Thesis_ChalisaPrarasri

The Effect of Reward
Modulation on Place Cell
Coding in a Virtual Spatial
Navigation Task
Chalisa Prarasri, Ayaka Hachisuka, Pascal Ravassard,
Mayank Mehta!
Biomedical Research Thesis, Fall 2014!
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CHALISA PRARASRIBIOMEDICAL RESEARCH THESIS

Abstract!
It is thought that spatial navigation in mammals is represented, at least in part, in the
entorhinal cortex-hippocampal system. This region is one of the first to be affected in
Alzheimer’s disease, thus contributing to the disorientation seen in manifestations of the
disease. This symptom is not surprising, considering that the entorhinal cortex-hippocampal
system is hypothesized to contain cells that form an organism’s cognitive map. In particular,
‘place cells’ are hippocampal neurons whose activity profiles correspond to exact spatial
locations. It is yet unclear exactly how different environmental and internal cues contribute to
the various place cell properties that work with the rest of the entorhinal cortex-hippocampal
system to generate an organism’s cognitive map. One property, directionality, describes the
phenomenon by which certain place cells encode spatial locations differentially while a rat is
traveling along one direction in a 1-D environment compared to the opposite direction.
Interestingly, this phenomenon exists only in 1-D environments, as place cells tend to encode
a particular spatial location, regardless of direction of motion, in rats running in 2-D
environments. The aim of this study is to determine the effect of reward-based motivation on
the directional properties of hippocampal place cells by providing small rewards at one end
of a linear-track spatial navigation task, and relatively large rewards on the other end.
However, because it is extremely difficult to elucidate the contributions of different cues, an
immersive virtual reality (VR) system previously developed in the laboratory18 was used in
this study, allowing us to manipulate the contributions of different inputs more precisely than
is possible in real-world environments. Analysis of place cell recording data within this
experimental paradigm is currently underway. Preliminary results support our hypothesis
that reward-related inputs contribute to the directional coding of hippocampal place cells.!
Introduction!
Historical Overview!
Since the late 1950s, the hippocampus has been considered a key player in the
mechanisms underlying learning and memory. A crucial observation in this understanding of
the human brain came in 1957 with the famous patient H.M., who had both hippocampi
(along with adjacent structures) surgically removed to combat intractable epilepsy1. The
severe anterograde amnesia that H.M. experienced after the surgery had profound
implications for the role of the hippocampus and adjacent structures in human learning and
memory. !
CHALISA PRARASRI2BIOMEDICAL RESEARCH THESIS

In 1976, the field was furthered when John O’Keefe published a study describing a type
of hippocampal neuron whose firing activity appeared to correspond to exact spatial
locations2. This type of cell, dubbed a ‘place cell’ (Figure 1A), was first observed by O’Keefe
and Dostrovsky3 in behaving rats whose hippocampi had been implanted with micro-
electrodes to record the extracellular activity of individual neurons. Because of the
implications it had for how the brain encodes memories (particularly spatial memories), this
discovery was the impetus that launched an entirely new field of research focused on
understanding the brain’s ‘internal GPS’. In the decades following the observation of place
cells, other spatially selective cell types have been discovered in the hippocampus and
adjacent regions such as the entorhinal cortex. These include grid cells (Figure 1A), which fire
periodically to form a hexagonal grid over the entire region that the organism can move in
(Figure 1A), and head direction cells5, which fire with respect to an organism’s directional
heading. Together, these cells are thought to form the brain’s cognitive map — a hypothesis
that is corroborated by the discovery that the hippocampus is one of the first regions affected
by the progression of Alzheimer’s6, a disease which can manifest with deficits in spatial
navigation in its early stages7. !
Cellular Systems Underlying Spatial Navigation!
Further research has led to the supposition that spatial navigation in mammals is
composed of two interacting mechanisms: allocentric navigation (“map-based” navigation,
which reports an individual’s location based on external environmental cues such as visual
landmarks, olfactory cues, and auditory cues), and idiothetic navigation (“self-referenced”

navigation, which uses self-motion cues for path integration)8. These navigation systems are
thought to be integrated by the hippocampus and entorhinal cortex — both of which are
implicated in the processing of episodic memories.!
The mechanisms underlying the coding of specific locations have proven to be highly
intricate. Currently, research suggests that no one sensory modality controls place cell firing
in itself. For example, while moving distal visual landmarks around can lead a place cell to
reorient or resize its firing locations (called ‘place fields’) relative to the landmark, turning off
the lights may have little to no effect9 in a familiar environment. Further, place cells tend to
disproportionately represent goal locations10 and areas with denser sensory stimuli11. Much
evidence supports the belief that these place cell characteristics are a direct result of differing
inputs from external senses such as olfaction, vision, and audition, combined with internal
representations driven by inputs such as those from the proprioceptive and vestibular
systems12,13. Despite extensive research over the last few decades though, it is still unclear
exactly how the specific cell types in the hippocampal-entorhinal system work together to
form the cognitive map required for spatial navigation, and how the different senses are
integrated to form this map. !
One thing that does seem to be clear, however, is that subsets of place cells fire
differently when environments are altered14. Even minor sensory changes made to an
environment can lead place cells to change their firing rates, or even completely alter their
firing fields. The phenomenon, termed ‘remapping’, has been observed in two forms, each of
which is thought to represent different changes in environment. ‘Rate remapping’ occurs
when the firing rates of place cells change within the same place fields. This has been seen in
experiments in which testing enclosures in the same location were manipulated to have
different colors or different shapes14. ‘Global remapping’ occurs when place cells are seen to
change the locations of their place fields. This type of remapping is most easily induced by
moving the organism to a different spatial location, but has been seen to occur when current
environments are changed sufficiently, such as when the floor or enclosure textures and
geometrical shape are manipulated14. Interestingly, global remapping has also been observed
in response to changes in a rat’s motivation or expectation within the same environment,
such as when the type of food reward is changed, or when the task is altered from random
foraging to systematic goal-seeking. For instance, place cells have been observed to encode
for future goal-related trajectories15. It appears that rate remapping is well-suited for
encoding non-spatial changes in an environment, thus providing a putative mechanism for
episodic memory processing. Global remapping, on the other hand, may provide a

mechanism for distinguishing contexts, and even for distinguishing different conditions
within the same context16. !
Directional Properties of Place Cells!
To further complicate the puzzle of mammalian navigation, in 1983 McNaughton et al.
discovered a fascinating characteristic of place cells termed directionality11. Rodents running
in 1-dimensional tracks (linear tracks) were observed to have some place cells with fields
which fired differently in one running direction compared to the other. These cells, termed
unidirectional cells, exhibited uncorrelated activity between the two running directions on
the linear track. This is in comparison to bidirectional cells, which tend to have correlated
place fields in opposite journeys across the track (Figure 1B). Since place cells tend to encode
a particular spatial location, regardless of direction of motion, in rats foraging in 2-
dimensional environments, this finding has interesting implications for the role of goal-
oriented behavior in place cell coding. !
Investigating the Role of Limbic Inputs on Place Cell Directionality!
Despite the fact that the hippocampus receives many connections from other brain
areas that are not strictly sensory, the role of inputs not directly linked to an animal’s sensory
experience has yet to be explored as they relate to directional place cell coding. The
hippocampus is an integral part of a system that is considered the brain’s emotional and
motivational center — the limbic system. This region, which encompasses many brain
structures including the hippocampus, amygdala, nucleus accumbens, and ventral tegmental
area17, is responsible for linking sensory inputs with the appropriate behavioral outputs for a
given organism (Figure 1C). Thus, the limbic system may play a role in inducing the
directional properties observed in place cells during specific goal-oriented behavior. !
Experimental Design!
Given that the hippocampus is directly connected to other limbic structures, we
hypothesize that limbic inputs are relevant to the firing properties of hippocampal place cells.
The present study aims to test this hypothesis by recording place cells in the CA1 region of
the hippocampus while rats traverse a 1-D track in search of sequential but asymmetrically
sized rewards at each end. Two track environments will be used: one in which the walls are
symmetrical such that it is impossible to visually discern the two opposite directions of the
track, and one in which the walls are visually asymmetrical. The rat will receive
approximately 4 times the volume of reward on one end of the track compared to the other,
such that in the visually symmetric environment, the only spatially informative asymmetric
input will be reward volume. The control condition will consist of the same two visual

environments, but with equal reward volumes distributed at reward zones on both sides of
the track. !
Virtual Reality Systems in the Study of Spatial Navigation!
Despite careful controls, however, a large hindrance to experiments in this field is that it
is difficult to separate the different sensory inputs — distal visual cues, self-motion cues, and
local cues to determine their exact contributions to spatial navigation. One solution to this
hurdle is the use of a virtual reality (VR) simulation, as performing a navigation task in a
virtual reality does not make use of local cues (i.e. olfactory cues) or vestibular cues, but does
require visual cues and proprioception. Thus in this study, we used an immersive virtual
reality system, previously developed in the laboratory18, which allows the rat to perform
navigation tasks on a spherical treadmill in order to isolate the contributions of these sensory
inputs (Figure 1D).!
Due to these sensory differences between VR and real world (RW), place cell activity in
VR is markedly different from activity observed in RW environments. Previous studies in the
laboratory have shown that fewer place cells are active while a rat traverses a 1-D track in VR
compared to a visually similar track in real world, suggesting that the sensory modalities
missing in VR are necessary for full activation of the place cell population18. Further,
bidirectional cells in VR tend to encode less for absolute position, as they do in RW tracks —
instead code for a particular distance from the start position of each traversal along the
track. This property is called disto-coding, in contrast to the position-coding observed in RW. !
Significance!
This study will not only improve our understanding of the directional properties of
place cells, but also the role of different brain inputs (especially those related to reward and
motivation) on place cell coding. The observation that place cells can code for non-spatial
information has led many to hypothesize that what we primarily refer to as the brain’s
navigation system is in fact a comprehensive system for encoding declarative memories in
general. As mentioned previously, place cell activity and remapping provides a potential
mechanism for the processing of episodic memories in a context-dependent manner. Thus,
understanding the brain’s ‘internal GPS’ may be more significant than previously thought, as
it may contribute to the understanding of one of the brain’s most complicated and mysterious
functions — learning and memory.!
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Materials and Methods!
Subjects: !
•3 Male Long-Evans rats were
used in these experiments (1
behavioral test rat and 2
implanted rats).!
VR Setup: !
•A virtual reality (VR) setup
was used (Figure 2), as
previously described18. !
Behavioral Training in the VR
apparatus: !
•Handling (1 Week) — Rats
were handled daily to allow
them to get used to humans.!
• Harness Training (1 Week) — Rats were trained to wear a harness, used to
constrain them in the VR, for progressively increased durations (10-45 min).!
• Pavlovian Conditioning (1 Week) — Rats were conditioned to associate a reward
tone, used in the VR, with the delivery of a sugar water reward through a lick tube (50
µl of 10% sucrose per reward pulse, and 5 pulses per reward).!
• Ball Training (1 Week) — Rats were trained to walk on a ball (similar to the
spherical treadmill used in VR experiments) while constrained by a harness for sugar
water rewards delivered manually. !
• VR Training (2-5 Weeks) — Rats were harnessed in the VR (Figure 2A) and trained
to walk into reward zones (denoted by a black and white striped pillar hanging over a
white dot on the ground, (Figure 2B-C). As an incentive to run for the sugar water,
animals were kept water restricted on a daily schedule. They were trained sequentially
in each of the following navigation tasks of increasing difﬁculty: !
• “Random 5” 2D world — A hexagonal table (1 meter on a side) with 5 randomly
placed pillars. The visited pillars are teleported to a new random location to mimic
random foraging. !
• “Random 2” 2D world — A hexagonal table identical to Random 5, except 70 cm
on a side, with only 2 randomly placed pillars.!

• “Random 1” 2D world — Identical to Random 2, but with only 1 pillar.!
Behavioral Analysis:!
• Behavioral analyses were performed using snippets of MATLAB code previously
used in the laboratory, which, along with original snippets of code, were incorporated
into a program suitable for analyzing the tasks used in these experiments (C.P., A.H.,
P.R). Data for these analyses were collected by the VR program, which kept track of the
rat’s virtual speed, location, and heading, as well as times at which rewards were given.!
Spike Data Collection:!
• Two animals were bilaterally implanted with an implant carrying up to 24 tetrodes
targeting the CA1 region of the hippocampus to record extracellular activity. Local ﬁeld
potentials and spike data were collected with a Neuralynx system as previously
described18.!
• A baseline session was recorded in the “sleep box”, where the rat rested for 30-60
minutes before and after navigation tasks. These baselines are used as a control for spike
sorting, since only a fraction of place cells are active in a given environment while a
majority ﬁre during sleep.!
Spike Sorting Analysis:!

• “Clustering” — PyClust, a Python script, was used to semi-manually cluster
spikes in order to isolate unit activity (putative place cells and interneurons) based on
their waveform characteristics following automatic spike extraction.!
• Matlab functions previously written in the laboratory and modiﬁed for the
purpose of the current study were used to analyze unit activity.!
Ratemaps, Raster Plots, Place Fields:!
• Related plots were generated from the spike data as previously described18. !
Experimental Setup:!
• Environment: !
• The virtual environment consisted of a 2 m linear track centered in a square 3 x 3
m room with walls that were either symmetric or asymmetric along the axis of the track
(Figure 3). Each session consisted of 3 to 4 blocks of 15 trials within the same virtual
room. A trial was deﬁned as two runs across the track, one in each direction, such that
the rat received 2 rewards in total and ended at his original starting point. The visual
scene was instantaneously rotated by 180° after the reward was delivered, such that the
animals did not have to rotate the spherical treadmill by themselves to face the track in
the opposite direction to start the following journey. As a result, only the visual cues
were available to distinguish between both running directions; this could be achieved in
the asymmetrical but not the symmetrical environment (as both directions were visually
identical), as described bellow:!
• Experiment A (Asymmetric Rewards + Asymmetric Visual Cues)!
• This session was conducted in a visually asymmetric virtual world. During block
2, the rat received 8 reward pulses in the reward zone at one end of the track and 2
pulses at the other end. !
• Experiment B (Asymmetric Rewards + Symmetric Visual Cues) !
• This session was conducted in a symmetric virtual world. During block 2, the rat
received 8 reward pulses in the reward zone at one end of the track and 2 pulses at the
other end.!
• Experiment C (Symmetric Rewards + Asymmetric Visual Cues — Control) !
• This session was conducted in an asymmetric virtual world. During block 2, the
rat received 5 reward pulses in the reward zone at one end of the track and 5 pulses at
the other end.!
• Experiment D (Symmetric Rewards + Symmetric Visual Cues — Control) !

• This session was conducted in a symmetric virtual world. During block 2, the rat
received 5 reward pulses in the reward zone at one end of the track and 5 pulses at the
other end.!
Results!
One implanted Long Evans rat was run in each of experiments A-D, for 2-3 sessions per
experiment, and putative place cell unit activity was collected and preliminarily analyzed. !
Experiment A (Asymmetric Rewards + Asymmetric Visual Cues)!
To assess the effect of reward modulation on behavior, we analyzed the rat’s velocity as
a function of position while moving toward the ‘forward’ end of the track (toward the 8-
pulse reward in block 2) compared to the backward end (toward the 2-pulse reward in block
2). Results are presented for one example session (Figure 4). Although mean peak speeds
appear to be slightly greater in block 2, the difference is not outside of the range of standard
error. No signiﬁcant differences are observed between forward and backward runs, or
between blocks. Out of 127 CA1 excitatory cells recorded during 2 sessions of Experiment A,
54 units were track-active (i.e., 42% of the cells’ ﬁring rate exceeded 0.5 Hz on average for the

whole session during the running periods). Although the population of place cells has yet to
be analyzed as a whole, Figure 5 depicts the activity of two example unidirectional cells
observed during the experiment which showed global remapping in relation to the
experimental block. The first cell, which does not fire during block 1, is seen to fire
unidirectionally during blocks 2 and 3 with its place field in the forward reward zone (Figure
5A). The cell’s firing rate is markedly decreased in block 3 compared to block 2. The second
cell is observed to develop a place field centered at 180 cm along the track during block 2,
which continues into block 3 (Figure 5B). The Raster plot shows that the place field appears to
widen to a maximum of approximately 90 cm around trial 45. Figure 6 depicts the activity of
two unidirectional place cells that were observed to remap between blocks during the
experiment. The first cell, which exhibited unidirectional place fields in the backward
direction during block 1, is seen to partially remap during block 2 to include a new place field
just after the start of the forward journey, thus becoming more bidirectional, but with an
asymmetric pattern of activity (Figure 6A). Inspection of the Raster plot shows that a second

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place field appears in trial 18, 3 trials after reward modulation begins, and disappears again
in trial 31, just after reward modulation ceases. The second cell, which does not exhibit
activity during blocks 1 and 3, develops a unidirectional place field during block 2 that is
centered around 150 cm into the track in the backward direction (Figure 6B). The field
appears during trial 15, immediately after reward modulation begins, and disappears after
trial 32, a few trials after reward modulation stops. At current, all 5 cells observed during
Experiment A exhibited unidirectional place fields. One cell exhibited a place field that was
stable throughout the blocks (not shown), while 4 cells either partially or globally remapped. !
Experiment B (Asymmetric Rewards + Symmetric Visual Cues) !
Velocity analysis of one session in Experiment B reveals a slightly greater mean speed
as the rat runs away from the 8-pulse reward during block 2 (Figure 7). However, the
difference is not outside of standard error. !
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Out of 119 CA1 excitatory cells recorded during 2 sessions of Experiment, 49 units were
track-active (41%). Two analyzed units are illustrated in Figure 8, both of which were
bidirectional. The first cell exhibited clear, bidirectional place fields during block 1 at equal
distance from the starting position in both directions, which gradually narrowed and then
disappeared half-way through block 2 (Figure 8A). Since we define bidirectionality as any
spatially selective cell with a directionality index (Dir) that is less than 0.5, the second cell’s
activity appears to be bidirectional throughout the session (Figure 8B). However, during
block 2, we can observed a slight bias of the firing rate toward the forward direction (Dir =
-0.253) which indicates unidirectionality. Though only two of the analyzed cells were shown,
all 3 cells observed during Experiment B exhibited bidirectional place fields. One cell was
stably bidirectional throughout the task, while the other two were observed to globally or
place remap with respect to block 2. !
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Experiment C (Symmetric Rewards + Asymmetric Visual Cues — Control) !
Out of 181 CA1 excitatory cells recorded during 2 sessions of Experiment C and
baseline, 85 units were track-active (47%). Although cells active during this session have yet
to be statistically analyzed as a population, we observed the expected proportion of
bidirectional and unidirectional cells (~60 and ~40% respectively). We could also draw two
prevalent categories of particular cells: those with a gradual place field development and
firing rate increase over the course of the session, and those with place field instability. !
Two example unidirectional units are illustrated in Figure 9. The first cell (Figure 9A)
developed a unidirectional place field (Dir = .59 during block 4) toward the beginning of the
forward journey starting trial 19. The second cell (Figure 9B), also exhibits a place field on the
forward journey (Dir = 0.74 during block 4). Both cells exhibit unidirectional place fields and
increase both peak and mean firing rates over time (For cell 1: peak = 3.2 Hz, mean = 0.86 Hz
in block 1, peak = 22.1 Hz, mean = 2.9 Hz in block 4; for cell 2: peak = 14.7 Hz, mean = 2.9 Hz
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in block 1, peak = 31 Hz, mean = 3.5 Hz in block 4), and as a result the directionality index
improved (For cell 1: Dir. = -0.16 in block 1, Dir. = 0.59 in block 4; for cell 2: Dir. = -0.06 in
block 1, Dir. = 0.74 in block 4). !
Two example units with unstable fields are illustrated in Figure 10. The first cell (Figure
10A) develops a stable place field on the forward journey at the beginning of block 2, but also
exhibits an unstable second place field on the backward journey present during block 2 and
show robust firing during block 4. Consequently, the cell is unidirectional during blocks 2
(Dir = 0.5) and 3 (Dir = 0.77), and bidirectional during block 4 (Dir = 0.17). The second cell
exhibits an unstable unidirectional place field that appears around trial 7 and disappears
around trial 35. !
Out of the 4 cells analyzed for this session, 2 had fields that developed during the
session, 3 showed unidirectional place fields, and 2 were unstable.!
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Experiment D (Symmetric Rewards + Symmetric Visual Cues — Control) !
Out of 188 CA1 excitatory cells recorded during 2 sessions of Experiment D and
baseline, 70 units were track-active (37%). The properties of these cells also have yet to be
analyzed as a population, though some common characteristics were noted in these as well:
gradual place field development over the course of the session, a tendency toward
bidirectional position-coding, and place field instability.!
Two example bidirectional disto-coding cells are shown in Figure 11. The first cell
(Figure 11A) develops a bidirectional place field near the reward zones at the beginning of
block 2. The second cell (Figure 11B) was one of the few observed to exhibit a stable place
field throughout the session, with a bidirectional place field covering the second half of the
track in both directions.!
Two example unstable bidirectional cells are shown in Figure 12. The first cell (Figure
12A) exhibits a bidirectional place field that appears during block 2. The field initially
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encompasses a roughly 30 cm area near the reward zones, but shrinks over time to
encompass only the reward zones (15 cm in width). The second cell (Figure 12B) exhibits a
place field that is present at the start of the session in the second half of the track in both
directions similar to the cell in Figure 10B, but stops firing during block 3. Also, similar to the
cells shown in Figure 11, these cells are disto-coding.!
Out of the 4 cells analyzed for this session, 2 had fields that developed during the
session, 4 showed bidirectional place fields, and 2 were unstable.!
Discussion!
Our preliminary behavioral analyses suggest that rats do not run faster toward the
larger reward as could be expected. This indicates that the animal was able to complete the
whole task while maintaining the same pace throughout the session, and that his behavior
was stereotypical across trials and blocks, and more importantly, across directions. This is an
encouraging point to specifically address the effect of the reward modulation without
behavioral confounds, since place cell activity is known to be modulated by velocity19.
Analysis of other behavioral factors, such as mean speed, peak speed, time to complete a
journey, time to complete a trial, and inter-trial time is in progress. Additionally, it is unclear
whether this rat’s behavior in the presence of reward modulation is representative of most
rats, since we have previously demonstrated that individuals can exhibit large differences in
running speed (Figure 13). Thus, behavior analysis will be pooling several sessions from
more animals (implanted and unimplanted) to increase the significance of our preliminary
results. !
As shown in our results section, cells appear to exhibit more directionality during
Experiment A compared to Experiment B, as well as for Experiment C compared to
Experiment D (i.e., in both set of visually asymmetric environments). This result is expected,
as the running directions are visually different. On the other hand, the visually symmetric
environment do not provide any external cues as to which direction the rat is moving in,
(other than the experimental block 2 in task B). This conjecture explains the observation that
all 4 cells examined in Experiment D exhibited bidirectional place fields. !
It is still unclear whether there is a difference in directionality between the experimental
block and the other blocks in task B due to the asymmetrical reward. On the other hand in
task A, the activity of the cells shown in Figures 5 and 6 indicates some promise for the role of
reward modulation on directionality. These example cells indeed exhibit remapping
temporally related to the change of reward paradigm in block too. However with so few

examples it would be premature to conclude that the observed remapping was caused by the
experimental condition as opposed to coincidental instability. Future analyses will address
this question at the population level, and the remapping during this block will be compared
with the place field instability level observed during the control task. Indeed, although place
field formation is observed with respect to block two for a majority of cells in Experiments A
and B, these characteristics are also observed in our control experiments, C and D. This not
only proves the importance of controls other than our in-session ones (blocks 1, 3 and 4), but
also may call into question our experimental setup in which the experimental condition
occurs only 15 trials into the session. Out of the 8 cells examined from control experiments C
and D, 5 exhibited place fields that appeared during block 2 or afterward. Thus, more than
half of the cells we presented required 15 trials or more to even partially develop their place
fields, suggesting that at least some place cells require a significant amount of time to
properly integrate inputs to form their place fields. This experience-dependent activity (i.e.
developing over trials) presents a potential problem for our experiment, as the experimental
condition might need to start later during the session and require the animals to run
supplementary trials. Alternatively we could use block 3 as the experimental block, thus
giving more time to insure that the cells develop their place fields before applying the
experimental condition. This can be done for this particular task, as the rat we used usually
ran 4 blocks in each session, but this might be challenging for other animals with lower

motivation. In any case, this emphasizes the need for block-wise comparisons for
experimental and control sessions.!
Further, our observation that some place cells seem to remap with respect to block 2 in
both the controls (C and D) in addition to the reward modulation tasks (A and B) suggests
that place field instability may also present a second outcome for the interpretation of our
results. For instance, the least stable place fields are present for close to 15 trials, as seen in all
the cells shown in Figures 10 and 12. Fortunately, differences in place cell coding across
blocks may become more salient upon analysis of the full population of 55 track-active cells
recorded in Experiments A and B. We can also strengthen any results by further examining
remapping in the task proposed previously, in which the experimental condition occurs
during block 3 instead of block 2. If we were to observe differences in place cell activity when
the experimental condition occurs in block 3 compared to block 2, this would have interesting
implications for the role of experience-dependent activity on place cell coding. We then need
to evaluate the natural instability of place fields in control sessions to see whether the reward
modulation does produce significant remapping.!
Interestingly, out of the 4 cells examined from Experiment A, we observed that 2
exhibited greater activity in the forward reward zone,, while only 1 of the 4 had a greater
activity in the backward reward zone. This can be related to previous studies that have
shown that place cells tend to disproportionately represent reward zones20. If the location
associated with the larger reward recruits more place cells, it could reflect a greater
dopaminergic input to the hippocampus. Dopamine receptor activation has been shown to
increase LTP, thus strengthening or creating connections that may affect place cell activity21.
More interestingly, place fields are also modified outside the reward zones, which could
occur by the same mechanisms proposed above, and reflect reward prediction and
expectancy properties22.!
Despite these promising observations, it is important to keep in mind that these data
are preliminary and have not yet been analyzed at the population level. Our preliminary
results may reflect a choice-bias, as these 16 cells were chosen by the experimenter based on
their interest value — out of 258 total track-active cells across all sessions. Further, statistical
analysis must be performed before any conclusions can be drawn.!
The analysis of this data is still in progress. Although 2-3 sessions of each of
Experiments A through D have been recorded with one rat, the data have yet to be fully
processed and analyzed to determine population characteristics. Population data are
necessary to determine whether the changes in place cell coding observed with respect to

block 2 in Experiments A and B can actually be attributed to the reward modulation as
opposed to other factors, such as place cell instability. Cell directionality must be analyzed
across the population of track-active cells for each experiment in order to determine whether
reward modulation truly induces increased directionality, as hypothesized, and as suggested
by our preliminary results. This includes the directionality index within different blocks and
across sessions, which will allow us to determine whether the directional differences we
qualitatively see between sessions run in the visually asymmetric tasks compared to the
visually symmetric tasks are real. Further, we intend to analyze other place cell characteristics
across the population, such as the rate map correlation between different blocks within the
same session, in addition to the information content of cells within different blocks and across
sessions. !
! Additionally, more controls must be conducted to account for other potential
confounding factors. These include a task in which 2 pulses are given at the forward reward
zone, and 8 are given at the backwards reward zone. This task will help to rule out the
possibility that place cells simply behave differently on different sides of the visually
asymmetric environment, or in relation to the order in which rewards are received, as
opposed to reacting to the reward modulation. As data from at least 2 more rats is necessary
before more reliable conclusions can be drawn from these experiments, two rats have been
VR-trained, and one has been implanted and will perform the tasks once putative
hippocampal place cells have been isolated for recording.!
In the future, it could be of interest to create increased asymmetry in the reward
distribution in order to generate greater place cell unidirectionality than what we
preliminarily observed in a visually asymmetric environment. For instance, instead of or in
parallel to modulating reward amount, we could dispense different reward tastes, as rodents
have previously been shown to exhibit behavioral adaptations related to food preference23.!
Although conclusions drawn from these data are not strong due to the small sample
size and choice-bias, along with confounding factors such as the time required for place ﬁeld
generation and place ﬁeld instability, the presence of cells that were observed to change
behavior with respect to trials during which reward amount was modulated (block 2)
suggests that differences in reward amount can have an effect on place cell coding. This,
although meagerly and preliminarily, supports our hypothesis that there exist reward-based
inputs to the hippocampus, potentially originating in limbic structures, which can modulate
place cell coding.!

Acknowledgements !
Chalisa Prarasri designed the experiments, with the help of Pascal Ravassard. Ayaka
Hachisuka and Chalisa Prarasri wrote the XML code for the the task and debugged it.
Analysis was conducted by Chalisa Prarasri, Ayaka Hachisuka, and Pascal Ravassard. The
biomedical research minor at UCLA provided scientiﬁc counseling and advice. Grants came
from the Keck Foundation for Neurophysics. !
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