0.82),
laterality (F1,103 = 2.838, P = 0.095), or interaction (F1,103 = 0.147,
P = 0.70, Fig. 4M). Similarly, self-entropy in the mIPSC frequency
did not exhibit a signiﬁcant main effect of training (F1,103 =0.519, P = 0.47), laterality (F1,103 = 0.538, P = 0.47), or interaction(F1,103 = 0.342, P = 0.56, Fig. 4M). Thus, the training affected neither self-entropy regardless of the hemispheres. The Kernel

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Figure 4. Diversity of mEPSC/mIPSC frequency and self-entropy per neuron after training. (A) 2-Dimensional plot of the frequency of mEPSCs and mIPSCs in dorsal CA1

Learning Promotes Synaptic Diversity in CA1

Sakimoto et al.

| 2191(C) Ventral synapses exhibited greater single-channel current than dorsal synapses, although the training did not affect the current. (D) An example of nonstationary
ﬂuctuation analysis for GABAA receptor current. (E) Mean number of open Cl− channels at dorsal and ventral CA1 synapses. IA training signiﬁcantly increased thenumber of open channels only at dorsal synapses. Ventral GABAergic synapses possessed more channels than dorsal synapses. (F) Neither training nor CA1 region
affected the single-channel current. The number of cells in each group is shown at the bottom of each bar. Error bars indicate + SEM. *P < 0.05 versus untrained. #P <0.05 versus dorsal.

analysis further visualized the density distribution (Fig. 4N).
The average levels of self-entropy were 16.8 ± 2.7 bits
(untrained) and 16.3 ± 1.7 bits (IA trained) per single CA1 neuron (Fig. 4P).

The Number of Postsynaptic AMPA Receptor Channels
To examine whether IA alters the number of AMPA receptors,
we used evoked EPSC responses to calculate the number of
opening AMPA receptors at dorsal or ventral CA3–CA1 synapses
using nonstationary ﬂuctuation analysis (Fig. 5A). The numberof open AMPA receptors was signiﬁcantly larger in IA-trained
rats than untrained rats at dorsal CA3–CA1 synapses (t33 = 2.28,
P = 0.029), but not at ventral CA3–CA1 synapses (t18 = 0.32, P =0.75, Fig. 5B). Although the single-channel current was signiﬁcantly greater at ventral synapses than dorsal synapses (F1,53 =
6.02, P = 0.017), the training did not affect the single-channel
current (Fig. 5C). These results suggest that the training promotes postsynaptic plasticity by increasing the number of
AMPA receptor-channels at dorsal but not ventral CA3–CA1
synapses.

The Number of Postsynaptic GABAA Receptor Channels
To examine whether IA alters the number of GABAA receptors,
we used mIPSC responses to calculate the number of opening
GABAA receptors at dorsal or ventral CA1 synapses (Fig. 5D).
The number of open GABAA receptors was larger in IA-trained
rats than untrained rats at dorsal CA1 synapses (t96 = 2.30, P =0.024), but not at ventral CA1 synapses (t81 = 0.44, P = 0.66,
Fig. 5E). Although ventral synapses possessed more Cl− channels than dorsal synapses (F1,179 = 6.532, P = 0.011), neither area

nor the training affected the single-channel current (Fig. 5F).
These results suggest that the training promotes postsynaptic
plasticity by increasing the number of GABAA receptorchannels at dorsal but not ventral CA1 synapses.

Bilateral Microinjection of Plasticity Blockers
To examine the physiological role of plastic changes affecting
the IA learning, saline, AP5, or Mla was bilaterally microinjected
into the dorsal or ventral CA1 (Fig. 6A). In the dorsal CA1, bothAP5 and Mla treated-rats showed shorter latency than salineinjected rats after the training (Fig. 6B, F2,16 = 16.411, P < 0.0001).
Conversely in the ventral CA1, neither AP5 nor Mla treatment
affected the latency (Fig. 6C, F2,15 = 0.773, P = 0.48).

Discussion
Rat hippocampus is known to contain approximately 311 500
CA1 pyramidal neurons, receiving 13 059-28 697 CA3–CA1 synapses and up to 1 742 temporoammonic synapses from entorhinal cortex per single neuron (Bezaire and Soltesz 2013).
Although both structural and functional heterogeneity are
known in dorso/ventral or left /right CA1 neurons, the locationof learning-induced synaptic plasticity has not been speciﬁedin the broad CA1 area. Here we found that the training
increased AMPA receptor-mediated responses at dorsal CA3–
CA1 synapses in both hemispheres, whereas ventral CA3–CA1
synapses did not show the plasticity in either hemisphere. The
speciﬁed CA1 subﬁelds of learning-induced plasticity provide a
synaptic evidence of dorso/ventral heterogeneity at the synapse level.

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Figure 5. Estimated number of open channels and single channel current. (A) An example of nonstationary ﬂuctuation analysis for AMPA receptor current. (B) Mean
number of open Na+ channels at dorsal and ventral CA1 synapses. IA training signiﬁcantly increased the number of open channels only at dorsal synapses.

2192| Cerebral Cortex, 2019, Vol. 29, No. 5

of AP5 or methyllycaconitine (Mla) into the dorsal CA1 impaired learning. (C) The injection into the ventral CA1 did not affect the performance. The number of rats ineach group is shown at the bottom of each bar. Vertical gray and black bars indicate guide cannula and injector. The number indicates posterior coordinate frombregma. Error bars indicate + SEM. **P < 0.01 versus saline (Sal).

Nonstationary ﬂuctuation analysis further revealed evidence at a single channel level. We found the training signiﬁcantly increased the postsynaptic number of open AMPA
receptors at dorsal CA1 synapses, whereas the training did notaffect the ventral CA1 synapses (Fig. 5B). By combining in vivo
gene delivery and in vitro patch-clamp recordings, we previously demonstrated that contextual learning depends on synaptic delivery of GluA1-containing AMPA receptors at dorsal
CA1 synapses at the molecular level (Mitsushima et al. 2011).
The increase in the number of open channels without changes
in the single-channel current further revealed learning-induced
current changes at the AMPA receptor-mediated CA1 synapses.
The dorsal and ventral hippocampus play different roles
based on distinct input and output connections (Swanson and
Cowan 1977). Spatial and contextual memory appears to
depend only on the dorsal hippocampus (Moser and Moser
1998; Strange et al. 2014), whereas ventral hippocampal lesions
alter stress responses and emotional behavior (Henke 1990;
Kjelstrup et al. 2002). In untrained rats, ventral CA1 neurons
tended to show greater mEPSC frequency (mEPSC, F1,98 = 3.241,
P = 0.075) and exhibited greater mIPSC frequency than dorsal
neurons (mIPSC, F1,98 = 40.120, P < 0.0001; Fig. 4). This heterogeneity was already reported Millior et al. (2016), showing greater
basal releases of both GABA and glutamate at ventral CA1 synapses. Moreover, as to the postsynaptic heterogeneity, ventral
neurons showed greater mE(I)PSC amplitude than dorsal neurons (mEPSC, F1,98 = 11.324, P = 0.0011; mIPSC, F1,98 = 11.795, P =0.0009; Fig. 3), suggesting greater postsynaptic AMPA/GABAA
current at ventral CA1 synapses in untrained rats. Finally, thegreater single current of AMPA receptor (Fig. 5C) and more postsynaptic GABAA channels (Fig. 5E) at ventral CA1 synapses may
provide further evidence of dorso/ventral heterogeneity at thesynapses.
Although laterality was not clear in our laboratory conditions,right side of CA1 exhibited more power of gamma oscillationand spine density than left side in rats reared in the spatiallyenriched conditions (Shinohara et al. 2013). In humans, patientswith unilateral damage to the right hippocampus exhibit spatial
memory deﬁcits (Abrahams et al. 1996), whereas damage tothe left hippocampus impairs verbal semantic representation(Richardson et al. 2004). In the present study, the right sides ofsynapses tended to have greater AMPA/NMDA ratios, mEPSC
amplitudes, and self-entropy than the left side after training,
though the laterality was not signiﬁcant. Fibers through the ventral hippocampal commissure are known to connect bilateral
CA1 (Amaral and Witter 1995; Kawakami et al. 2003), inducing
high coherence of CA1 theta waves during running or REM sleepin the freely moving state (Patel et al. 2012). As unilateral CA1
blockade of AMPA receptor delivery (Mitsushima et al. 2011,2013) fail to impair the IA learning, bilateral CA1 neurons may
work together to compensate for impairment of the other in rats
in normal laboratory conditions.
The question arises as to whether the synaptic strength
contributes to memory. In regards to the excitatory synapses,

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Figure 6. Intra-CA1 injection of plasticity blockers impairs the IA learning. (A) Experimental design of bilateral intra-CA1 injection and IA training. (B) Microinjection

Learning Promotes Synaptic Diversity in CA1| 2193

Synapses regulate cell ﬁring according to the all-or-none
principle (Kandel et al. 2013). If a neuron is considered an allor-none device (Cannon 1922; Wiener 1961), one neuron canhandle 1-bit of memory per clock cycle (log2 2 = 1 bit; Hartley
1928). Based on the principle, computational theory proposeda role of the hippocampus as a kind of memory device (Marr
1977). We previously found a logarithmic relationship between
the number of cells blocking plasticity and learning performance (Mitsushima et al. 2011), providing an evidence ofbinary processing of contextual information, such as what,
where, or when. We hypothesized that the plasticity at excitatory/inhibitory synapses may change the binary processingof ﬁring to encode memory. In support of this, real-time
recordings of multiple unit activity of dorsal CA1 neurons further showed task-dependent diversiﬁed feature of ripple-likeon/off ﬁrings (Ishikawa and Mitsushima 2016; Tomokage et al.
2018). Since selective elimination of the ripple-like events during post-training consolidation periods impairs the performance in dorsal hippocampus-dependent task (Girardeau
et al. 2009), the events may contribute to code experienced
information.
Synaptic dysfunction is well correlated with cognitive
decline in Alzheimer’s disease (Terry et al. 1991). Amyloid beta(Aβ42) is well known as a major causative agent (Shankar et al.
2008; Querfurth and LaFerla 2010; Penzes et al. 2011; Sevigny
et al. 2016), and long-term exposure to Aβ42 impairs the AMPA
receptor trafﬁcking by reducing synaptic distribution of CaMKII
in cultured pyramidal neurons (Gu et al. 2009). As regard target
molecule, hippocampal neurons that lack GluA3 were resistantagainst Aβ-mediated synaptic depression and spine loss, suggesting that Aβ initiates synaptic and memory deﬁcits byremoving GluA3-containing AMPA receptors from synapses(Reinders et al. 2016). On the other hand, as to the inhibitory
synapses, Aβ42 speciﬁcally binds to nicotinic α7 receptors (Wanget al. 2000) promoting the learning-induced plasticity at the
GABAA receptor-mediated inhibitory synapses (Mitsushima
et al. 2013; Townsend et al. 2016). Application of Aβ42, being
known to block the nicotinic α7 receptor-mediated cholinergic
response (Liu et al. 2001), quickly weakens GABAA receptormediated synaptic currents via downregulation of GABAA
receptors (Ulrich 2015). Understanding the learning-induced
plasticity in speciﬁc CA1 subﬁelds as well as the quantiﬁcationof synaptic diversity is necessary to diagnose the functional
impairment in cognitive disorders, which may help to identify
potential targets for therapeutic intervention.

Authors’ Contributions
D.M., Y.S., and J.M. wrote the article. Y.S., J.M., and D.M. performed the experiments. Y.O. developed the program for nonstationary ﬂuctuation analysis. D.M., H.K., and Y.K. developed
the patch-clamp system. D.M. designed the study and analyzed
data for both statistics and self-entropy.

Funding
Grants-in-Aid for Scientiﬁc Research B (D.M.), Scientiﬁc Research C(J.M., Y.S., D.M.), and Scientiﬁc Research in Innovative Areas (D.M.),
from the Ministry of Education, Culture, Sports, Science, andTechnology of Japan. Grants-in-Aid from Yokohama Foundation
for Advancement of Medical Science (D.M.), and the Yokohama
Academic Foundation (D.M.) also supported the project.

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contextual learning requires AMPA receptor delivery, as bilateral CA1 blockade of AMPA receptor delivery impairs learning(Mitsushima et al. 2011). Recently, Takemoto et al. further
developed a technique to inactivate synaptic GluA1 AMPA
receptors in vivo using chromophore-assisted light inactivation(CALI). Since optical inactivation of synaptic AMPA receptors
successfully erased acquired-fear memory (Takemoto et al.
2017), newly delivered GluA1-containing AMPA receptors into
the CA1 synapses seems to be required for contextual memory
formation.
Learning also affects the GABAA receptor-mediated inhibitory
synapses, but the plasticity seems to be task-dependent at CA1
synapses (Cui et al. 2008; Mitsushima et al. 2013). Spatial learningpresynaptically increases GABA release probability (Cui et al.
2008), whereas contextual learning postsynaptically strengthened
GABAA receptor-mediated inhibitory synapses (Mitsushima et al.
2013). Since optogenetic inactivation of somatostain-expressing
interneurons in bilateral dorsal CA1 impaired the contextual fear
learning, the learning seems to require the GABAergic inputs
from somatostain-expressing interneurons at basal dendrites of
dorsal CA1 neurons (Lovett-Barron et al. 2014). We further founda rapid phosphorylation of Ser408–409 GABAA receptor β3 subunit
within 5 min after the training (Sakimoto et al. 2017). Since theSer408–409 phosphorylation is known to prevent clathrin adaptor
protein 2-mediated GABAA receptor internalization (Jovanovic
et al. 2004; Lu et al. 2010; Petrini and Barberis 2014), the Ser408–409
phosphorylation may contribute to increase the number of
GABAA receptor channels (Fig. 5E) without changing Cl− current
per channel (Fig. 5F).
Sequential recording of mEPSCs and mIPSCs enables analysis of the strength of excitatory and inhibitory inputs in eachneuron one-by-one. The increase in amplitude may indicate
additional receptor delivery into the synapses, whereas theincrease in frequency could result in an increase in the numberof functional synapses or the probability of vesicle release fromthe presynaptic terminal (Kerchner and Nicoll 2008; Mitsushima
et al. 2013). IA training clearly increased both mE(I)PSC amplitudes and frequencies in both hemispheres of dorsal CA1 neurons, whereas the training increased neither mE(I)PSC amplitudes
nor frequencies in ventral CA1 neurons, regardless of the hemisphere. Moreover, the performance clearly impaired by the bilateral blockade of the plasticity in dorsal, but not ventral CA1
subﬁelds, suggesting a speciﬁc role of the dorsal CA1 synapses
for contextual learning (Fig. 6). Although the IA training failed
to affect the synaptic plasticity in either hemisphere of ventral
CA1 neurons, optogenetic cell body inhibition study revealed
essential role of ventral CA1 neurons for social discrimination
task (Okuyama et al. 2016).
Here we propose a new approach to quantify the learninginduced synaptic diversity in 4 CA1 subﬁelds. The subﬁeldspeciﬁc increase in self-entropy at dorsal, but not ventral, CA1
synapses in trained rats further provides an evidence of learning. Since bilateral blockade of the synaptic diversity clearly
impaired the learning performance (Mitsushima et al. 2013,
Ono and Mitsushima 2017), we hypothesized that the increased
self-entropy may code a piece of experienced information after
training. Present results not only conﬁrmed previous ﬁndings,
but also speciﬁed the subregion, suggesting a crucial role for IA
learning (Fig. 6). Considering the total number of dorsal CA1
neurons (Andersen et al. 2006; Bezaire and Soltesz 2013), a possible increase in total self-entropy after IA training is estimatedto be 12 550 000 bits. In any case, the quantiﬁcation of the synaptic diversity in speciﬁc CA1 subﬁelds would be a usefulapproach for diagnostic evaluation of cognitive disorders.

Sakimoto et al.

2194| Cerebral Cortex, 2019, Vol. 29, No. 5

Note
Conﬂict of Interests: The authors declare no competing ﬁnancial
interests.

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