Journal of Behavioral and Brain Science, 2013, 3, 107-130 Published Online February 2013 (
The Effect of Ethanol on the Neuronal Subserving of
Behavior in the Hippocampus
Yuri I. Alexandrov1,2, Yuri V. Grinchenko1,2, Diana G. Shevchenko1,
Robert G. Averkin3, Valentina N. Matz4, Seppo Laukka5, Mikko Sams6
1V. B. Svyrkov Laboratory of Neural Bases of Mind, Institute of Psychology,
Russian Academy of Sciences, Moscow, Russia
2Department of Psychophysiology, Faculty of Psychology, State Academical University of
Humanitarian Sciences, Moscow, Russia
3Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology,
Anatomy and Neuroscience, University of Szeged, Szeged, Hungary
4Laboratory of Morphology of the Central Nervous System, Institute of Higher Nervous Activity and Neurophysiology,
Russian Academy of Sciences, Moscow, Russia
5Learning Research Laboratory (LearnLab), University of Oulu, Oulu, Finland
6Brain and Mind Laboratory, Department of Biomedical Engineering and Computational
Science (BECS), Aalto University School of Science, Helsinki, Finland
Received October 25, 2012; revised November 26, 2012; accepted January 3, 2013
We have previously shown that both acute and chronic ethanol treatment depresses neural activity, specifically in the
cingulate cortex. Minor influences were found in the motor cortex. The acute effect of ethanol in the hippocampus was
intermediate to those in the cingulate and motor cortices. In the present study, we concentrate on the chronic effects of
ethanol on the hippocampus. We demonstrate how the neuronal activity underlying food-acquisition behavior is modi-
fied after chronic ethanol treatment, and how the hippocampus subserves formation of newly-formed alcohol-acquisi-
tion behavior. Neuronal activity in CA1 was more sensitive to chronic ethanol than the Dg area. Acute administration of
ethanol had a normalizing effect on the chronically-treated animals: their performance and the hippocampal neural ac-
tivity approached a normal range. The sets of neurons involved in food-acquisition behavior formed before chronic
ethanol treatment, and those involved in alcohol-acquisition behavior formed after treatment significantly overlapped
supporting the view that the neuronal mechanisms of pre-existing behavior provide the basis for the formation of new
behavior. Additionally, we also discovered alcohol-acquisition selective neurons. Assuming that the formation of new
neuronal specializations underlies learning, we believe that alcohol-selective neurons are specialized during the forma-
tion of alcohol-acquisition behavior. Our data demonstrate several new findings on the effect of acute and chronic
ethanol on hippocampus activity, and how the neuronal activity relates to behavior before and after ethanol treatment.
Keywords: Neuronal Activity; Learning; Memory; Place Cells; CA1; Dg; Alcohol; Morphology
1. Introduction
The hippocampus is intimately involved in memory,
learning and spatial cognition [1] (p. 148; see also in:
[2-9]; and others). It is very sensitive to alcohol intoxica-
tion [10-12] since ethanol selectively influences hippo-
campal neurotransmitter systems [6,13,14]. The amnesic
effects of ethanol administration are qualitatively similar
[6] (p. 122) to those found after hippocampal damage
[15]. Ethanol is therefore a useful tool to study the role of
the hippocampus in memory systems [16].
Accumulating data suggest that there is a strong simi-
larity between the neuronal mechanisms underlying the
formation of long-term memory during learning and
“long-lived adaptation” arising during chronic exposure
to addictive substances [17-20]. Cyclic-AMP response
element-binding protein (CREB) and immediate-early
genes are important components of the switch from short-
term to long-term memory [21-24]. Evidence of the ac-
tivity of these genes has also been found in a variety of
long-term adaptive changes in the brain, such as alcohol
abuse, to switch from short-term modifications in healthy
individuals to a long-term addiction. Understanding me-
chanisms of such switches is important and requires
more studies [17-19].
In our studies of acute ethanol effects on neural activ-
ity, we have analyzed behavioral specialization of neu-
opyright © 2013 SciRes. JBBS
rons. Ethanol has a selective influence on various brain
structures and even on neighboring neurons [25-30]. This
selectivity has been suggested to be due to various func-
tional and structural factors, each of which can predict
the effect of ethanol on a particular brain site, but fail to
do so on another site [31]. We have suggested that the
critical factor, though not unique, is the behavioral spe-
cialization of neurons, i.e. their belonging to a particular
functional system [32-36] underlying behavior.
We consider the formation of a new system as fixation
of the stage of individual development—the formation of
a new element of memory during learning. The neural
basis of this process is the specialization of “reserve”
(silent) neurons (and probably new neurons appearing in
neoneurogenesis) but not a change in specialization of
already specialized units. A new system is added to the
existing elements of memory. Newly formed systems do
not substitute the previously formed ones, but are “su-
perimposed” on them. Research in our laboratory has
demonstrated that complex instrumental behavior is real-
ized by new system that was formed during the learning
of the acts composing this behavior, and by the simulta-
neous realization of older systems, formed at previous
stages of individual development. The latter may be in-
volved in many behavioral patterns, i.e. they belong to
elements of memory that are common for various acts.
Therefore, the realization of particular behavior is the
realization of the history of its development. Multiple
systems, each fixing a certain stage of development of
the given behavior, are involved (see in [33-36]).
In our previous studies, different types of neuronal
specializations were identified in various brain areas of
freely moving rabbits and rats that were performing in-
strumental food-acquisition behavior in an operant cage
equipped with two pedals and two feeders [23,31,33-49].
Specializations can be classified in two main categories.
“SE-neurons” which are activated in relation to compara-
tively new behavioral acts formed during an animal’s
learning in the operant chamber (i.e. in relation to speci-
fic elements of the operant task: approaching the feeder,
taking food from the feeder, approaching the pedal, pres-
sing the pedal). Their activation is selectively related to a
certain behavioral act, but is independent of its detailed
motor execution. “NSE-neurons” are activated in relation
to non-specific elements of the behavioral task; their ac-
tivation is related to a certain movement (e.g. turning left
or right). They are activated during an identical move-
ment that can be performed in different behavioral con-
texts. “U-neurons” (undefined) do not show consistent
activation during the given task, i.e. their specialization is
Our classification is compatible with other neuronal
property classifications (see [42]) and corresponds well
to the multitude of facts revealed in various species dur-
ing investigation of cortical and subcortical unit activity.
In behaving animals, NSE-neurons as well as SE-neu-
rons can be found [50-59]. We have used this classifica-
tion in investigation of neuronal mechanisms of forma-
tion and realization of behavior, including the correlation
of immediate-genes expression with neurons specializa-
tions, for comparative investigations of neuronal sub-
serving of instrumental behaviors in different species, for
the study of effects of local brain damage, and acute and
chronic ethanol administration [23,24,31,33-49,60-62].
In our previous studies, we have analyzed the activity
of 1226 neurons in different layers of the posterior cin-
gulate cortex, the anterolateral motor cortex and in dif-
ferent regions of the hippocampus (CA1 and DG) of
freely moving healthy rabbits [31,40,42]. We concluded
that behavioral specialization is the main determinant of
the influence of ethanol [42]. We found that in the po-
sterior cingulate cortex, acute ethanol administration (1
g/kg; i.p.) selectively and reversibly depressed the activ-
ity of many SE-neurons. There were no changes in the
pattern of neuronal behavioral specializations (the nu-
merical relation between neurons belonging to the dif-
ferent types of behavioral specialization: SE-, NSE-, and
U-neurons) in the motor cortex. The hippocampus was in
an intermediate position. The same direction of ethanol
effects was found as for the cingulate cortex; the number
of certain kinds of SE-neurons (mostly complex-spike
cells) decreased, and that of NSE-neurons increased, but
there was no significant change in the relative number of
SE- and NSE-neurons as a whole. Interestingly, using
data showing attenuation of stress-induced c-fos expres-
sion by acute ethanol injection in different brain struc-
tures ([63], see review [64]), we can also conclude that
ethanol influenced many brain structures, but the cingu-
late cortex and the hippocampus are especially sensitive.
Similar suppressive effects on SE-neurons (including
classical place cells) were obtained when ethanol was ap-
plied via microdialysis on the hippocampal neurons of be-
having rats [65]. Steffensen and Henriksen [66] showed
that ethanol (1.2 g/kg) suppressed the activity of the prin-
cipal cells in the dentate gyrus and CA1, but not of the
interneurons in these regions. However, Yan et al. [67]
recently showed that for some types of CA1 interneurons
(SO interneurons) ethanol enhances, and for others (SLM
interneurons) it decreases the spontaneous activity. Addi-
tionally, White and Best [10] showed that acute injection
of ethanol reversibly eliminates spatially specific activity
of place-cells in the CA1 of the hippocampus. They pro-
posed that a decrease in place-cell firing could impair the
navigation capacity of animals. An increased sensitivity
of relatively new brain systems to ethanol was also de-
monstrated in nestlings [41] and humans [44].
Chronic ethanol treatment also has selective effects on
neurons [68-70]. However, it has remained unclear why
Copyright © 2013 SciRes. JBBS
some neurons are more affected than others [68] (p. 401),
[69]. We found evidence that the main target for chronic
effects were SE-neurons that are especially sensitive also
to acute ethanol influence [45,47]. Neuronal subserving
of behavior and morphology were modified after nine
months of ethanol treatment in the posterior cingulate
cortex (due to the selective effect on SE-neurons). The
modification was much more prominent than in the mo-
tor cortex, which is also less sensitive to acute effects.
In the present study, we examined the effect of ethanol
on hippocampal neurons. We hypothesized that ethanol
selectively influences the activity of hippocampal SE-
neurons. Specifically, we expected that if analogous sites
are modified by acute and chronic ethanol [71,72], hip-
pocampal modifications of neuronal subserving of be-
havior are smaller than those in the cingulate cortex, but
stronger than in the motor cortex.
The chronic influence of alcohol includes not only the
toxic effects considered above, but also the de novo for-
mation of a new need, that of alcohol. Appearance of a
new need is associated with the formation of behavior
directed to the satisfaction of that need. After chronic al-
coholization of animals, this need can be propped by
operant alcohol-acquiring behavior (AAB). It has been
suggested that the physiological substrate of alcoholic
motivation mediating AAB is formed on the basis of mo-
tivations formed premorbidly; this formation leads to the
reorganization of the subserving of premorbid behaviors
[73,74], such as food-acquisition behavior (FAB). Food
and drug addictions “may share common molecular, cel-
lular and systems-level mechanisms” [75] (p. 638). The
hippocampus belongs to brain networks relevant both in
the neurobiology of drug addiction and obesity [76].
Pleasure producing drugs influence those cellular and
molecular mechanisms that are activated by natural re-
wards like food [20,75]. We hypothesized that the set of
hippocampal neurons involved in supporting the premor-
bid and newly formed behaviors will overlap and that the
modification of premorbid FAB memory correlates to the
formation of AAB memory de novo. With this aim in
mind, we studied the behavioral specialization of neurons
in rabbits hippocampus trained to FAB and AAB, before
(FAB) and after (AAB) chronic alcohol treatment.
2. Materials and Methods
2.1. Ethics Statement
The experimental protocol was in accordance with the
Council of the European Communities Directive of No-
vember 24, 1986 (86/609 EEC) and the National Insti-
tutes of Health “Guidelines for the Care and Use of Ani-
mals for Experimental Procedures” and was approved by
the Russian Academy of Sciences.
We analyzed the activity of 783 neurons recorded in
the dorsal hippocampus (see Figure 1) of twelve chroni-
cally ethanol treated (9 month treatment, see below) male
adult rabbits (Orictolagus cuniculus; weight about 3 kg)
during the realization of complex instrumental behavior
in two experiments. We also studied the effect of chronic
ethanol treatment on brain morphology. The rabbits had a
nutritionally adequate diet.
The animals (N = 7; results were published earlier in
[42]) used in the study of acute ethanol effects on the
hippocampus (male rabbits) served as weight- and age-
matched healthy controls. Although data from the healthy
animals were obtained in a separate experiment, they can
be used for comparisons with the present experiments.
The same experimenters made the experiments. The age,
sex and weight of the animals, the duration, and the num-
ber of experimental sessions, the room, the experimental
cage, the training, the electrodes, the recording techni-
ques, the method and routine of acute ethanol administra-
tion, and the data analysis were identical in both the con-
trol and experimental groups (details below).
2.2. Chronic Ethanol Treatment
During chronic ethanol treatment lasting nine months, a
duration known to cause permanent structural and func-
tional alterations of the brain and impairments of perfor-
mance [6,77], the animals could freely choose between
ethanol (7% first 2 weeks, 10% later) and water perma-
nently present in water bottles (Cemic, Finland). Instru-
mental alcohol acquisition behavior can be formed in rab-
bits after such chronic ethanol treatment [46,48]. When
Figure 1. Localization of the electrode tracks. Photomicro-
graphs of representative frontal slices (Nissl-stained, 20 mi-
crometers in thickness) showing the microelectrode tracks
(delineated by red in A, C, and E) in CA1 (A-D ) and DG (E,
F). Magnification in A, C, and E—12.5×; in B, D, and F
Copyright © 2013 SciRes. JBBS
Copyright © 2013 SciRes. JBBS
ethanol is able to maintain operant action, dependence is
supposed to have occurred [78]. In order to test the etha-
nol need after eighth month of the treatment, ethanol was
withdrawn. After removal of ethanol for 24 hours, the
diurnal consumption increased by 25% in comparison to
the average amount per month. However, we have not
noticed physical signs of abstinence that can rarely be
observed in animals voluntarily administering the drug
and are not necessary indicators of dependence [71,72].
2.3. Food-Acquisition Training
Recording techniques, experimental cage, training, and
data analysis were the same for ethanol-treated and heal-
thy animals. They have been extensively described ear-
lier [31,39,42,45,46,48]. Before chronic ethanol treatment,
all rabbits were taught to acquire food by pressing one of
the two pedals in the experimental cage (Figure 2, de-
scribed in detail in [39]). Pressing of the pedal activated
an automatic feeder on the same side of the cage. Each
rabbit has learnt to repeatedly perform the food-acquisi-
tion task involving a constant series of acts (pressing the
pedal, turning to the feeder, taking a food from the feeder,
turning to the pedal) at both sides of the cage (left and
right in relation to the supervising experimenter). Dur-
ing training and recording periods in the home cage ani-
mals received about 25% of the normal daily amount of
2.4. Experiment 1
We compared behavior and neuronal activity in E and
E+ states (the sober state after isotonic solution admini-
stration and the state after acute ethanol administration),
and also the hippocampal morphology of the control and
experimental subjects. The animal subjects (N = 7) first
learned FAB and then had chronic ethanol treatment (dur-
ing 9 month, see below).
In E+ condition, the chronically treated group received
ethanol by intraperitoneal injection (12% ethanol in iso-
tonic solution) in a dose of 1 g/kg just before neuronal
recording session, and thereafter every 1.5 - 2 h, 0.3 - 0.5
g/kg ethanol was added until the end of experiment (as in
experiments with healthy controls). This routine allows
to reach maximum of blood alcohol concentration about
0.9 g/l (defined by gas chromatography; see [38]) 15 - 20
(a) (b) (c)
(d) (e) (f)
Figure 2. Schematic figure of the experimental cage and example of a NSE-neuron in CA1. The schematic figure at top left (a)
shows the experimental cage and animals acts during the behavioral cycle (see Methods). The NSE-neuron was activated
during food-acquisition both in left (b) and right (c) side cycles and during forced turning the animal’s body by the experi-
menter (d). The activation appeared during turning from the feeder (act 1 and act 6 for left and right side cycles respectively;
see Methods for the description of the acts), approaching the pedal, and after pedal pressing during turning from the pedal to
approach the feeder (acts 4 and 9 for left and right side cycles). This neuron activated during turning the animal in any direc-
tion in horizontal plane. a = neuron activity. b = actrogram of behavior in the left cycle (upward deflection = pedal pressing,
downward deflection = lowering head into feeder), c = actogram showing the position of the head in relation to either wall
(left or right) of the cage between the pedal and the feeder (downward deflection = head is near middle of the wall; see Mate-
rials and methods), d = actrogram of behavior in the right cycle (upward deflection = pedal pressing, dow nward deflection =
lowering head into feeder), e = EMG of m. masseter. Numbers 1 - 10 refer to the behavioral acts, depicted in the schematic
figure (a). (e) The probability of the presence of activation during the various behavioral acts. (f) Normalized mean frequency
f activity during various behavioral acts. The timescale is the same for b, c, and d; see horizontal bar (1 sec) under d. o
min after the first injection and to maintain a level about
0.4 g/l during the recording session. This concentration is
enough to elicit changes in behavior and brain activity in
rabbits, birds, and humans [36,39,41-46,48] (present re-
sults). In E, the equivalent amount of isotonic solution
was used before and during neuronal recording session.
Same animals participated both in the E+ and E ex-
periments. In half of the experiments animals were tested
in only one state (E+ or E) per day. The states were in-
troduced alternatingly, thus E conditions was followed
by E+, and so on. In the other half of the experiments, a
successful recording procedure at morning using the E
state was followed by another recording session at a new
electrode track afternoon using E+ state.
To make the data obtained in E+ and E experiments
comparable, we had to make the animals’ states similar
before the experiments. Previous results from other labo-
ratories [79], our earlier [46] and present data indicate
that neither alcohol withdrawal nor acute alcohol injec-
tion brings ethanol treated animals to a “normal” healthy
state. We tried to equalize animals’ states before E+ and
E in the following way: the dose of ethanol that the sub-
ject was allowed to consume in a home cage during the
night before the next experimental session depended on a
previous experimental session being twofold after E
than after E+ (0.5 g/kg). As a result, the blood alcohol
concentration just before recording did not differ signifi-
cantly after E+ and E, and only slightly exceeded the
upper limit of endogenous ethanol level in rabbits [45].
2.5. Experiment 2
Experiment 2 was otherwise similar to Experiment 1, ex-
cept that we compared neuronal activity in two appar-
ently similar instrumental behaviors. However, the moti-
vation in one case was food (FAB) and in the other case
ethanol (AAB). This experimental group (N = 5) was like
the first experimental group, but in addition the subjects
learned AAB during the final part of the chronic ethanol
2.6. Alcohol-Acquisition Training
After the chronic ethanol treatment in Experiment 2, the
same rabbits that had learned FAB before treatment were
additionally taught to acquire alcohol by means of the
same instrumental method in the same experimental cage.
As taking solid and liquid substances is very different,
we presented an ethanol solution in 0.5-mL gelatinous
capsules to make the final (consummatory) acts of FAB
and AAB more similar. The highest ethanol intake during
operant behavior in alcohol-preferring rats takes place at
15% concentration [80]. This is also what we found in
pilot experiments, and used this concentration in capsules.
After the training, we started single-unit recordings.
2.7. Recording Techniques
Single-unit activity was recorded in the control and
ethanol experiments from the CA1 area and the dentate
gyrus (Dg), in the same animals that were used for re-
cording of posterior cingulate-cortex neurons [45,46].
The coordinates of recording were by AP: 4.0, ML: 3.0 -
4.0, DV: 3.0 - 5.0 [81]. Glass pipette microelectrodes
with 2.5 m KCl, impedance of 1 - 5 MOhm at 1.5 kHz
were used and driven by a manual micromanipulator [82].
During advancing an electrode, the animals occupied one
of the corners near one of the feeders in rest. Below the
cortex, we paid attention to the appearance of ripples [42]
that were well detectable, even with the parameters of fil-
tering that we used for single unit recording (see below).
Approaching the stratum pyramidale was characterized
by the appearance of complex spikes that often fired syn-
chronously with the ripples. The activity of single cells
was preamplified (5×) and then amplified (1000×), fil-
tered at 300 - 5000 Hz. Single cells having a spike am-
plitude of not less than 400 - 1000 µV were recorded and
analyzed. We were able to record high amplitude positive
spikes (see Figures 3-5) that are characteristic of close to
the soma (juxtacellular) recordings. EMG was recorded
from m. masseter pars profundus, amplified (100×) and
filtered at 100 - 500 Hz. In addition to the electrophysio-
logical data, actographic marks of the behavior were also
tape-recorded. The animals’ movements from the pedal
to the feeder, or vice versa, were recorded with a photo-
cell fixed to the head of the animal, which responded to
photodiodes located in the middle of the left and right
walls of the cage (left and right behavioral cycles, corre-
spondingly) between the pedal and the feeder. Rabbit
behavior was video-recorded with the unit activity (au-
dio-channel), the light indicators of the pedal pressing
and head lowering, the counters of the cumulative num-
ber of spikes, and the timer. The depth of each active
unit’s location encountered during microelectrode pene-
tration was measured by means of a potentiometer at-
tached to a micromanipulator and connected with a cali-
brated scale showing the vertical location of the record-
ing tip. The total number of active cells during each mi-
croelectrode penetration was counted.
2.8. Data Analysis
Each behavioral cycle on the left side of the cage was
divided in accordance with the behavioral actographic
marks into five stages (behavioral acts): 1) turning the
head to the pedal; 2) approaching the pedal; 3) pressing
the pedal; 4) approaching the feeder and 5) seizing food
or an ethanol capsule from the feeder. The behavioral cy-
cle on the right side of the cage was divided into analo-
gous stages (acts 6 - 10; Figure 2). The mean frequency
of spike activity of a neuron in a particular act, and the
Copyright © 2013 SciRes. JBBS
(a) (b)
(c) (d)
Figure 3. Example of the activity of the SE-neuron in DG. The neuron had “specific” activation only in the right-sided
food-acquisition cycles. (a and b) details as in Figure 2. The neuron activated during approaching and pressing the right
pedal (b, acts 7 and 8), but not the left pedal (a, acts 3 and 4). (c and d) details as in Figure 2. Note that activations of the
neuron were observed in all realizations of acts 7 and 8.
(a) (b)
(c) (d)
(e) (f)
Figure 4. Example of the activity of the SE-neuron in CA1. The neuron had “specific” activation only in the left-side food-
acquisition cycles. Details as in Figure 2. The neuron activated during approaching and taking the piece of food from the left
feeder (a), but not from the right feeder (b) and not from the floor of the experimental cage (d). The neuron re mained silent
when the animal searched for a piece of food in the empty feeder (c). (e, f) Activations of the neuron were observed in all re-
alizations of act 5.
Copyright © 2013 SciRes. JBBS
(a) (b) (c)
(d) (e) (f)
(h) (i) (j)
Figure 5. Example of the activity of two simultaneously-recorded SE-neurons in DG . The first neuron had “specific” activa-
tion only in left side food-acquisition cycles (a) during lowering the head to the feeder (act 5). The cell activated also during
approaching the feeder and lowering the head to the feeder when it was empty (c). The second neuron had “spe cific” activa-
tion only in right side food-acquisition cycles (b) during turning from the pedal toward to the feeder and dur ing approaching
the feeder (act 9). The neuron was slightly active when the animal lowered the mouth to the feeder (act 10) and when it lifted
it up (act 6). The neuron fired with 3 spikes during approac hing the left feeder when it was empty (c). The spikes of this neu-
ron are indicated by asterisks (*). (d, e) Probability of activation and normalized mean frequency of activity of one neuron
and that of the other (g, h). (f, i) The probability of occurrence of any spike activity (including those not reaching the criterion
of activation) during the various behavioral acts of these two neurons.
probability of activation in the act were calculated. The
average frequency of activity for the entire recording was
also calculated for each neuron.
The indicator of activation was taken to be when the
frequency of an activity in one or several acts exceeded
by a factor of no less than 1.5 the average frequency of
the activity of a neuron over the whole recording period.
A neuron was considered to be specialized relative to a
system of specific behavioral acts if its activation during
this act was observed in all cases.
Graphs were used to describe the activity of the neuron
in each act of the behavior being studied over the course
of the entire period of recording, and to determine its
specialization they were plotted for all of the neurons
analyzed. The significance of the differences of the unit
activity in the acts was determined using Student’s t-
The units were divided into two groups: unidentified
(U-neurons, no consistent activation during the behav-
ioral cycles of instrumental behavior), and specialized in
relation to the systems of behaviors under study (activa-
ted in constant relation to a certain stage of the repeated
behavioral cycle). The latter group was further divided
into two groups with different behavioral specialization:
NSE-and SE-neurons (defined in the introduction to this
paper). NSE-neurons that showed activation in relation to
a particular movement of the body, head or lower jaw
were considered to be specialized relative to the compa-
ratively old systems formed earlier in individual devel-
opment [33,36]. Whether their activation appears or not
was related specifically to a certain movement, but inde-
pendent of its behavioral context. Activation appeared
during the same movement in different behaviors, e.g.
turning to the right when approaching the feeder on one
Copyright © 2013 SciRes. JBBS
side of the cage, or approaching the pedal at the opposite
side of the cage. SE-neurons showed activation in rela-
tion to novel behavioral acts established late in individual
development, such as during the animal’s learning in the
experimental cage (e.g. approaching the feeder, approa-
ching the pedal, pressing the pedal). Whether their acti-
vation appears or not was specifically related to a certain
behavioral act, but independent of its motor characteris-
tics. Similar activity was elicited when the animal pres-
sed the pedal with the left paw, right paw or both. Many
of the SE-neurons became active only when the animal
pressed a certain pedal, say in the left but not in the right
behavioral cycle. In the activity of such neurons, a “spe-
cific” phase may be distinguished—expressed activation;
this appears during that behavioral act, in relation to a
system in which these neurons were specialized. This ac-
tivation usually greatly exceeds the “non-specific” activ-
ity of this neuron recorded during other behavioral acts;
furthermore, “non-specific” activity is more variable and
appears not in all cases. The behavioral specialization of
a neuron is its permanent characteristic (see in [34-36];
and also the discussion section of this paper). Hence,
“specific” neuronal activity can serve as an index for the
actualization of a certain system, and the “non-specific”
activity of a neuron, as we have shown earlier theoreti-
cally and empirically [31,35,42,46], may indicate the spe-
cific system’s retrieval from memory during performance
of other “non-specific” behavioral acts. That is to say
non-specific activity reflects an intra-memory relation-
ship between the system to which a given neuron belongs
and other systems. There are also facts indicating that
even very variable discharges are not “neural noise”, but
signs of the neuron’s involvement in the organization of
behavior [83,84].
2.9. Morphological Analysis
After the experiments, the rabbits were sacrificed with an
overdose of Nembutal, and the brains were fixed in 10%
formalin solution. Serial frontal slices were prepared
(thickness 10 - 20 μm) and every 10th section was stained
using the Nissl method. The brains of the healthy control
and ethanol treated animals (Experiments 1 and 2) were
analyzed qualitatively and quantitatively with a light mi-
cro scope (“Cytopan”, Austria). The thickness of differ-
ent hippocampal layers was measured in the hemisphere
contralateral to the site of the microelectrode recording in
coordinates corresponding to the position of the micro-
electrode track (see Figure 1). The location of the units
in CA1 or Dg was determined on the basis of microma-
nipulator readings and this analysis.
The density of neurons complicated the determination
of the number of pyramidal or granular cells by direct vi-
sual counting. Thus, we measured the size of each layer
from the alveus to the lower arm of stratum granulosum
of fascia dentata. The calculations were taken for each
animal on five slices using an ocular-micrometer at 70-
fold magnification. However, it was necessary to count
the number of cells in the CA3 hilar because of some
conflicting data observed during studying it (c.f. Results).
The numerical density of neurons (in a grid with square
sides of 160 × 65 μ) was determined; calculations were
done in 10 visual field/slice using 5 slices per animal.
The size of neurons (50 neurons from each animal) was
measured at 945-fold magnification so that we deter-
mined the maximal and minimal mutually perpendicular
diameters (a, b) of the cell and the square was calculated
using the formula π4ab.
2.10. Statistics
2 test, two tailed t-test, exact Fisher criterion and
canonical discriminant function analysis (c.f. Results for
the description) were used in data analysis with a signi-
ficance level set at p < 0.05.
3. Results
3.1. Experiment 1
During chronic ethanol treatment, the number of days
when the animals consumed more water than ethanol de-
creased from 18% during the first 2 months to 4% within
6 - 9 months. Alcohol consumption increased during the
first 2 months from 3.8 ± 0.2 (mean ± S.E.M.) to 4.4 ±
0.2 g/kg/day (t = 2.56, p < 0.05). Thereafter, consump-
tion gradually decreased, and at the end of the chronic
ethanol treatment, the consumption was 2.7 ± 0.2 g/kg/
day. A similar reduction after eight-months of treatment
has been observed in rats and is related to the decreased
tolerance, characteristic of the late stages in the devel-
opment of alcoholism [85].
3.1.1. The Effect of Chronic Ethanol Treatment on
the Pattern of Neuronal Behavioral
We analyzed the activity of 381 hippocampal neurons
(168 in CA1 and 213 in Dg) of ethanol treated rabbits.
194 neurons were analyzed in E (98 in CA1 and 96 in
Dg) and 187 in E+ condition (70 in CA1 and 117 in Dg).
The results were compared with the activity of 303 neu-
rons (177 in CA1 and 126 in Dg) of the control animals,
152 of them in E (102 in CA1 and 52 in Dg) and 151 in
E+ condition (75 in CA1 and 76 in Dg). NSE- and SE-
neurons were found both in ethanol treated animals and
in healthy ones. As was shown earlier for the healthy
control animals, in chronically treated animals the great
majority (93%) of SE neurons may be classified as com-
plex spike neurons. 79% of NSE-neurons were classified
Copyright © 2013 SciRes. JBBS
as simple spike displace units or theta units. Thus, SE-
and NSE-units may be compared to projectional pyra-
middal and granular neurons, or short-axon non-pyrami-
dal interneurons, correspondingly (for details of the rela-
tionship of the classification of neurons used here and of
that based on morphological, electrophysiological and
functional properties of hippocampal cells, c.f. [42]).
Figure 2 shows an example of NSE-neuron activity.
This neuron became activated during horizontal turning
of the animal in different behavioral contexts: in the left
and right cycles of the FAB while approaching both ped-
als and feeders (b, c) and while turning the animal’s body
by the experimenter (d). Figures 3 and 4 show examples
of SE-neuron activity. One of them was activated during
approaching and pressing the right (3b), but not the left
(3a) pedal. The other was activated only during approa-
ching and taking food from the left (4a) but not from
right (4b) feeder. It was silent seizing food from the cage
floor (d) and while searching for food in the empty fee-
der (c). Sometimes it was possible to record activity si-
multaneously from two SE-neurons that clearly differed
in action potential amplitude or polarity. Such a pair of
cells that have “specific” activations during acts of taking
food near opposite walls is depicted in Figure 5.
Importantly, the pattern of behavioral specialization
observed in E condition changed after chronic ethanol
treatment. The relative numbers of SE-, NSE- and U-
neurons (15%, 9%, 76%) in ethanol treated animals were
statistically different (χ2 = 7.08; df = 2; p < 0.05) from
those in control animals (20%, 16%, 64%). This differ-
ence was significant in CA1 (χ2 = 6.47; df = 2; p < 0.05)
but not in Dg. An increase in the proportion of U-neu-
rons (12%) was especially prominent (χ2 = 6.10; df = 1; p
< 0.02). Again, it was significant in CA1 (χ2 = 4.14; df =
1; p < 0.05), but not in Dg. Nevertheless, the absolute
number of U-neurons increased in both structures in
ethanol treated animals. We examined this using the sta-
tistical method we employed earlier [31,42,45].
Taking into account the number of active units ob-
served in the microelectrode tracks during penetration,
we predicted the number of U-neurons, assuming that
this number would remain constant when the total num-
ber of active units in the structure changed. This ex-
pected number of U-neurons exceeded the observed
number significantly both in CA1 (χ2 = 11.79; df = 1; p <
0.001) and in Dg (χ2 = 27.66; df = 1; p < 0.0001). Among
the U-units, the proportion of neurons with slow (<1/sec)
background frequency (determined as the mean frequency
of those discharged during the behavior cycles) was sig-
nificantly increased (χ2 = 5.06; df = 1; p < 0.05) in the
ethanol treated animals. This increase was evident and
significant both in CA1 (χ2 = 5.31; df = 1; p < 0.05) and
Dg (χ2 = 20.39; df = 1; p < 0.0001).
The proportion of NSE-neurons increased in CA1, but
not in Dg, in ethanol treated animals from 6% (healthy)
till 17% (χ2 = 4, 26; df = 1; p < 0.05). Therefore, in etha-
nol treated animals proportions of SE- and NSE-neurons,
that were equal in control animals (18% and 17%), be-
come significantly different (16% and 6%; χ2 = 4.54; p <
The relative number of SE neurons that were highly
selective and active during approaching and/or pressing
the pedal near one wall of operant cage but not near the
opposite wall was significantly smaller in the ethanol
treated (3%) than in the control animals (7%; exact Fi-
sher criterion, p < 0.05).
We carried out additional tests for 13 SE-neurons
(whose activity was clearly place selective and the spike
amplitude was stable enough after completion the stan-
dard program) in ethanol treated animals and for 14 units
in the control rabbits to reveal if they have classical
“place” fields (see [86]). 10 cells in the first animal group
and 12 in the second were classified as “place” units, i.e.
units which were only activated when the rabbit was lo-
cated in a certain place within a varying behavioral con-
text (see also [36,42]). Figure 6 shows that the neuron
was activated near the right (b) but not the left pedal (6a)
corner during FAB, while approaching the right corner
without pedal pressing (4c), and when there was dis-
placement of the animal in the same place by the experi-
menter. The rest of the cells showed activations related to
a given place in the cage only in certain behavioral con-
ditions. The behavioral dependence of place related ac-
tivity in some hippocampal cells [42,58, 87], and the pos-
sibility for hippocampal principle cells being activated by
internal memory cues related to behavioral results [88-90]
has been demonstrated earlier. The quantitative relation-
ship between these two subgroups of cells remained con-
stant after ethanol treatment.
3.1.2. The Effect of Chronic Ethanol Treatment on
the Hippoca mpal Morpholo gy
Chronic ethanol treatment did not produce visible altera-
tions in the structure of pyramidal or granular cells. No
dark-stained cells, microvacuolation or shrinkage of the
cytoplasm were observed. Earlier Bengoechea and Gon-
zalo [91] have reported an absence of any change in size
of the nuclei of hippocampal neurons in patients with
chronic alcoholism. However, some measures related to
the size of layers and cell bodies in our experiments
showed significant changes. Canonical discriminant func-
tion analyses revealed that five indices of cell morphol-
ogy are sufficient for successful classification of neurons
from ethanol treated and from healthy animals (Table 1).
In this subset, 94% of the original grouped rabbits were
correctly classified (except for one rabbit that was clas-
sified as healthy though chronic).
To estimate the reliability of classification a cross-vali-
Copyright © 2013 SciRes. JBBS
Copyright © 2013 SciRes. JBBS
(a) (b)
(c) (d) (e)
Figure 6. Example of the activity of the right-side specific SE-neuron in CA1. The cell had “specific” activation only in right
side food-acquisition cycles. Details as in Figure 2. The cell was silent in left side food-acquisition cycles (a) and activated
during approaching the right cor ner with the pedal (b). The cell was also active during approaching the right corner without
the pressing the pedal (c) and during tests: forced displacement of the experimental animal in the same place by experimenter.
(d, e): The probability of the presence of activation during the various behavioral acts and normalized mean frequency of
activity during various behavioral acts. Note that activations of the neuron were observed in all realizations of acts 7. The
timescale is the same for a, b, and c; see horizontal bar (1 sec) under c.
Table 1. Canonical discriminant function coefficients.
dation procedure was used whereby each rabbit was clas-
sified by the functions derived from all samples except
that particular rabbit. This procedure showed that 69% of
the cross-validated grouped rabbits were correct.
Str Rad 10.653
V-MOL-A 44.027
V-MOL-B 22,225
hilar CA3 7.769
TopFd 33.888
(Constant) 2.390
Unstandardized Coefficients
The five indices are the following. Str Rad designates
mainly fibers of CA1 apical dendrites of pyramidal cells.
This index decreased by 6% (ns). Marked reduction of
spines on CA1 pyramidal and granule cell dendrites have
been shown earlier [77]. McMullen et al. [92] did not
find a significant decrease in the neuronal density of CA1,
but the thickness of the strata oriens and radiatum of
CA1 decreased significantly after 5 months of forced al-
cohol treatment. V-MOL-A designates mainly upper fi-
bers of Dg, consisting of axon collaterals of CA3 and
hilar CA3 cells, granular cells dendrites, and short axon
neurons. This index increased by 23% (t = 2.35; p <
0.05). Similarly, Walker and Hunter found that chronic
ethanol treatment reduced the spine density of CA1 py-
ramidal cells and increased the spine density of DG gra-
nule cells [69]. V-MOL-B designates mainly lower fibers
of Dg consisting of granular cells axons, mossy fibers,
polymorph cells dendrites. This index increased by 22%
(ns). Hilar CA3 designates mainly a pyramidal cells layer.
This index decreased by 30% (t = 2.73; p < 0.05). TopFd
designates V-MOL-A together with granular Fd cells.
This index increased by 15% (ns). Although the hilar
CA3 index significantly decreased, a corresponding de-
crease in the size of neuronal bodies was not observed.
was 18% smaller (t = 5.53; p < 0.001) in ethanol treated
than in healthy control animals, which correlates with the
hilar CA3 index decrease in the former subjects group.
3.1.3. The Acute Effect of Ethanol on Neuronal
Activity in FAB
Our previous study showed that in healthy animals the
proportion of U-units with slow background frequency
decreased significantly, by 11%, in E+ when compared
to E [42]. Corresponding a 7% decrease in ethanol
treated animals was not significant.
On the contrary, in healthy rabbits, the proportion of
SE neurons as a whole did not change significantly after
acute ethanol administration. In ethanol treated animals,
we found a tendency for the relative number of Dg SE-
neurons in E+ to be larger than in E (12%, χ2 = 3.80; df
To understand this mismatch, we counted the number
of neurons per standard square (8450 μ2). This number
= 1; p =0.051). The increase in the absolute number of
SE-neurons in Dg after acute administration of ethanol
was significant (χ2 = 4.42; df = 1; p < 0.05).
Changes within the group of SE neurons can also be
seen after acute ethanol administration. All 8 tested CA1
place neurons that were activated in a certain place wi-
thin a varying behavioral context in E+ had their place
field near the pedal, while in E only 2 out of 6 neurons
had place fields near the pedal. This difference is sig-
nificant (exact Fisher criterion, p < 0.02) although it is to
be considered as tentative because of the very small sam-
ple size. This decrease was significant also when check-
ing if the absolute number of CA1 pedal place cells chan-
ged from E to E+ (exact Fisher criterion, p < 0.002).
These changes correspond to a more general phenome-
non. The relative number of hippocampal neurons that
were specialized on the latest stage of learning in the ex-
perimental cage (i.e. “newest”) and especially selective
in respect to behavior being active during approaching
and/or pressing the pedal near one wall of the experi-
mental cage but not near the opposite wall (subgroup of
SE-neurons) increased significantly after ethanol admini-
stration (from 3% in E to 7% in E+; exact Fisher crite-
rion, p < 0.05). On the contrary, in healthy rabbits, the
relative number of these sub-group neurons decreased
significantly from 7% to 1% (exact Fisher criterion, p <
0.02). Interestingly, the number of “unilateral” pedal
neurons reached a control value after ethanol administra-
tion in chronic rabbits, i.e. that found in healthy animals
in a sober state. Different directions of changes lead to a
highly significant difference between the relative num-
bers of these neurons in E+ if compared to healthy and
ethanol treated animals (exact Fisher criterion, p < 0.002).
The increase and decrease were significant also when
checking the proposition about change of the absolute
numbers (exact Fisher criterion, p < 0.02; in both cases).
3.2. Experiment 2
FAB and AAB Performance
We have previously shown that rabbits are not attracted
by the substance capsules per se, they did not consume
empty capsules [46]. Usually, an animal stopped AAB
(i.e. refused to take capsules with ethanol from the feeder
after pedal pressing) earlier than it stopped FAB. How-
ever, rabbits that had just refused to take ethanol cap-
sules, willingly drank considerable amounts of ethanol
from the syringe presented to them by an experimenter.
Alcohol consumption during a recording session did not
exceed 0.40 g/kg. When the duration of each behavioral
act of AAB was compared with that of FAB, it was ap-
parent that most acts of the former behavior (turning the
head to a pedal, approaching a pedal, approaching a
feeder) were significantly slower (30% - 46%) than that
of the latter (in detail [46]).
3.3. Neuronal Activity in FAB and AAB
3.3.1. Commo n Ne uro ns
73 out of 99 units recorded during behavioral cycles of
both AAB and FAB were U-neurons. These neurons had
no consistent activation during any acts of AAB and
FAB. The majority of U-neurons were similarly active in
FAB and AAB. Our earlier data indicate that the U-neu-
rons are related to some system of “other behaviors”, not
analyzed in the study [31,42,45]. In connection with
these data, there is good reason to believe that U-neurons
(representatives of “other behaviors”) make a contribu-
tion (similar) to subserving both AAB and FAB.
However, one and the same unit might be a U-neuron
in for example FAB, but an SE-neuron in AAB or vice
versa. Consequently, although the number of U-neurons
was the same in these two behaviors (73 = 73), the set of
U-neurons in AAB and FAB was different. The set of U-
neurons consisted of 28 CA1 and 45 Dg cells in FAB and
of 29 CA1 and 44 Dg cells in AAB.
Activation of 17 neurons (10 in CA1 and 7 in Dg)
were classified as NSE-neurons. All NSE-neurons dem-
onstrated similar activity in AAB and FAB, i.e. if a neu-
ron had activation in FAB during a certain movement,
e.g. turning to the right, this neuron was also activated
during similar turning in AAB.
Among the neurons whose activations characteristics
fitted all the criteria for the SE-neurons, there were both
common as well selective neurons. One example of com-
mon neurons is shown in Figure 7. This neuron had se-
lective “specific” activations during taking food/a cap-
sule with ethanol from the right but not left feeder, both
in FAB (7(a), (c), and (e)) and AAB (7(b), (d), and (f)).
Thus, the situation was similar to that with U-neurons.
The number, but not the set of SE-neurons was the same
in two behaviors (N = 9). This number consisted of 7 (5
common) CA1 SE-neurons and 2 (both common) Dg SE-
neurons in FAB, while in AAB the distribution was the
following: 6 (5 common) CA1 SE-neurons and 3 (2 com-
mon) Dg SE-neurons. Spike frequency in activations of
three common SE- and NSE-neurons was significantly
higher during AAB than FAB; the spike frequency of one
neuron was significantly lower during AAB (t-test; p <
0.05 ÷ 0.02).
3.3.2. Alcohol- and Food-Selective Neurons
Four SE-neurons had “specific” activation selectively
only in AAB (N = 2) or only in FAB (N = 2). These units
were classified as specialized in relation to the acts of
AAB, i.e. as “alcohol-acquisition selective” cells or to
the acts of FAB i.e. as “food-acquisition selective” cells
according to the criteria given in the Methods section of
Copyright © 2013 SciRes. JBBS
(a) (b)
(c) (d)
(e) (f)
Figure 7. Example of the activity of “common” SE-neuron
in CA1. The neuron had similar “specific” activations in
FAB and AAB. Graphs (a-d) as in Figure 2, graphs (e and f)
as in Figure 5. The neuron had “specific” activations both
during taking the food (a, c, and e) and taking the capsule
filed with ethanol solution (b, d, and f) from the right feeder
(act 10). “Non-specific” activation appeared during approa-
ching the right feeder in alcohol-acquisition behavior.
this paper. Figure 8 represents a food-selective neuron
whose “specific” activations appeared only in FAB. In
AAB, the neuron demonstrated only “non-specific” acti-
vations. Figure 9 represents an alcohol-selective neuron.
It has “specific” activation during approaching the left
pedal in AAB. Tests showed that it has a place field near
this pedal. This field manifests itself only in AAB.
4. Discussion
4.1. The Effect of Chronic Ethanol Treatment on
Hippocampus Neurons
Our data shows that the pattern of neuronal behavioral
specializations in hippocampus changes after chronic
ethanol treatment. Significant changes (increase) were
observed only in the proportion of U-neurons, but not in
SE and NSE-neurons. In the cingulate cortex, the pro-
portions of all neurons changed [45]. In the motor cortex,
(a) (b)
(c) (d)
(e) (f)
Figure 8. Example of the activity of food specific SE-neuron
in CA1. The neuron had “specific” activation only in the
FAB (a, c, and e). “Non-specific” activation appeared in the
“same” acts during AAB (b, d, and f). Details as in Figure 7.
the proportions of different neuron types did not change
[47]. As a whole, our results show that the strength of
ethanol influence is strongest in the cingulate cortex,
somewhat weaker in the hippocampus, and weakest in
the motor cortex. In this respect, the acute and chronic
effects of ethanol are similar.
The number of SE-neurons was similar in chronic and
healthy animals. However, the number of neurons belong-
ing to the “pedal” subgroup of SE-neurons decreased sig-
nificantly both after chronic ethanol treatment and acute
ethanol administration in healthy animals. These neurons
belong to the newest systems formed at the latest stage of
learning that starts from taking food from a feeder and
proceeds through the development of the approach to a
pedal and comes to an end with training to press the pe-
Our results are in line with the Ribot-Jackson principle,
stating that those mechanisms which appear last are most
prone to disintegration [93]. However, a difference in
direction of changes was also observed. For example, the
number of U-neurons was larger in chronically-treated
tha in healthy animals, but in healthy animals ethanol n
Copyright © 2013 SciRes. JBBS
Copyright © 2013 SciRes. JBBS
(a) (b)
(c) (d) (e)
(f) (g) (h)
Figure 9. Example of the activity of alcohol specific SE-neuron in CA1. The neuron had “specific” activation only in the AAB.
The neuron activated “specifically” during approaching the left pedal in AAB (f-h) but not FAB (c-e). UP: Raster plots and
histograms are constructed in relation to the start of approaching the pedal in the left side cycle (a, act 2) and right side cycle
(b, act 7). Vertical bars on raster plots correspond to spike of single neuron and horizontal bars show sequences of spikes in
an individual cycle of FAB or AAB behaviors. Cumulative histograms with the bin width of 100 ms are shown beneath the
raster plots. Gray histograms for FAB, black histograms for AAB; (c-h) as in Figure 7.
decreased the number of U-neurons.
Hippocampal morphology is very sensitive to chronic
ethanol [68,94,95]. There is evidence that the main target
of ethanol is the NMDA-receptor system [68,96], which
may be influenced indirectly via GABAAR neurotrans-
mission [97]. In rodents, the extent of hippocampal neu-
ronal loss ranges from 10% to 40% (for a review, see
[68]), in our data it was 18%.
High neuronal loss and a reduction of the total hippo-
campus volume has been found also in people who drink
heavily [91,98]. Cadete-Leite et al. [99] (p. 12) conclu-
ded that chronic ethanol treatment leads to Dg cells loss,
along with dendritic regrowth compensating for the loss
of granule cells. We assume that changes in the thickness
of different hippocampus layers found in the present ex-
periments may be due to the following factors: the loss of
pyramidal cells, interneurons (which are present in all
layers), metabolic modifications in the cells’ bodies and/
or their axons and dendrites, a compensatory increase in
length, as well as branching of dendrites, and their addi-
tional myelinisation.
Similar to King et al. [94], we found an opposite di-
rection of changes in indexes that readily differentiate
healthy from chronic animals. Fibers of CA1 (apical den-
drites of pyramidal cells) decreased, and Dg granular
cells dendrites increased. Re-growing of the dendrites of
granule cells was reported also by Paula-Barbosa et al.
[95]. Comparative analysis of the degree of chronic etha-
nol influence on the pattern of specializations in CA1 and
Dg shows that the former is more sensitive to the influ-
ence mentioned. The results of the pattern analysis agrees
well with our another data set [24], showing that the
c-fos (immediate-early gene) expression resulting from a
behavioral mismatch (that is the first stage of learning;
[19,34,61]) is stronger in CA1 than in Dg. However,
based on histological criteria, it seems that Dg has been
modified more, and this modification includes re-grow-
ing. The criterion used is important here. Using another
criterion, decrease of rate of local cerebral glucose utili-
zation, led Smith et al. [100] to conclude that CA1 and
DG are equally susceptible to chronic ethanol treatment.
Bengoechea and Gonzalo [101] demonstrated that after
70 days of ethanol intake, neuron loss was significantly
more prominent in hilar CA3 than in CA1. Similarly,
Miki et al. [11] showed that according to this criterion
the hilus region is particularly vulnerable to the chronic
ethanol effects, also during early postnatal life in rats. Pro-
bably, this dissimilarity reflects different dynamics and/
or content of adaptation processes deploying in the struc-
tures owing to toxic ethanol influences. Alternatively,
smaller sensitivity of Dg activity to alcohol might be, at
least partly, due to more expressed morphological modi-
fications in this structure.
Neurogenesis may also contribute to the maintaining
of the optimal functioning of Dg [102-104]. It might be a
mechanism of hippocampal rearrangement after chronic
ethanol administration [105]. Neuron precursor cells in
adults proliferate at the border of the dentate granule cell
layer and the hilus; BrdU-labeled cells (that may co-ex-
press neuronal markers) can be detected in both sub-re-
gions of humans and animals [103,106,107]. Gould et al.
[108] have shown that the number of adult-generated neu-
rons doubles in Dg due to learning. Although short-du-
ration ethanol self-administration (less than 2 weeks) de-
creases neurogenesis in Dg, exercise stops this decrease
[109]. In the present experiment, exercise is FAB realiza-
tion and AAB learning and realization. Moreover, Åberg
et al. [105] have found that long-term (about 10 weeks)
moderate ethanol consumption leads to an increase of
neurogenesis in Dg, both in its rostral and caudal parts.
They stress that a key factor in their experiments was that
animals had free access to water and ethanol, and were
able, as in our experiments, to regulate ethanol intake at
will. Forced ethanol administration decreases neurogene-
sis in hippocampus. Forced administration of large etha-
nol doses causes also an opposite effect, so that most
sever morphological alterations were in CA1 [110]. Åberg
et al. [105] (pp. 1, 9) assume that new cells in the Dg
may subserve the long-lasting modifications of brain
function after ethanol treatment, including a role in learn-
ing and memory. Nilsson et al. [106] also make note of
the clear relation between enhanced neurogenesis in adult
hippocampus with improved memory.
4.2. The Effect of Acute Ethanol Treatment on
the Involvement of Hippocampal Neurons in
Subserving of Instrumental FAB
Acute ethanol increases the duration of FAB cycles in
healthy animals [31]. Interestingly, acute ethanol de-
creased the duration of FAB in ethanol treated animals.
However, the number of mistakes in behavior (e.g., miss-
ing a pedal, checking feeders without pressing a pedal)
increased significantly [45].
As in the cingulate cortex [45], we observed an oppo-
site effect of acute ethanol administration on the activity
of SE-neurons in chronic and healthy animals. In chronic
animals, their proportion increased, and in healthy ones it
decreased. Acute ethanol in chronic animals increased
the number of hippocampal “pedal” neurones, while this
number decreased in healthy ones. Importantly, in chro-
nic animals, acute administration of ethanol increased the
number of “unilateral” “pedal neurons”. In sober state,
their number was smaller in chronic than in healthy ani-
mals. Thus, acute ethanol shifted their proportion to-
wards the normal value.
Acute alcohol satisfies the alcohol need, and estab-
lishes the temporal balance in the catecholamine turnover
[111,112] and results, as is evident from the present and
our earlier data [45], in the involvement of additional
SE-neurons in the subserving of behavior. In the analysis
of cingulate cortex data, we used a log-linear analysis to
discover the source of additional SE-neurons. We found
evidence that additional SE-neurons were recruited from
the pool of U-neurons, and evidence of those SE-neurons
that were silent in E. However, in the hippocampus,
acute ethanol did not decrease the number of U-neurons
significantly. If a need for alcohol, at least partly, inhibits
other behaviors in alcoholics [113], reduction in need
might lead to disinhibition of previously silent food-ac-
quisition SE-neurons, and their involvement in FAB. In
any case, the increase in the number of SE-neurons leads
to a transient and partial normalization of the pattern of
behavioral specializations in ethanol treated rabbits. Si-
milarly, neurons in the primary somatosensory cortex of
ethanol treated rats have normalized (but are not quite
normal) responses to sensory stimulation after acute etha-
nol treatment [79].
Copyright © 2013 SciRes. JBBS
Our data agree with previous ones showing that acute
ethanol administration has a normalizing effect upon the
test performance of alcoholics [114,115]. A small amount
of alcohol has been shown to improve the creative output
of writers, artists, and composers who drink heavily [116].
Our results suggest that such an improvement might be
partly caused by transient normalization of the pattern of
neuronal behavioral specializations in alcohol-sensitive
neurons in the cingulate cortex and hippocampus.
4.3. Involvement of the Hippocampus in the
Formation of New and Modification of
Existing Behavior
Despite the relatively small quantity of ethanol consumed
by the animals during the experiment involving recording
of neuron activity lasting eight hours or more, none of
the animals showed any signs of physical withdrawal.
Such signs are rarely seen in animals taking ethanol in
conditions of free choice and are not an obligatory com-
ponent for determining their dependence.
Comparison of neuron activity in FAB and AAB
showed that sets of neurons involved in both behaviors
overlapped strongly, the majority of neurons being com-
mon to these behaviors. As we noted in our Introduction,
the irregular spiking of U-neurons might reflect their
participation in behavior that we are not aware of. The
present data, showing that a unit giving “specific” activa-
tion in one behavior may be a U-neuron in another beha-
vior, are in line with this suggestion. Therefore, both com-
mon neurons and U-neurons of undefined specialization
might be involved in FAB and AAB.
Our previous results suggest that the basis of learning
a new behavior is the establishment of permanent spe-
cializations of previously “silent” neurons, which become
active and start to take a role in subserving newly formed
behavior [33,35,36,48]. Others have shown that 1) the
newly formed neuron specializations and morphological
changes seem to be permanent, 2) recruitment of “silent”
neurons into the subserving of behavior could be an im-
portant learning mechanism [117] (p. 813) and 3) there
are many silent neurons in different brain areas [50,59,
118-132]. These findings support the suggestion that new
neurons become involved rather than the specialization
of previously specialized neurons changing. New neu-
rons appearing in adult neurogenesis are also likely to be
involved in the process of the formation of new behav-
iors (e.g. [34,133-136]).
In the present experiment, the formation of premorbid
FAB is the stage of individual development preceding
the formation of AAB. The existence of common neu-
rons involved in both types of behavior supports the view
that the neuronal mechanisms of pre-existing (in this case
premorbid) behavior provide the basis for the formation
of the neuronal mechanisms of new behavior directed at
satisfying the new need that of alcohol. Common neurons
might be ones specialized in relation to systems of pre-
viously formed behavior which do not lose their specia-
lization, but undergo modifications associated with the
fact that the systems in relation to which they were spe-
cialized are involved in performing the newly formed be-
havior. We regard this type of modification occurring after
new learning, and concerning cells belonging to systems
formed before the learning, as processes of reconsolida-
tion (see e.g. [137]) and called it “accommodative” re-
consolidation [35,46,48]. Such modifications might ex-
plain the quantitative differences in the activity of com-
mon SE- and NSE-neurons in FAB compared to AAB
observed here.
The initial stage of consolidatory and reconsolidatory
changes associated with the formation of neuronal spe-
cialization for newly formed systems is probably the ex-
pression of early genes that may be considered as a
molecular indicator of a mismatch between the need to
achieve a goal and the absence in individual memory of
the appropriate way to achieve it [22-24,34,36,61]. Early
genes are expressed in association with accommodative
reconsolidation of the type which NSE-neurons undergo
during the formation of AAB [138]. Castro-Alamancos et
al. [139] showed that the training of rats to press a pedal
with the paw was associated with significant increases in
the level of early gene expression in the projection zone
of the motor cortex. We think that our new data agrees
with this suggestion, taking into account that most chan-
ges in the motor cortex (at least in rats and rabbits) dur-
ing training are not due to formation of new specializa-
tions, but are modifications of previously formed systems,
associated with the reorganization of pre-existing struc-
tures of individual experience during learning [39,48,49].
The sets of neurons associated with FAB and AAB are
not identical. We found a small proportion of food-se-
lective and alcohol-selective neurons showing specific
activation only in FAB or only in AAB correspondingly.
There is evidence that the neuronal mechanisms of juice-
acquiring and cocaine-acquiring behaviors in monkeys
and rats are also partially separated ([140] (p. 1072) and
We have previously identified alcohol-selective SE-
neurons in the posterior cingulate [46] and in the motor
[48] cortex. Assuming that the formation of specializa-
tion of new neurons underlies learning, we believe that
alcohol-selective neurons are specialized during the for-
mation of AAB. We have not found food-selective cells
in the above structures. Nevertheless, on the basis of data
showing such neurons in other experimental situations
(differentiation of food from non-food objects or food
from water and salt, see [37,142]) we have suggested that
it is extremely difficult to prove the non-existence of the
Copyright © 2013 SciRes. JBBS
food-selective SE neurons [46] (p. 95). It is perhaps more
proper to say that the occurrence of alcohol-selective cells
in our experimental conditions was significantly higher.
Indeed, the present results show the existence of such
neurons in the hippocampus. Probably food-selective cells
were “added” to the FAB-group cells during AAB for-
mation as a component of accommodative reconsolida-
tion processes.
We found a small number of behaviorally selective
neurons in our experiment. Robinson and Carelli [143],
who recently compared activity of nucleus accumbens
neurons in instrumental (pedal pressing) AAB and in
water-acquisition behavior found that only 15% of the
neurons are common to both behaviors. There are several
reasons that can explain the discrepancy of the results,
such as the experimental animals, the studied brain struc-
ture, the reinforcement type, the classification of neurons,
and the history of learning. An important difference is
that in our experiments, pressing one and the same pedal
provided food or alcohol, but in the study by Robinson
and Carelli there were separate pedals for water and etha-
Differences in neuronal organization of FAB and AAB
can be examined from the point of place neurons concept.
Our previous and present data indicate that the activation
of place units corresponded to the space which was di-
vided into “fields” in relation to the behavioral acts the
animal realized while approaching the goal-objects in the
given environment [36,42,46,60]. Also other studies sup-
port this view [57,58,90,144-152]. Accordingly, all SE-
neurons may be considered as place units, which are ac-
tive in a given place (or places), because this place was
related to the appropriate results of the behavior. The dif-
ference between those SE-neurons which were in our
study classified as classic place units, and the other type
of SE-neurons would then be that the latter belong to
systems involved only in instrumental behaviors, but the
classic place units are also active in many other acts re-
alized in the same location.
If SE-neurons are place neurons, this means that the
involvement of new alcohol-selective SE-neurons in the
subserving of behavior during learning of the AAB is
“re-division” (“relocation” according to [146,147]; this is
“remapping” according to [153]) of the experimental
cage because of the appearance new place cells, although
the location of goal-objects in the cage stays invariable.
The occurrence of place-related activation of a given neu-
ron at a given place in a cage in one behavior, and the
absence of such activation of the same neuron in another
behavior deploying in the same cage without changing
any environmental cues (Figure 9; see also [149,151,153,
154]) supports the idea of re-division. This is in line with
the earlier idea by Markus et al. that place fields emerge
due to learning [149] (p. 7093). Thus, in our experiments,
re-division implies that physically the same environment
is presented by different hippocampal activity before and
after AAB formation.
On the basis of the chronic effect of alcohol, we can
suggest at least two mutually related aspects of neuronal
modifications determining the similarity between the
neuronal mechanisms underlying the formation of long-
term memory and long-lived adaptation arising during
chronic exposure to alcohol (c.f. the introduction to this
First is the loss of synapses and death of some cells
with simultaneous hyperinnervation of others, due to the
toxic action of ethanol [94] that probably takes place in
our experiments. This may be considered as a morpho-
logical description of reorganizations that manifest itself
functionally in the change of the FAB’s pattern of neu-
ronal specializations in ethanol treated vs healthy animals.
Long-lasting expression of FOS family proteins is in-
duced by drugs administration [155] and prolonged ac-
tivation of immediate genes (c-fos, c-jun) relates to de-
layed neuronal death in the hippocampus [156]. There is
experimental evidence suggesting that death of neurons
may be a component of processes of memory formation
as well [157]. Changes in the numbers of synapses are
also known to be an important component of the struc-
tural rearrangements accompanying the formation of long
term memory [128,130,158].
Secondly, a particular type of long-lived adaptation,
which occurs in chronic alcohol consumption, is indeed
very similar to the modifications underlying the forma-
tion of new memory. These include rearrangements of
neurons associated with the formation of new specializa-
tions for AAB and with processes of accommodative
reconsolidation of premorbid specializations. The prob-
lem is the relation between newly formed AAB and dif-
ferent kinds of premorbid behaviors: AAB strongly in-
hibit neurons of these pre-exiting behaviors that can be
realized only if the AAB goal is satisfied, i.e. after con-
sumption of alcohol. Thus our data agree with the sug-
gestion that drug addiction is an unusual form of learning
[18] (p. 569).
Kelley [19] has shown that exposure of rats to “drug-
paired” conditions induces c-fos expression (in turn evo-
ked by NMDA activation) reflecting a mismatch between
wanting the drug and its absence. It is the beginning of
new learning directed to the establishment of addictive
behavior. According to Kelley [19], addiction is basically
new learning and addictive drugs use the same neuronal
mechanisms as are used in reinforcement learning. Our
thinking is very similar, suggesting that similar mecha-
nisms of neural specialization underlie both “normal”
and “addictive” learning.
A major clinical challenge for the treatment of drug
addiction is its perseverance even after long-lasting drug
Copyright © 2013 SciRes. JBBS
abstinence [159]. Nestler and Aghajanian [17] specifi-
cally asked why alcoholism recurs even after many years
of abstinence. We suggest that this is because newly
formed neuronal specializations during abstinence do not
replace previously formed specializations for alcohol con-
sumption, but, rather supplement them. Chandler and Ka-
livas [20] state that drug acquisition behavior becomes
“hard-wired” in the brain and may be repeatedly and eas-
ily reactivated. Persistence of neuronal alcohol specific
specializations can be considered as a factor in the hard-
wiring. As “mechanisms mediating neuroadaptations in-
duced by chronic drug exposure and their behavioral con-
sequences (addiction) may be similar in different spe-
cies”, it is not strange that relapse is “highly predic-
tive … after withdrawal” not only in humans but also in
animals [160] (pp. 1016, 1014). Gene expression (start-
ing with immediate gene activation) underlying the for-
mation of new neuronal specializations plays the key role
in these mechanisms, underlying behavioral adaptations
in normal and in pathological states. The recent review
by Robison and Nestler [155] presents strong arguments
that alterations in gene activity considerably contribute to
the addictive phenotype. FOSB is encoded by the FosB
gene and belongs to FOS family transcription factors.
Expression of FOSB may be induced by many drugs
and is linked to various addiction-related behaviors.
5. Conclusions
Here we show that chronic ethanol treatment modifies
the activity and morphology of rabbit hippocampus neu-
rons. Our results indicate that the strength of chronic
ethanol influence, as also the effect of acute ethanol in
healthy animals, is strongest in the cingulate cortex, some-
what weaker in the hippocampus, and weakest in the
motor cortex. Thus, the acute effects of alcohol are pre-
dictive for its chronic effects. We suggest that the sub-
serving of behavior by hippocampus changes because of
ethanol causes adaptive reorganization of neurons, as
well as because of formation of new neuronal specializa-
tions in relation to the behavior directed at getting alco-
hol, and, in relation to this, because of modification of
functioning of neurons belonging to earlier formed beha-
viors. Our new results help us to understand how closely
the neuronal mechanisms in the hippocampus underlying
newly formed and previously formed behaviors are con-
nected through neurons common for both behaviors. The
results also demonstrate the similarity of the neuronal
mechanisms of long-term memory and long-lived modi-
fications of the nervous system occurring in conditions of
repeated dosage with alcohol and probably with other ad-
dictive substances.
This work was supported by The Grant of the Council
of the President of the Russian Federation for Leading
Scientific Schools (#3010.2012.6).
6. Author Contributions
Designed the experiments: Y. I. Alexandrov, Y. V. Grin-
chenko. Performed the experiments: Y. I. Alexandrov, Y.
V. Grinchenko, S. Laukka, V. N. Mats. Analyzed the
data: Y. I. Alexandrov, R. G. Averkin, Y. V. Grinchenko,
D. G. Shevchenko, M. Sams. Wrote the paper: Y. I. Ale-
xandrov, R. G. Averkin, M. Sams.
[1] P. Somogyi, “Hippocampus: Intrinsic Organization,” In:
P. Shepherd and S. Grillner, Eds., Handbook of Brain Mi-
crocircuits, Oxford University Press, New York, 2010, pp.
[2] O. S. Vinogradova, “Functional Organization of the Lim-
bic System in the Process of Registration of Information:
Facts and Hypotheses,” In: R. L. Isaakson and K. H.
Pribram, Eds., The Hippocampus, Plenum Press, New
York, 1984, pp. 1-69.
[3] L. R. Squire, “Memory and the Hippocampus: A Syn-
thesis from Findings with Rats, Monkeys, and Humans,”
Psychological Review, Vol. 99, No. 2, 1992, pp. 195-231.
[4] H. Eichenbaum “The Hippocampal System and Declara-
tive Memory in Animals,” Journal of Cognitive Neu-
roscience, Vol. 4, No. 3, 1992, pp. 217-231.
[5] G. Buzsaki, “The Hippocampo-Neocortical Dialogue,”
Cereb Cortex, Vol. 6, No. 2, 1996, pp. 81-92.
[6] D. B. Matthews and A. L. Morrow, “Effects of Acute and
Chronic Ethanol Exposure on Spatial Cognitive Process-
ing and Hippocampal Function in the Rat,” Hippocampus,
Vol. 10, No. 1, 2000, pp. 122-130.
[7] J. M. Silvers, S. Tokunaga, R. B. Berry, A. M. White and
D. B. Matthews, “Impairments in Spatial Learning and
Memory: Ethanol, Allopregnanolone, and the Hippocam-
pus,” Brain Research Reviews, Vol. 43, No. 3, 2003, pp.
275-284. doi:10.1016/j.brainresrev.2003.09.002
[8] E. T. Rolls, “A Computational Theory of Episodic Me-
mory Formation in the Hippocampus,” Behavioural Brain
Research, Vol. 215, No. 2, 2010, pp. 180-196.
[9] M. F. Carr, S. P. Jadhav and L. M. Frank, “Hippocampal
Replay in the Awake State: A Potential Substrate for Me-
mory Consolidation and Retrieval,” Nature Neuroscience,
Vol. 14, No. 2, 2011, pp. 147-153. doi:10.1038/nn.2732
[10] A. M. White and P. J. Best, “Effects of Ethanol on Hip-
pocampal Place-Cell and Interneuron Activity,” Brain
Research, Vol. 876, No. 1-2, 2000, pp. 154-165.
[11] T. Miki, S. J. Harris, P. Wilce, Y. Takeuchi and K. S.
Bedi, “Neurons in the Hilus Region of the Rat Hippo-
Copyright © 2013 SciRes. JBBS
campus Are Depleted in Number by Exposure to Alcohol
During Early Postnatal Life,” Hippocampus, Vol. 10, No.
3, 2000, pp. 284-295.
[12] B. Givens, J. M. Williams and T. M. Gill, “Septohippo-
campal Pathway as a Site for the Memory-Impairing Ef-
fects of Ethanol,” Hippocampus, Vol. 10, No. 1, 2000, pp.
[13] S. Kumar, P. Porcu, D. F. Werner, D. B. Matthews, J. L.
Diaz-Granados, R. S. Helfand and A. L. Morrow, “The
Role of GABA(A) Receptors in the Acute and Chronic
Effects of Ethanol: A Decade of Progress,” Psychophar-
macology, Vol. 205, No. 4, 2009, pp. 529-564.
[14] T. Tsurugizawa, A. Uematsu, H. Uneyama and K. Torii,
“The Role of the Gabaergic and Dopaminergic Systems in
the Brain Response to an Intragastric Load of Alcohol in
Conscious Rats,” Neuroscience, Vol. 171, No. 2, 2010, pp.
451-460. doi:10.1016/j.neuroscience.2010.09.016
[15] L. M. Garcia-Moreno, N. M. Conejo, A. Capilla, O. Gar-
cia-Sanchez, K. Senderek and J. L. Arias, “Chronic Etha-
nol Intake and Object Recognition in Young and Adult
Rats,” Progress in Neuro-Psychopharmacology & Bio-
logical Psychiatry, Vol. 26, No. 5, 2002, pp. 831-837.
[16] D. B. Matthews and J. R. Silvers, “The Use of Acute
Ethanol Administration as a Tool to Investigate Multiple
Memory Systems,” Neurobiology of Learning and Me-
mory, Vol. 82, No. 3, 2004, pp. 299-308.
[17] E. J. Nestler and G. K. Aghajanian, “Molecular and Cel-
lular Basis of Addiction,” Science, Vol. 278, No. 5335,
1997, pp. 58-63. doi:10.1126/science.278.5335.58
[18] T. W. Robbins and B. J. Everitt, “Drug Addiction: Bad
Habits Add up,” Nature, Vol. 398, No. 6728, 1999, pp.
567-570. doi:10.1038/19208
[19] A. E. Kelley, “Memory and Addiction: Shared Neural
Circuitry and Molecular Mechanisms,” Neuron, Vol. 44,
No. 1, 2004, pp. 161-179.
[20] L. J. Chandler and P. W. Kalivas, “Neuroscience: Brain’s
Defence against Cocaine,” Nature, Vol. 455, No. 7214,
2008, pp. 743-744. doi:10.1038/455743a
[21] B. Milner, L. R. Squire and E. R. Kandel, “Cognitive
Neuroscience and the Study of Memory,” Neuron, Vol.
20, No. 3, 1998, pp. 445-468.
[22] K. V. Anokhin, “Memory Consolidation: Narrowing the
Gap between Systems and Molecular Genetic Neuro-
sciences,” In: R. Miller, A. M. Ivanitsky and P. M.
Balaban, Eds., Conceptual Advances in Brain Research,
Complex Brain Functions Conceptual Advances in Rus-
sian Neuroscience, Harwood Academic Publishers, Am-
sterdam, 2000, pp. 53-72.
[23] O. E. Svarnik, Y. I. Alexandrov, V. V. Gavrilov, Y. V.
Grinchenko and K. V. Anokhin, “Fos Expression and
Task-Related Neuronal Activity in Rat Cerebral Cortex
after Instrumental Learning,” Neuroscience, Vol. 136, No.
1, 2005, 33-42. doi:10.1016/j.neuroscience.2005.07.038
[24] O. E. Svarnik and Y. I. Alexandrov, “Differential c-Fos
Activation in CA1, CA3 and Dentate Gyrus Areas of Hip-
pocampus after Various Phases of Appetitive Instrumen-
tal Learning,” International Symposium Hippocampus
and Memory Abstracts, Pushchino, 2006, p. 110.
[25] W. R. Klemm, C. G. Mallari, L. R. Dreyfus, J. C. Fiske, E.
Forney and J. A. Mikeska, “Ethanol-Induced Regional
and Dose-Response Differences in Multiple-Unit Activity
in Rabbits,” Psychopharmacology, Vol. 49, No. 3, 1976,
pp. 235-244. doi:10.1007/BF00426822
[26] S. F. Zornetzer, D. W. Walker and B. E. Hunter, “Neu-
rophysiological Changes Produced by Alcohol,” In: Bio-
medical Processes and Consequences of Alcohol Use,
Government Printing Office, Washington DC, 1982, pp.
[27] A. M. White, D. B. Matthews and P. J. Best, “Ethanol,
Memory, and Hippocampal Function: A Review of Re-
cent Findings,” Hippocampus, Vol. 10, No. 1, 2000, pp.
[28] N. D. Volkow, Y. Ma, W. Zhu, J. S. Fowler, J. Li, M.
Rao, K. Mueller, K. Pradhan, C. Wong and G. J. Wang,
“Moderate Doses of Alcohol Disrupt the Functional Or-
ganization of the Human Brain,” Psychiatry Research,
Vol. 162, No. 3, 2008, pp. 205-213.
[29] H. Morikawa and R. A. Morrisett, “Ethanol Action on
Dopaminergic Neurons in the Ventral Tegmental Area:
Interaction with Intrinsic Ion Channels and Neurotrans-
mitter Inputs,” International Review of Neurobiology, Vol.
91, 2010, pp. 235-288.
[30] C. P. Blomeley, S. Cains, R. Smith and E. Bracci, “Etha-
nol Affects Striatal Interneurons Directly and Projection
Neurons through a Reduction in Cholinergic Tone,” Neu-
ropsychopharmacology, Vol. 36, No. 5, 2011, pp. 1033-
1046. doi:10.1038/npp.2010.241
[31] Y. I. Alexandrov, Y. V. Grinchenko, S. Laukka, T. Jar-
vilehto, V. N. Maz and I. A. Svetlajev, “Acute Effect of
Ethanol on the Pattern of Behavioural Specialization of
Neurons in the Limbic Cortex of the Freely Moving Rab-
bit,” Acta Physiologica Scandinavica, Vol. 140, No. 2,
1990, pp. 257-268.
[32] P. K. Anokhin, “Biology and Neurophysiology of Condi-
tioned Reflex and Its Role in Adaptive Behavior,” Per-
gamon Press, Oxford, 1973.
[33] V. B. Shvyrkov, “Behavioral Specialization of Neurons
and the System-Selection Hypothesis of Learning,” In: F.
Klix and H. Hagendorf, Eds., Human Memory and Cog-
nitive Capabilities, Elsevier, Amsterdam, 1986, pp. 599-
[34] Y. I. Alexandrov, “Learning and Memory: Traditional
and Systems Approaches,” Neuroscience and Behavioral
Physiology, Vol. 36, No. 9, 2006, pp. 969-985.
Copyright © 2013 SciRes. JBBS
[35] Y. I. Alexandrov, “How We Fragment the World: The
View from Inside versus the View from Outside,” Social
Science Information, Vol. 47, No. 3, 2008, pp. 419-457.
[36] Y. I. Alexandrov, T. N. Grechenko, V. V. Gavrilov, A. G.
Gorkin, D. G. Shevchenko, V. Grinchenko Yu, I. O.
Aleksandrov, N. E. Maksimova, B. N. Bezdenezhnych
and M. V. Bodunov, “Formation and Realization of Indi-
vidual Experience,” Neuroscience and Behavioral Phy-
siology, Vol. 27, No. 4, 2000, pp. 441-454.
[37] Y. I. Alexandrov and A. V. Korpusova, “Role of Goal in
Determination of Neuronal Activity of the Rabbit Motor
and Visual Cortical Areas,” Neuroscience and Behavioral
Physiology, Vol. 17, No. 6, 1987, pp. 473-479.
[38] Y. I. Alexandrov, Y. V. Grinchenko, I. A. Svetlaev and O.
Abdrashitov, “Factors Determining the Effect of Acute
Ethanol Administration on Behavior Realization,” I.P.
Pavlov Journal of Higher Nervous Activity, Vol. 39, 1989,
pp. 1149-1151. (In Russian)
[39] Y. I. Alexandrov, Y. V. Grinchenko and T. Jarvilehto,
“Change in the Pattern of Behavioural Specialization of
Neurons in the Motor Cortex of the Rabbit Following
Lesion of the Visual Cortex,” Acta Physiologica Scan-
dinavica, Vol. 139, No. 1-2, 1990, pp. 371-385.
[40] Y. I. Alexandrov, Y. V. Grinchenko, S. Laukka, T. Jar-
vilehto and V. N. Maz, “Acute Effects of Alcohol on Unit
Activity in the Motor Cortex of Freely Moving Rabbits:
Comparison with the Limbic Cortex,” Acta Physiologica
Scandinavica, Vol. 142, No. 3, 1991, pp. 429-435.
[41] L. I. Alexandrov and Y. I. Alexandrov, “Changes of
Auditory-Evoked Potentials in Response to Behaviorally
Meaningful Tones Induced by Acute Ethanol Intake in
Altricial Nestlings at the Stage of Formation of Natural
Behavior,” Alcohol, Vol. 10, No. 3, 1993, pp. 213-217.
[42] Y. I. Alexandrov, Y. V. Grinchenko, S. Laukka, T. Jar-
vilehto, V. N. Maz and A. V. Korpusova, “Effect of
Ethanol on Hippocampal Neurons Depends on Their Be-
havioural Specialization,” Acta Physiologica Scandina-
vica, Vol. 149, No. 1, 1993, pp. 105-115.
[43] Y. I. Alexandrov, A. V. Korpusova, Y. V. Grinchenko, V.
N. Mats, S. Laukka and T. Jarvilehto, “Structural Changes
and Reorganization of the Activity of the Cortical Neu-
rons in the Behavior of Chronically Alcoholized Rabbits,”
I.P. Pavlov Journal of Higher Nervous Activity, Vol. 44,
1994, pp. 1077-1085. (In Russian)
[44] Y. Alexandrov, M. Sams, J. Lavikainen, K. Reinikainen
and R. Naatanen, “Differential Effects of Alcohol on the
Cortical Processing of Foreign and Native Language,”
International Journal of Psychophysiology, Vol. 28, 1998,
pp. 1-10. doi:10.1016/S0167-8760(97)00066-4
[45] Y. I. Alexandrov, Y. V. Grinchenko, M. V. Bodunov, V.
N. Matz, A. V. Korpusova, S. Laukka and M. Sams,
“Neuronal Subserving of Behavior before and after Chro-
nic Ethanol Treatment,” Alcohol, Vol. 22, No. 2, 2000, pp.
97-106. doi:10.1016/S0741-8329(00)00111-7
[46] Y. I. Alexandrov, Y. V. Grinchenko, D. G. Shevchenko,
R. G. Averkin, V. N. Matz, S. Laukka and A. V. Korpu-
sova, “A Subset of Cingulate Cortical Neurones Is Spe-
cifically Activated during Alcohol-Acquisition Beha-
viour,” Acta Physiologica Scandinavica, Vol. 171, No. 1,
2001, pp. 87-97.
[47] Y. I. Alexandrov, Y. V. Grinchenko, V. N. Mats, S.
Laukka and A. V. Korpusova, “Involvement of Rabbit
Motor Cortex Neurons in the Instrumental Behavior be-
fore and after Chronic Ethanol Consumption: A compa-
rison with the Limbic Cortex,” I.P. Pavlov Journal of
Higher Nervous Activity, Vol. 52, 2002, pp. 85-96. (In
[48] Y. I. Alexandrov, Y. V. Grinchenko, D. G. Shevchenko,
V. N. Mats, S. Laukka and R. G. Averkin, “Neuron
Activity in the Anterolateral Motor Cortex in Operant
Food-Acquiring and Alcohol-Acquiring Behavior,” Neu-
roscience and Behavioral Physiology, Vol. 35, No. 5,
2005, pp. 501-509. doi:10.1007/s11055-005-0085-2
[49] V. V. Gavrilov, Y. V. Grinchenkо and Y. I. Alexandrov,
“Comparison of the Sets of Behaviorally Specialized
Limbic Cortex Neurons in Rats and Rabbits,” Forum of
European Neuroscience, Berlin EJN Abstract Book, Vol.
10, 1998, p. 154.
[50] J. Y. Chang, S. F. Sawyer, R. S. Lee and D. J. Woodward,
“Electrophysiological and Pharmacological Evidence for
the Role of the Nucleus Accumbens in Cocaine Self-Ad-
ministration in Freely Moving Rats,” Journal of Neuro-
science, Vol. 14, No. 3, 1994, pp. 1224-1244.
[51] R. E. Hampson, J. D. Simeral and S. A. Deadwyler, “Dis-
tribution of Spatial and Nonspatial Information in Dorsal
Hippocampus,” Nature, Vol. 402, No. 6762, 1999, pp.
610-614. doi:10.1038/45154
[52] E. S. Luschei, C. R. Garthwaite and M. E. Armstrong,
“Relationship of Firing Patterns of Units in Face Area of
Monkey Precentral Cortex to Conditioned Jaw Move-
ments,” Journal of Neurophysiology, Vol. 34, No. 4, 1971,
pp. 252-261.
[53] R. E. Martin, G. M. Murray, P. Kemppainen, Y. Masuda
and B. J. Sessle, “Functional Properties of Neurons in the
Primate Tongue Primary Motor Cortex during Swallow-
ing,” Journal of Neurophysiology, Vol. 78, No. 3, 1997, pp.
[54] J. B. Ranck Jr., “Studies on Single Neurons in Dorsal
Hippocampal Formation and Septum in Unrestrained Rats.
I. Behavioral Correlates and Firing Repertoires,” Ex-
perimental Neurology, Vol. 41, No. 2, 1973, pp. 461-531.
[55] K. Shima, K. Aya, H. Mushiake, M. Inase, H. Aizawa and
J. Tanji, “Two Movement-Related Foci in the Primate
Cingulate Cortex Observed in Signal-Triggered and Self-
Paced Forelimb Movements,” Journal of Neurophysio-
logy, Vol. 65, No. 2, 1991, pp. 188-202.
[56] R. W. Stackman and J. S. Taube, “Firing Properties of
Rat Lateral Mammillary Single Units: Head Direction,
Head Pitch, and Angular Head Velocity,” Journal of Neu-
Copyright © 2013 SciRes. JBBS
rophysiology, Vol. 18, No. 21, 1998, pp. 9020-9037.
[57] S. I. Wiener, “Spatial, Behavioral and Sensory Correlates
of Hippocampal CA1 Complex Spike Cell Activity: Im-
plications for Information Processing Functions,” Pro-
gress in Neurobiology, Vol. 49, No. 4, 1996, pp. 335-361.
[58] S. I. Wiener, C. A. Paul and H. Eichenbaum, “Spatial and
Behavioral Correlates of Hippocampal Neuronal Activity,”
Journal of Neuroscience, Vol. 9, No. 8, 1989, pp. 2737-
[59] D. J. Woodward, P. H. Janak and J. Y. Chang, “Ethanol
Action on Neural Networks Studied with Multineuron
Recording in Freely Moving Animals,” Alcoholism: Cli-
nical & Experimental Research, Vol. 22, No. 1, 1998, pp.
10-22. doi:10.1111/j.1530-0277.1998.tb03612.x
[60] Y. I. Alexandrov, “The Psychophsyiological Significance
of Central and Peripheral Neuron Activity in Behavior,”
Nauka Press, Moscow, 1989.
[61] Y. I. Alexandrov, “Systemogeny and Neuronal Death,”
Neurochemistry, Vol. 21, 2004, pp. 5-14.
[62] A. G. Gorkin, K. G. Reymann and Y. I. Aleksandrov,
“Long-Term Potentiation and Evoked Spike Responses in
the Cingulate Cortex of Freely Mobile Rats,” Neurosci-
ence and Behavioral Physiology, Vol. 33, No. 8, 2003, pp.
763-772. doi:10.1023/A:1025189013402
[63] A. E. Ryabinin, K. R. Melia, M. Cole, F. E. Bloom and M.
C. Wilson, “Alcohol Selectively Attenuates Stress-Indu-
ced c-fos Expression in Rat Hippocampus,” Journal of
Neuroscience, Vol. 15, No. 1, 1995, pp. 721-730.
[64] C. Vilpoux, V. Warnault, O. Pierrefiche, M. Daoust and
M. Naassila, “Ethanol-Sensitive Brain Regions in Rat and
Mouse: A Cartographic Review, Using Immediate Early
Gene Expression,” Alcoholism: Clinical & Experimental
Research, Vol. 33, No. 6, 2009, pp. 945-969.
[65] N. Ludvig, B. T. Altura, S. E. Fox and B. M. Altura, “The
Suppressant Effect of Ethanol, Delivered via Intrahippo-
campal Microdialysis, on the Firing of Local Pyramidal
Cells in Freely Behaving Rats,” Alcohol, Vol. 12, No. 5,
1995, pp. 417-421. doi:10.1016/0741-8329(95)00012-G
[66] S. C. Steffensen and S. J. Henriksen, “Comparison of the
Effects of Ethanol and Chlordiazepoxide on Electrophy-
siological Activity in the Fascia Dentata and Hippocam-
pus Regio Superior,” Hippocampus, Vol. 2, No. 2, 1992,
201-211. doi:10.1002/hipo.450020210
[67] H. Yan, Q. Li, R. Madison, W. A. Wilson and H. S.
Swartzwelder, “Differential Sensitivity of Hippocampal
Interneurons to Ethanol in Adolescent and Adult Rats,”
Journal of Pharmacology and Experimental Therapeutics,
Vol. 335, No. 1, 2010, pp. 51-60.
[68] F. Fadda and Z. L. Rossetti, “Chronic Ethanol Consump-
tion: From Neuroadaptation to Neurodegeneration,” Pro-
gress in Neurobiology, Vol. 56, No. 4, 1998, pp. 385-431.
[69] D. W. Walker and B. E. Hunter, “Neuronal Adaptation in
the Hippocampus Induced by Long-Term Ethanol Expo-
sure,” NIDA—Research Monographs, Vol. 78, 1987, pp.
[70] T. Miki, S. J. Harris, P. A. Wilce, Y. Takeuchi and K. S.
Bedi, “Effects of Alcohol Exposure during Early Life on
Neuron Numbers in the Rat Hippocampus. I. Hilus Neu-
rons and Granule Cells,” Hippocampus, Vol. 13, No. 3,
2003, pp. 388-398. doi:10.1002/hipo.10072
[71] G. F. Koob and F. E. Bloom, “Cellular and Molecular
Mechanisms of Drug Dependence,” Science, Vol. 242,
No. 4879, 1988, pp. 715-723.
[72] G. F. Koob, L. Stinus, M. Le Moal and F. E. Bloom, “Op-
ponent Process Theory of Motivation: Neurobiological
Evidence from Studies of Opiate Dependence,” Neurosci-
ence & Biobehavioral Reviews, Vol. 13, No. 2-3, 1989,
pp. 135-140. doi:10.1016/S0149-7634(89)80022-3
[73] K. V. Sudakov, “Motivation and Reinforcement in the
Systemic Mechanisms of Behavior: Dynamic Reinforce-
ment Engrams,” Neuroscience and Behavioral Physiology,
Vol. 26, No. 5, 1996, pp. 445-453.
[74] K. V. Sudakov and A. V. Kotov, “Functional Trans-
switching of Dominant Motivation in the Systemic Or-
ganization of Behavioral Acts,” Neuroscience and Be-
havioral Physiology, Vol. 17, No. 2, 1987, pp. 85-91.
[75] P. J. Kenny, “Common Cellular and Molecular Mecha-
nisms in Obesity and Drug Addiction,” Nature Reviews
Neuroscience, Vol. 12, No. 11, 2011, pp. 638-651.
[76] N. D. Volkow, G. J. Wang, J. S. Fowler and F. Telang,
“Overlapping Neuronal Circuits in Addiction and Obesity:
Evidence of Systems Pathology,” Philosophical Transa-
ctions of the Royal Society B: Biological Sciences, Vol.
363, No. 1507, 2008, pp. 3191-3200.
[77] D. W. Walker, B. E. Hunter and W. C. Abraham, “Neu-
roanatomical and Functional Deficits Subsequent to Ch-
ronic Ethanol Administration in Animals,” Alcoholism:
Clinical & Experimental Research, Vol. 5, No. 2, 1981,
pp. 267-282. doi:10.1111/j.1530-0277.1981.tb04901.x
[78] T. K. Li, L. Lumeng, W. J. McBride, J. M. Murphy, J. C.
Froehlich and S. Morzorati, “Pharmacology of Alcohol
Preference in Rodents,” Advances in Alcohol & Substance
Abuse, Vol. 7, No. 3-4, 1988, pp. 73-86.
[79] J. K. Chapin and D. J. Woodward, “Ethanol Withdrawal
Increases Sensory Responsiveness of Single Somatosen-
sory Cortical Neurons in the Awake, Behaving Rat,”
Alcoholism: Clinical & Experimental Research, Vol. 13,
No. 1, 1989, pp. 8-14.
[80] J. M. Murphy, G. J. Gatto, W. J. McBride, L. Lumeng
and T. K. Li, “Operant Responding for Oral Ethanol in
the Alcohol-Preferring P and Alcohol-Nonpreferring NP
Lines of Rats,” Alcohol, Vol. 6, No. 2, 1989, pp. 127-131.
[81] R. L. McBride and W. R. Klemm, “Stereotaxic Atlas of
Rabbit Brain Based on the Rapid Method of Photography
of Frozen, Unstained Sections,” Communications in
Copyright © 2013 SciRes. JBBS
Behavioral Biology, Vol. A2, 1968, pp. 179-215.
[82] Iu. V. Grinchenko and V. B. Shvyrkov, “A Simple Micro-
manipulator for Studying Neuronal Activity in Unre-
strained Rabbits,” I.P. Pavlov Journal of Higher Nervous
Activity, Vol. 24, 1974, pp. 870-872. (In Russian)
[83] D. Ferster, “Is Neural Noise Just a Nuisance?” Science,
Vol. 273, No. 5283, 1996, p. 1812.
[84] E. Vaadia, I. Haalman, M. Abeles, H. Bergman, Y. Prut,
H. Slovin and A. Aertsen, “Dynamics of Neuronal Inter-
actions in Monkey Cortex in Relation to Behavioural
Events,” Nature, Vol. 373, No. 6514, 1995, pp. 515-518.
[85] V. Burov Iu, A. B. Kampov-Polevoi and O. P. Kashevs-
kaia, “Formation of Experimental Alcoholism in a Popu-
lation of Crossbred Rats,” Bulletin of Experimental Bio-
logy and Medicine, Vol. 96, 1983, pp. 67-68. (In Russian)
[86] J. O’Keefe, “Place Units in the Hippocampus of the Freely
Moving Rat,” Experimental Neurology, Vol. 51, No. 1,
1976, pp. 78-109. doi:10.1016/0014-4886(76)90055-8
[87] E. R. Wood, P. A. Dudchenko and H. Eichenbaum, “The
Global Record of Memory in Hippocampal Neuronal
Activity,” Nature, Vol. 397, No. 6720, 1999, pp. 613-616.
[88] E. Pastalkova, V. Itskov, A. Amarasingham and G.
Buzsaki, “Internally Generated Cell Assembly Sequences
in the Rat Hippocampus,” Science, Vol. 321, No. 5894,
2008, pp. 1322-1327. doi:10.1126/science.1159775
[89] G. Buzsaki, “Hippocampus: Network Physiology,” In: P.
Shepherd and S. Grillner, Eds., Handbook of Brain Mi-
crocircuits, Oxford University Press, New York, 2010, pp.
[90] V. V. Gavrilov, S. I. Wiener and A. Berthoz, “Discharge
Correlates of Hippocampal Complex Spike Neurons in
Behaving Rats Passively Displaced on a Mobile Robot,”
Hippocampus, Vol. 8, No. 5, 1998, pp. 475-490.
[91] O. Bengoechea and L. M. Gonzalo, “Effect of Chronic
Alcoholism on the Human Hippocampus,” Histology and
Histopathology, Vol. 5, No. 3, 1990, pp. 349-357.
[92] P. A. McMullen, J. A. Saint-Cyr and P. L. Carlen, “Mor-
phological Alterations in Rat CA1 Hippocampal Pyra-
midal Cell Dendrites Resulting from Chronic Ethanol
Consumption and Withdrawal,” Journal of Comparative
Neurology, Vol. 225, No. 1, 1984, pp. 111-118.
[93] T. Ribot, “Diseases of Memory,” Appleton-Century Crofts,
New York, 1882,
[94] M. A. King, B. E. Hunter and D. W. Walker, “Alterations
and Recovery of Dendritic Spine Density in Rat Hippo-
campus Following Long-Term Ethanol Ingestion,” Brain
Research, Vol. 459, No. 2, 1988, pp. 381-385.
[95] M. M. Paula-Barbosa, F. Brandao, M. D. Madeira and A.
Cadete-Leite, “Structural Changes in the Hippocampal
Formation after Long-Term Alcohol Consumption and
Withdrawal in the Rat,” Addiction, Vol. 88, No. 2, 1993,
pp. 237-247. doi:10.1111/j.1360-0443.1993.tb00807.x
[96] D. B. Matthews, “Ethanol and the Hippocampal System:
Behaviour to Molecular Biology,” Hippocampus, Vol. 10,
No. 1, 2000, p. 87.
[97] J. Schummers and M. D. Browning, “Evidence for a Role
for GABA(A) and NMDA Receptors in Ethanol Inhibi-
tion of Long-Term Potentiation,” Molecular Brain Re-
search, Vol. 94, No. 1-2, 2001, pp. 9-14.
[98] T. P. Beresford, D. B. Arciniegas, J. Alfers, L. Clapp, B.
Martin, Y. Du, D. Liu, D. Shen and C. Davatzikos, “Hip-
pocampus Volume Loss Due to Chronic Heavy Drink-
ing,” Alcoholism: Clinical & Experimental Research, Vol.
30, No. 11, 2006, pp. 1866-1870.
[99] A. Cadete-Leite, M. A. Tavares, H. B. Uylings and M. Pau-
la-Barbosa, “Granule Cell Loss and Dendritic Regrowth
in the Hippocampal Dentate Gyrus of the Rat after Chro-
nic Alcohol Consumption,” Brain Research, Vol. 473, No.
1, 1988, pp. 1-14. doi:10.1016/0006-8993(88)90309-5
[100] D. G. Smith, J. E. Learn, W. J. McBride, L. Lumeng, T.
K. Li and J. M. Murphy, “Long-Term Effects of Alcohol
Drinking on Cerebral Glucose Utilization in Alcohol-Pre-
ferring Rats,” Pharmacology Biochemistry and Behavior,
Vol. 69, No. 3-4, 2001, pp. 543-553.
[101] O. Bengoechea and L. M. Gonzalo, “Effects of Alcoholi-
zation on the Rat Hippocampus,” Neuroscience Letters,
Vol. 123, No. 2, 1991, pp. 112-114.
[102] H. G. Kuhn, H. Dickinson-Anson and F. H. Gage, “Neu-
rogenesis in the Dentate Gyrus of the Adult Rat: Age-Re-
lated Decrease of Neuronal Progenitor Proliferation,”
Journal of Neuroscience, Vol. 16, No. 6, 1996, pp. 2027-
[103] J. M. Parent, “Injury-Induced Neurogenesis in the Adult
Mammalian Brain,” Neuroscientist, Vol. 9, No. 4, 2003,
pp. 261-272. doi:10.1177/1073858403252680
[104] C. D. Clelland, M. Choi, C. Romberg, G. D. Clemenson
Jr., A. Fragniere, P. Tyers, S. Jessberger, L. M. Saksida,
R. A. Barker, F. H. Gage and T. J. Bussey, “A Functional
Role for Adult Hippocampal Neurogenesis in Spatial Pat-
tern Separation,” Science, Vol. 325, No. 5937, 2009, pp.
210-213. doi:10.1126/science.1173215
[105] E. Aberg, C. P. Hofstetter, L. Olson and S. Brene, “Mo-
derate Ethanol Consumption Increases Hippocampal Cell
Proliferation and Neurogenesis in the Adult Mouse,” In-
ternational Journal of Neuropsychopharmacology, Vol. 8,
No. 4, 2005, pp. 557-567.
[106] M. Nilsson, E. Perfilieva, U. Johansson, O. Orwar and P.
S. Eriksson, “Enriched Environment Increases Neuroge-
nesis in the Adult Rat Dentate Gyrus and Improves Spa-
tial Memory,” Journal of Neurobiology, Vol. 39, No. 4,
1999, pp. 569-578.
Copyright © 2013 SciRes. JBBS
[107] P. S. Eriksson, E. Perfilieva, T. Bjork-Eriksson, A. M. Al-
born, C. Nordborg, D. A. Peterson and F. H. Gage, “Neu-
rogenesis in the Adult Human Hippocampus,” Nature
Medicine, Vol. 4, No. 11, 1998, pp. 1313-1317.
[108] E. Gould, A. Beylin, P. Tanapat, A. Reeves and T. J.
Shors, “Learning Enhances Adult Neurogenesis in the
Hippocampal Formation,” Nature Neuroscience, Vol. 2,
No. 3, 1999, pp. 260-265. doi:10.1038/6365
[109] F. T. Crews, K. Nixon and M. E. Wilkie, “Exercise Re-
verses Ethanol Inhibition of Neural Stem Cell Prolifera-
tion,” Alcohol, Vol. 33, No. 1, 2004, pp. 63-71.
[110] J. N. Riley and D. W. Walker, “Morphological Altera-
tions in Hippocampus after Long-Term Alcohol Consum-
ption in Mice,” Science, Vol. 201, No. 4356, 1978, pp.
646-648. doi:10.1126/science.566953
[111] M. M. Airaksinen and P. Peura, “Mechanisms of Alcohol
Withdrawal Syndrome,” Medical Biology, Vol. 65, No.
2-3, 1987, pp. 105-112.
[112] I. P. Anokhina, B. M. Kogan and A. Z. Drozdov, “Distur-
bances in Regulation of Catecholamine Neuromediation
in Alcoholism,” Alcohol, Vol. 23, No. 5, 1988, pp. 343-
[113] B. S. Bratus, “Psychological Analysis of Personality Chan-
ges in Alcoholics,” Edition of Moscow University, Mos-
cow, 1974.
[114] R. J. Gibbins, H. Kalant, A. E. LeBlanc and J. W. Clark,
“The Effects of Chronic Administration of Ethanol on
Startle Thresholds in Rats,” Psychopharmacologia, Vol.
19, No. 2, 1971, pp. 95-104. doi:10.1007/BF00402633
[115] B. Kissin, “The Pharmacodynamics and Natural History
of Alcoholism,” In: The Biology of Alcoholism, Plenum
Press, New York, 1974, pp. 1-36.
[116] A. M. Ludwig, “Alcohol Input and Creative Output,”
British Journal of Addiction, Vol. 85, No. 7, 1990, pp.
953-963. doi:10.1111/j.1360-0443.1990.tb03726.x
[117] D. E. Feldman and M. Brecht, “Map Plasticity in Soma-
tosensory Cortex,” Science, Vol. 310, No. 5749, 2005, pp.
810-815. doi:10.1126/science.1115807
[118] I. V. Bondar, D. A. Leopold, B. J. Richmond, J. D. Victor
and N. K. Logothetis, “Long-Term Stability of Visual
Pattern Selective Responses of Monkey Temporal Lobe
Neurons,” PLoS One, Vol. 4, No. 12, 2009, p. e8222.
[119] P. M. Bradley, B. D. Burns, T. M. King and A. C. Webb,
“Electrophysiological Correlates of Prior Training: An in
Vitro Study of an Area of the Avian Brain Which Is Es-
sential for Early Learning,” Brain Research, Vol. 708, No.
1, 1996, pp. 100-107. doi:10.1016/0006-8993(95)01470-5
[120] M. Brecht, M. Scneider and I. Manns, “Silent Neurons in
Sensorimotor Cortices: Implication for Cortical Plasti-
city,” In: F. F. Ebner Ed., Neural Plasticity in Adult
Somatic Sensory-Motor Systems, Taylor & Francis Group,
Boca Raton, 2005, pp. 1-19.
[121] P. A. Greenberg and F. A. Wilson, “Functional Stability
of Dorsolateral Prefrontal Neurons,” Journal of Neuro-
physiology, Vol. 92, No. 2, 2004, pp. 1042-1055.
[122] M. S. Jog, Y. Kubota, C. I. Connolly, V. Hillegaart and A.
M. Graybiel, “Building Neural Representations of Habits,”
Science, Vol. 286, No. 5445, 1999, pp. 1745-1749.
[123] M. A. Nicolelis, A. A. Ghazanfar, B. M. Faggin, S. Vo-
taw and L. M. Oliveira, “Reconstructing the Engram: Si-
multaneous, Multisite, Many Single Neuron Recordings,”
Neuron, Vol. 18, No. 4, 1997, pp. 529-537.
[124] E. M. Schmidt, M. J. Bak and J. S. McIntosh, “Long-Term
Chronic Recording from Cortical Neurons,” Experimental
Neurology, Vol. 52, No. 3, 1976, pp. 496-506.
[125] H. A. Swadlow and T. P. Hicks, “Subthreshold Receptive
Fields and Baseline Excitability of, ‘Silent’ S1 Callosal
Neurons in Awake Rabbits: Contributions of AMPA/Kai-
nate and NMDA Receptors,” Experimental Brain Re-
search, Vol. 115, No. 3, 1997, pp. 403-409.
[126] L. T. Thompson and P. J. Best, “Long-Term Stability of
the Place-Field Activity of Single Units Recorded from
the Dorsal Hippocampus of Freely Behaving Rats,” Brain
Research, Vol. 509, No. 2, 1990, pp. 299-308.
[127] J. C. Williams, R. L. Rennaker and D. R. Kipke, “Sta-
bility of Chronic Multichannel Neural Recordings: Impli-
cations for a Long-Term Neural Interface,” Neurocom-
putting, Vol. 26-27, 1999, pp. 1069-1076.
[128] T. Xu, X. Yu, A. J. Perlik, W. F. Tobin, J. A. Zweig, K.
Tennant, T. Jones and Y. Zuo, “Rapid Formation and Se-
lective Stabilization of Synapses for Enduring Motor Me-
mories,” Nature, Vol. 462, 2009, pp. 915-919.
[129] M. A. Wilson and B. L. McNaughton, “Dynamics of the
Hippocampal Ensemble Code for Space,” Science, Vol.
261, No. 5124, 1993, pp. 1055-1058.
[130] G. Yang, F. Pan and W. B. Gan, “Stably Maintained Den-
dritic Spines Are Associated with Lifelong Memories,”
Nature, Vol. 462, No. 7275, 2009, pp. 920-924.
[131] L. M. Frank, G. B. Stanley and E. N. Brown, “Hippo-
campal Plasticity across Multiple Days of Exposure to
Novel Environments,” Journal of Neuroscience, Vol. 24,
No. 35, 2004, pp. 7681-7689.
[132] J. Epsztein, M. Brecht and A. K. Lee, “Intracellular De-
terminants of Hippocampal CA1 Place and Silent Cell
Activity in a Novel Environment,” Neuron, Vol. 70, No.
1, 2011, pp. 109-120. doi:10.1016/j.neuron.2011.03.006
[133] A. Carleton, L. T. Petreanu, R. Lansford, A. Alvarez-
Buylla and P. M. Lledo, “Becoming a New Neuron in the
Adult Olfactory Bulb,” Nature Neuroscience, Vol. 6, No.
5, 2003, pp. 507-518.
[134] T. J. Shors, G. Miesegaes, A. Beylin, M. Zhao, T. Rydel
and E. Gould, “Neurogenesis in the Adult is Involved in
Copyright © 2013 SciRes. JBBS
the Formation of Trace Memories,” Nature, Vol. 410, No.
6826, 2001, pp. 372-376. doi:10.1038/35066584
[135] J. A. Paton and F. N. Nottebohm, “Neurons Generated in
the Adult Brain Are Recruited into Functional Circuits,”
Science, Vol. 225, No. 4666, 1984, pp. 1046-1048.
[136] G. Kempermann, H. G. Kuhn and F. H. Gage, “Experi-
ence-Induced Neurogenesis in the Senescent Dentate Gy-
rus,” Journal of Neuroscience, Vol. 18, No. 9, 1998, pp.
[137] K. V. Anokhin, A. A. Tiunova and S. P. Rose, “Reminder
Effects—Reconsolidation or Retrieval Deficit? Pharma-
cological Dissection with Protein Synthesis Inhibitors
Following Reminder for a Passive-Avoidance Task in
Young Chicks,” European Journal of Neuroscience, Vol.
15, No. 11, 2002, pp. 1759-1765.
[138] O. E. Svarnik, T. A. Fadeeva, K. V. Anokhin and Y. I.
Alexandrov, “Learning Strategies and c-Fos Expression
in the Rat Retrosplenial Cortex during Operant Skill Ac-
quisition,” FENS Abstracts, Vol. 4, 2008.
[139] M. A. Castro-Alamancos, J. Borrell and L. M. Garcia-
Segura, “Performance in an Escape Task Induces Fos-
Like Immunoreactivity in a Specific Area of the Motor
Cortex of the Rat,” Neuroscience, Vol. 49, No. 1, 1992,
157-162. doi:10.1016/0306-4522(92)90083-E
[140] E. M. Bowman, T. G. Aigner and B. J. Richmond, “Neu-
ral Signals in the Monkey Ventral Striatum Related to
Motivation for Juice and Cocaine Rewards,” Journal of
Neurophysiology, Vol. 75, No. 3, 1996, pp. 1061-1073.
[141] R. M. Carelli and S. A. Deadwyler, “Cellular Mechanisms
Underlying Reinforcement-Related Processing in the Nu-
cleus Accumbens: Electrophysiological Studies in Behav-
ing Animals,” Pharmacology Biochemistry and Behavior,
Vol. 57, No. 3, 1997, pp. 495-504.
[142] K. M. Kendrick and B. A. Baldwin, “The Effects of
Sodium Appetite on the Responses of Cells in the Zona
Incerta to the Sight or Ingestion of Food, Salt and Water
in Sheep,” Brain Research, Vol. 492, No. 1-2, 1989, pp.
211-218. doi:10.1016/0006-8993(89)90903-7
[143] D. L. Robinson and R. M. Carelli, “Distinct Subsets of
Nucleus Accumbens Neurons Encode Operant Respond-
ing for Ethanol versus Water,” European Journal of
Neuroscience, Vol. 28, No. 9, 2008, pp. 1887-1894.
[144] J. A. Ainge, M. Tamosiunaite, F. Woergoetter and P. A.
Dudchenko, “Hippocampal CA1 Place Cells Encode In-
tended Destination on a Maze with Multiple Choice Po-
ints,” Journal of Neuroscience, Vol. 27, No. 36, 2007, pp.
9769-9779. doi:10.1523/JNEUROSCI.2011-07.2007
[145] C. R. Breese, R. E. Hampson and S. A. Deadwyler, “Hip-
pocampal Place Cells: Stereotypy and Plasticity,” Journal
of Neuroscience, Vol. 9, 1989, 1097-1111.
[146] P. J. Best and A. M. White, “Hippocampal Cellular Acti-
vity: A Brief History of Space,” Proceedings of the Na-
tional Academy of Sciences, Vol. 95, No. 6, 1998, pp.
2717-2719. doi:10.1073/pnas.95.6.2717
[147] P. J. Best, A. M. White and A. Minai, “Spatial Processing
in the Brain: The Activity of Hippocampal Place Cells,”
Annual Review of Neuroscience, Vol. 24, No. 1, 2001, pp.
459-486. doi:10.1146/annurev.neuro.24.1.459
[148] T. Kobayashi, H. Nishijo, M. Fukuda, J. Bures and T.
Ono, “Task-Dependent Representations in Rat Hippocam-
pal place Neurons,” Journal of Neurophysiology, Vol. 78,
No. 2, 1997, pp. 597-613.
[149] E. J. Markus, Y. L. Qin, B. Leonard, W. E. Skaggs, B. L.
McNaughton and C. A. Barnes, “Interactions between
Location and Task Affect the Spatial and Directional
Firing of Hippocampal Neurons,” Journal of Neurosci-
ence, Vol. 15, No. 11, 1995, pp. 7079-7094.
[150] I. Lee and J. Kim, “The Shift from a Response Strategy to
Object-in-Place Strategy during Learning Is Accompanied
by a Matching Shift in Neural Firing Correlates in the
Hippocampus,” Learning & Memory, Vol. 17, No. 8, 2010,
pp. 381-393. doi:10.1101/lm.1829110
[151] P. J. Kennedy and M. L. Shapiro, “Motivational States
Activate Distinct Hippocampal Representations to Guide
Goal-Directed Behaviors,” Proceedings of the National
Academy of Sciences, Vol. 106, No. 26, 2009, pp. 10805-
10810. doi:10.1073/pnas.0903259106
[152] L. Zinyuk, S. Kubik, Y. Kaminsky, A. A. Fenton and J.
Bures, “Understanding Hippocampal Activity by Using
Purposeful Behavior: Place Navigation Induces Place Cell
Discharge in Both Task-Relevant and Task-Irrelevant Spa-
tial Reference Frames,” Proceedings of the National
Academy of Sciences, Vol. 97, No. 7, 2000, pp. 3771-3776.
[153] M. A. Moita, S. Rosis, Y. Zhou, J. E. LeDoux and H. T.
Blair, “Putting Fear in Its Place: Remapping of Hippo-
campal Place Cells during Fear Conditioning,” Journal of
Neuroscience, Vol. 24, No. 31, 2004, pp. 7015-7023.
[154] E. R. Wood, P. A. Dudchenko, R. J. Robitsek and H.
Eichenbaum, “Hippocampal Neurons Encode Information
about Different Types of Memory Episodes Occurring in
the Same Location,” Neuron, Vol. 27, No. 3, 2000, pp.
623-633. doi:10.1016/S0896-6273(00)00071-4
[155] A. J. Robison and E. J. Nestler, “Transcriptional and Epi-
genetic Mechanisms of Addiction,” Nature Reviews Neu-
roscience, Vol. 12, No. 11, 2011, pp. 623-637.
[156] S. S. Schreiber and M. Baudry, “Selective Neuronal Vul-
nerability in the Hippocampus—A Role for Gene Expres-
sion?” Trends in Neurosciences, Vol. 18, No. 10, 1995,
pp. 446-451. doi:10.1016/0166-2236(95)94495-Q
[157] V. V. Sherstnev, V. V. Iurasov, Z. I. Storozheva, M. A.
Gruden and N. E. Iakovleva, “Biochemical Markers of
Apoptosis in Different Brain Regions after Training,” I.P.
Pavlov Journal of Higher Nervous Activity, Vol. 55, 2005,
pp. 729-733. (In Russian)
[158] C. H. Bailey and E. R. Kandel, “Structural Changes Ac-
companying Memory Storage,” Annual Review of Phy-
siology, Vol. 55, No. 1, 1993, pp. 397-426.
[159] S. Pulipparacharuvil, W. Renthal, C. F. Hale, M. Tanigu-
Copyright © 2013 SciRes. JBBS
Copyright © 2013 SciRes. JBBS
chi, G. Xiao, A. Kumar, S. J. Russo, D. Sikder, C. M.
Dewey, M. M. Davis, P. Greengard, A. C. Nairn, E. J.
Nestler and C. W. Cowan, “Cocaine Regulates MEF2 to
Control Synaptic and Behavioral Plasticity,” Neuron, Vol.
59, No. 4, 2008, pp. 621-633.
[160] V. Deroche-Gamonet, D. Belin and P. V. Piazza, “Evi-
dence for Addiction-Like Behavior in the Rat,” Science,
Vol. 305, No. 5686, 2004, pp. 1014-1017.