ca1 pyramidal cells organize an episode by segmented and ... · 1 . 1 . ca1 pyramidal cells...
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CA1 pyramidal cells organize an episode by segmented and ordered events 1
Chen Sun1, *, Wannan Yang3, Jared Martin1, & Susumu Tonegawa1, 2, * 2
1RIKEN-MIT Laboratory for Neural Circuit Genetics at the Picower Institute for Learning and Memory, Department 3
of Biology and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, 4
Massachusetts 02139, USA 5
2Howard Hughes Medical Institute at Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A. 6
3School of Biological Sciences, The University of Edinburgh, Edinburgh EH8 9YL, UK 7
*To whom correspondence should be addressed: C.S. ([email protected]) or S.T. ([email protected]) 8
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ABSTRACT 10
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A prevailing view is that the brain represents episodic experience as the continuous moment to 12
moment changes in the experience. Whether the brain also represents the same experience as a 13
sequence of discretely segmented events, is unknown. Here, we report a hippocampal CA1 14
“chunking code”, tracking an episode as its discrete event subdivisions (“chunks”) and the 15
sequential relationships between them. The chunking code is unaffected by unpredicted 16
variations within the events, reflecting the code’s flexible nature by being organized around 17
events as abstract units. The chunking code changes accordingly when relationships between 18
events are disrupted or modified. The discrete chunking code and continuous spatial code are 19
represented in the same cells, but in an orthogonal manner, and can be independently perturbed. 20
Optogenetic inactivation of MEC inputs to CA1 disrupts the chunking but not spatial code. The 21
chunking code may be fundamental for representing an episode, alongside codes tracking 22
continuous changes. 23
24
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Main text 25
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How is an episode represented in the brain? It has long been thought that the hippocampus (1) 27
encodes the space, objects, and time (2-4) in daily episodes. Early studies revealed hippocampal 28
cells that code for variations in space (5). Subsequently, hippocampal CA1 cells have been found 29
to be tuned to variations of non-spatial modalities such as passing time (6, 7) and variations in 30
sensory stimuli (6-11). As a result, a unified view of the hippocampus has emerged as a sequence 31
generator that encodes an episode by tracking its moment-to-moment continuous variations in 32
variables such as space, passing time, and sensory stimuli. 33
34
In parallel, psychologists and others, have theorized that episodes are fundamentally subdivided 35
by the brain into events or chunks (2, 4, 8-11). Indeed, in real life, rather than remembering all 36
the moment-to-moment continuous variations in their spatio-temporal domain, a typical episode, 37
for instance attending a dinner party, is remembered in terms of segmented events: being led to 38
their designated table, ordering from the menu, waiting for the food, enjoying the jazz band, 39
doing this, and then doing that, etc. An every-day analogy to understand the importance of 40
chunking is to consider the use of folders to organize all the raw documents and data that resides 41
in the computer. Without the widespread use of folders to subdivide computer data into chunks, 42
the organization of data would be an unworkable mess. In other neural systems, visual scenes are 43
segmented by the brain at the highest processing level into discrete objects (12, 13), and motor 44
sequences produced by the brain rely on discrete motor sub-programs (14, 15). These examples 45
reflect discretization as a general organizational principle of both the computer, and perhaps the 46
brain. 47
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48
Whether episodic experience, too, is fundamentally segmented by the brain into chunks is not 49
known. The present study aims to investigate whether the hippocampal CA1 subfield carries a 50
code organized around events as fundamental units of episodic experience, supported by the 51
sequentially ordered relationships between these events. Although episodic experience is 52
behaviorally continuous, a chunking process in the brain should allow it to flexibly and 53
efficiently code wide variations in the episodic experiences by organizing around meaningful 54
events, above every moment’s detail. Such an event tracking code could be one of the 55
fundamental organizing principles of episodic experience by the brain. 56
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Task design to study segmentation of episodic experience into events and relations between 58
them: revealing the “chunking code” 59
60
An ordinary episodic experience contains spatial and object variations (Fig. 1a top, middle). We 61
designed our behavior task to be a ‘skeletal’ version of ordinary episodic experience: a sequence 62
of events stripped of differences in spatial and sensory variations to minimize their influence on 63
episodic representations (Fig. 1a, bottom). In our task episode, mice repeatedly ran through a 64
square maze with four laps per trial (Fig. 1d) driven by delivery of a reward at the onset of lap 1 65
only. These mice visited the reward box after every lap regardless whether a reward was delivered 66
or not (Extended Data Fig. 1a) before starting the next lap. There were two experimental purposes 67
for this task design. First, despite the task experience being continuously run, we aimed to see 68
whether a neural code could be found that organized this continuous episode (Fig. 1a top) based 69
on what may be regarded as subdivisions of the task experience: the lap events (Fig. 1a bottom). 70
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By design, such a neural code would not be tracking different sensory cues since sensory 71
information was identical between the laps, unlike tasks used in other studies (5-7, 16-26) in which 72
different events reflect different, constant sensory presentations of visual cues, tones, or odors 73
(Illustration Fig. 1a middle). Rather differently, in our task design, the lap events would reflect a 74
more abstract entity, as we will show. Second, having established events as fundamental 75
organizing units of episodic experience, in our particular task the lap events would then reflect a 76
further special property within the experience. Since each of the four lap events was materially 77
identical to one another, if hippocampal CA1 neurons differentially code lap 1, lap 2, lap 3, and 78
lap 4, it would be a code that reflects the abstract sequential relationships between otherwise 79
identical events. To illustrate, representing lap n reflects the pure, iterative relationship to the 80
previous lap n-1 and the subsequent lap n+1. Such a representation of abstract sequential 81
relationships would reveal an organizational scheme of episodic experience based on these events. 82
83
A virus expressing the calcium indicator GCaMP6f (AAV2/5-Syn-DIO-GCaMP6f) (27) was 84
injected into dorsal CA1 (dCA1) of the hippocampus in Wfs1 (Wolframin-1) promoter-driven Cre 85
transgenic mice (Fig. 1b) (28, 29). A microendoscope was implanted above dCA1 (30) to enable 86
long-term calcium imaging in freely moving mice (Fig. 1b, c). We recorded calcium activity and 87
characterized the spatial selectivity of CA1 neurons (Extended Data Fig. 1c) as mice ran the square 88
maze task (Fig. 1d). During testing, animals completed 15-20 trials in succession. On average, test 89
mice took 98 seconds to complete one trial (Fig 1e). For each neuron during each of the four laps, 90
we calculated its average calcium activity strength during moving periods (> 4 cm/s) to identify 91
differences in calcium dynamics that were related to the lap number (Fig. 1g). Some neurons were 92
found to be most active during reward consumption (lap 1) in the reward box (Extended Data Fig. 93
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1d: representative neurons); these cells were excluded from further analysis because they were 94
active in direct response to the reward (Methods). In general, neurons that were active in the start 95
box during non-rewarded laps, or in the maze, were active at the same location on every lap 96
(Extended Data Fig. 1b, e), but showed a preference for a specific lap (Fig. 1g) with stronger 97
activity than in other laps. We calculated and plotted the trial-by-trial calcium activities of 98
representative neurons for each of the four laps. Representative neurons preferred a particular lap, 99
reliably across individual trials (Fig. 1g, Extended Data Fig. 2). Two representative cells that were 100
observed together in the same animal during the same experiment were active in the same spatial 101
location on the track, but were preferentially and consistently active on different laps (Fig. 1g: lap 102
1 vs lap 2 cell from animal 197; Extended Data Fig. 2: lap 2 vs lap 4 cell from animal 285). 103
104
We calculated the calcium activity across the four laps within spatial bins that tiled the maze 105
(Methods). Since CA1 activity is sensitive to a variety of behavioral variables including spatial 106
location (5), running speed (31, 32), and head direction (32, 33) (Extended Data Fig. 3a-b), we 107
fitted the activity of each neuron to a linear model incorporating the animal’s spatial location, head 108
direction, and running speed (Methods) to account for these variables. We then asked whether 109
these modelled variables were enough to account for the systematic variation in CA1 activity. Thus, 110
we calculated the remaining calcium activity across four laps that was not accounted for by the 111
model and referred to this activity to as ‘model corrected’, or MC, calcium activity (Fig. 1h). Thirty 112
percent (1055/3506 cells, n = 14 mice) of CA1 pyramidal cells had peak, lap-specific MC activity 113
that was significantly different (outside the 95th confidence interval) compared to shuffles. These 114
cells are henceforth called ‘chunking cells’ because their activity is modulated by the lap events 115
(i.e. the chunks) that make up the episode. 116
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There were chunking cells that preferred each of the four laps (Fig. 1i). In order to examine the 118
pattern of calcium activity modulated by lap for a given cell, we calculated its lap modulated MC 119
activity in the peak spatial bin (Fig. 1h, bottom; Methods) for all four laps; we call this sequence 120
of differential activity strengths across the four laps the ‘chunking code’ (Fig. 1h, bottom). The 121
percentage of chunking cells increased from 17% on the first day to 29% following eight days 122
training on the lap task (see Methods; pre: 176/1008 cells in n=5 mice; post: 335/1168 cells in the 123
same mice; Extended Data Fig. 4; χ2 =37.9, p = 7.4*10-10) showing that the chunking code is 124
learned. We tracked chunking cells across days and saw that their chunking codes were highly 125
correlated even across days (Fig. 1l, Examples: Fig. 1j-k;—Extended Data Fig. 5a for the 126
analogous raw ΔF/F results). 127
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The chunking code is unaffected by variations within events 129
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Despite the fact that animal behavior was continuous throughout the task and without experimental 131
breaks, several key results indicated that the chunking code was organized around discrete 132
subdivisions of the episodic experience (i.e. laps). First, although chunking cells were periodically 133
active on each of the 4 laps (Fig. 1f, Extended Data Fig. 1b) the vast majority of them had 134
statistically significant enhanced activity, according to the above criterion, on only one of those 135
laps (Fig. 2a left), indicating relatively sharp lap-specific tuning. 136
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Second, since previous studies (5-7, 17, 34) showed hippocampus codes for continuously changing 138
variables, we investigated the chunking code compared to several continuous variable codes more 139
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closely. Could chunking cells instead be tracking a particular duration of time since the start of the 140
trial? In fact, episodic or time cells (6, 7) require a constant and reliable temporal delay period; 141
otherwise they do not arise (7). Because the animals in our task were freely behaving and exhibited 142
unpredictable and variable durations to complete the trials of the task (Fig. 2b), they were unlikely 143
to be time cells. Could chunking cells instead be representing the total distance continually 144
travelled along the course of the 4-lap task since the start of the trial? To test this, we conducted a 145
consecutive two-day experiment in which we elongated the maze in one dimension to twice the 146
usual length on the 2nd day while the animals continued to undergo the standard 4-lap-per-trial task 147
(Fig. 2c, Methods: task specific training). The chunking code was significantly preserved across 148
days (Fig. 2d, Examples: Fig. 2e-f; raw ΔF/F: Extended Data Fig. 5b) despite the spatial distortions 149
of maze length, making it unlikely that chunking cells track the continuous distance traveled. 150
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Finally, real episodic experience has a high degree of variability. To investigate this point further, 152
we next performed a single day experiment with the 4-lap-per-trial task in which the maze was 153
elongated on pseudo-randomly chosen laps of pseudo-randomly chosen trials (Fig. 2g left, 154
Extended Data Fig. 6 for full task schedule, Methods). This maze was largely stripped of 155
predictability in travelled distance but preserved only the 4-discrete lap structure. A total of 26% 156
of CA1 cells (331/1257 cells, n = 6 animals) active in all trial types of this experiment were 157
significant chunking cells. For these chunking cells, their sequence of lap-to-lap pattern of activity 158
strengths (i.e. their chunking code) during the standard (short SSSS) trials was still preserved 159
during each of the pseudo-randomly elongated trial types (Fig. 2i-k; Example cell: Fig. 2h, raw 160
ΔF/F: Extended Data Fig. 5d). For these cells, their chunking codes were even preserved during 161
SSLL trials compared to LLSS trials (Fig. 2l), which were trials that had the same total distance 162
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(Fig. 2g right) but differed in their internal segmentation into four lap events. Therefore, the 163
chunking code of this sizeable population of CA1 cells was unperturbed by arbitrary and 164
unpredicted variations within the relevant lap or even variations within neighboring (preceding 165
and ensuing) laps (Fig. 2g right for illustration). These results further indicate that the chunking 166
code treats these lap events as organizing units of the experience. 167
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It can be seen that the chunking code tracks these lap events in an abstract manner, robust against 169
arbitrary variabilities in physical variables like time (Fig. 2b) or distance (Fig. 2g). We conducted 170
one final examination of the abstract nature of these events. A two day experiment was conducted 171
in which the 4-lap per reward was still preserved on the 2nd day (Extended Data Fig. 7, raw ΔF/F: 172
Extended Data Fig. 5e), whereas the spatial trajectories were altered every two laps (Extended 173
Data Fig. 7, Methods: task specific training) to perturb spatial sequences during the same lap 174
events. Here, a significant proportion of lap 1-4 chunking cells still had preserved chunking code 175
across sessions (Extended Data Fig. 7) despite the animal experiencing differential spatial 176
trajectories. This shows the abstract nature of these “lap events”, tracked by the chunking code. 177
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The chunking code tracks the sequential relationships between events 179
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Based around these lap events, how is episodic experience then organized? The reliable 181
preservation of lap event-specific activity across trials (Fig. 1g) and across days (Fig. 1l, Examples: 182
Fig. 1j-k) suggests that the chunking code captures sequential relationships between the materially 183
identical lap events. To further test this hypothesis, we conducted two types of experiments. 184
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First, we conducted an experiment in which reward was provided every lap, in order to abolish the 186
sequentially differentiating effect of the once-in-four lap delivery of the reward (Fig. 3a, left). We 187
found that only 9% (101/1072 cells, n= 5 animals, Fig. 3a right, and Fig. 3b) of CA1 cells were 188
significant chunking cells. This was significantly lower than the percentage of chunking cells 189
observed in the standard 4-lap-per-trial experiments in those same animals (28% = 371/1328 cells 190
in the same animals, χ2 =128.7, p = 0.0). 191
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Second, we conducted a consecutive two-day experiment with the standard 4-lap-per-trial 193
experiment on the first day and added a non-rewarded 5th lap on all the trials of the second day 194
before reward eating (Fig. 3c, Methods: task specific training). We postulated that this subtle 195
modification of the ordering of events may be sufficient to perturb the chunking code. During day 196
2 of testing, while lap 1 and 2 cells had preserved chunking code despite the additional lap (Fig. 197
3d; raw ΔF/F: Extended Data Fig. 5c), lap 3 cells were perturbed (Fig. 3d), and a significant 198
proportion of them (17/55 cells = 31%, n = 4 mice, p < 10-3 compared to shuffling) shifted to prefer 199
lap 4 (Fig. 3e-f). This was despite the fact that lap 3 and the preceding (lap 2) and succeeding laps 200
(lap 4) were materially identical to each other, as well as across days. Lap 4 cells showed a 201
similarly shifted chunking code to prefer lap 5 (Fig. 3g-h). Although, overall, the pattern of a 202
majority of Lap 4 cell activity across the two days was well correlated across the first 4 laps (Fig. 203
3d), a proportion of lap 4 cells (42/60 cells = 70%, n = 4 mice, p < 10-4 compared to shuffling) 204
were subtly perturbed. These data show a decrease in overall activity strength during lap 4 on day 205
2 (Fig. 3i) with a concomitant restoration of activity strength during lap 5 (Fig. 3j). Thus, the 206
alterations of the materially identical lap 3 and 4 representations were due to the addition of the 5th 207
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lap that changed the sequential structure of the task. Such alterations perturbed lap 3 and 4’s 208
prospective sequential relationships to the end of the trial. 209
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Interestingly, when the sequential organization of events was altered by the addition of this extra 211
lap, the chunking code was correspondingly altered as well, but it did so in a discrete, lap-specific 212
manner rather than gradually through the course of the task experience (Fig 3d). Indeed, whereas 213
lap 1 and 2 representations were well correlated across days, representations during lap 3 were 214
abruptly and discretely altered. This further supports the idea that the chunking code organizes 215
episodic experience around discrete segments—and even chunking code changes happen to occur 216
along these natural segments of the episode. 217
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Taken together, these data show that the chunking code organizes an episode around events and 219
reflects the abstract sequential relationships between them. 220
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Chunking and spatial codes are jointly but independently represented in the same cells 222
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What is the relationship between the chunking code for discrete events and the well-known place 224
code for continuously changing space (5)? The chunking and spatial codes occur in the same cells, 225
yet are treated in an orthogonal manner: the spatial code manifests as where on the spatial track 226
the neuron is active, and the chunking code manifests as how much the neuron is active, during 227
each different laps (Fig. 4a), without affecting the spatial tuning during each lap (Fig. 1f, Extended 228
Data Fig. 1b). Within this joint arrangement of the two codes, we further hypothesized that the 229
discrete chunking code could be manipulated independently from the continuous spatial code. 230
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When the maze and task were not altered in any way across days, both chunking and spatial 231
representations remained highly correlated (Fig. 4g). In contrast, perturbing the sequential 232
relationships between events by adding a lap (Fig. 3c) altered some chunking representations (Fig. 233
3d-f, Fig. 3k, orange histogram) but still preserved spatial representations in the same cells (Fig. 234
3k, blue histogram). 235
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A perturbation of the chunking code can also be seen in a different experiment. Since medial 237
entorhinal cortex (MEC) input into CA1 has been implicated in the sequential organization of 238
episodes (35-38), we asked how MEC inhibition might affect the chunking code versus the spatial 239
code. Based on these earlier studies, a virus expressing inhibitory opsin (AAV2/2-EF1a-DIO-240
eNpHR3.0-mCherry) was injected bilaterally into the MEC sub-region of pOxr1-Cre mice (Fig. 3l 241
left, 3m). In addition, a virus expressing the calcium indicator GCaMP6f (AAV2/5-CamKII-242
GCaMP6f- WPRE-SV40) was unilaterally injected into dorsal CA1 (dCA1) of the same mice (Fig. 243
3l left). An opto-endoscope was implanted above dCA1 to enable long-term calcium imaging as 244
well as optogenetic inhibition of the axonal terminals from MEC neurons in dCA1. The mice ran 245
28-40 trials of the 4-lap task where the trials alternated between inactivation (Light-On) and no 246
inactivation (Light-Off) (Fig. 3l). Inactivation of MEC cell terminals in dCA1 globally altered 247
chunking representations but did not change the spatial representations in the same cells (Fig. 3o-248
p, Example cells: Fig. 3n, Extended Data Fig. 8). Therefore, the chunking code is different from 249
the spatial code even though they are represented jointly in the same cells. 250
251
Can the converse result, spatial code alteration without chunking code alteration, be observed? To 252
investigate this point, we conducted a consecutive two-day experiment in which we rotated the 253
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maze by 90 degrees on the 2nd day while the animals continued to undergo the standard 4-lap-per-254
trial task (Fig. 4b, Methods: task specific training). In this case, the chunking code for lap 1, 2, 3 255
and 4 cells were all preserved across days (Fig. 4c, Fig. 4f, orange histogram; raw ΔF/F: Extended 256
Data Fig. 5f) despite the spatial rotation of the entire maze. The chunking code preservation was 257
in contrast to a change in spatial field location, relative to the maze, in the same cells (Fig. 4f, blue 258
histogram, Examples: Fig. 4d-e). In this way, the same cell can simultaneously code a different 259
spatial moment within the lap run, and still code the same lap event number. 260
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Thus, these experiments show that the hippocampal CA1 chunking code, manifested as event-262
modulated activity strengths, is jointly yet orthogonally represented in the same cells as the spatial 263
code, and the two codes can be manipulated independently of one another. 264
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The discrete chunking code is also jointly represented with the continuous time code 266
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If indeed the brain tracks episodic experience jointly via a discrete chunking code and a continuous 268
spatial code, would it continue to represent episodes in this dual manner even in an episode where 269
the main continuous changes are not spatial? To answer this question, we conducted another 4-270
lap-per-trial experiment using a continuous time code. In this experiment, the first arm of the 271
standard 4-lap-per-trial maze was replaced by a treadmill (Fig. 5a). Animals ran for 12s on a 272
treadmill at 14 cm/s on every lap. Monitoring activity of neurons on a treadmill obviates the 273
necessity of model corrections for running speed and head direction changes (Fig. 1h) because 274
they are nearly constant on the treadmill (Extended Data Fig. 9a-b). The treadmill experiences 275
gave rise to time-modulated cells (6, 7) (Fig. 5d) that tiled the 12s experience (Fig. 5b, Extended 276
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Data Fig. 9c), just as place cells had previously tiled the length of the maze run (Fig 1f). On the 277
treadmill, 20% of CA1 cells (243/1222 cells, n = 5 animals) had significantly different activity 278
depending on the lap number (i.e. the chunking code) (Fig. 5e, Methods, and examples: Fig. 5c, 279
Extended Data Fig. 9d). It can be checked that the chunking code of these cells during the treadmill 280
period was robustly preserved between trials (Fig. 5f). Note that the percentage of chunking cells 281
was significantly reduced during the control task where reward was given every lap (6% = 42/681 282
cells, n = 3 animals, Fig. 5g-i). These results show that the simultaneous tracking of episodes in 283
both a discrete manner and a continuous manner is a fundamental organizing principle, regardless 284
whether the episodic experience is primarily spatial or temporal in nature (Fig. 6). 285
286
DISCUSSION 287
288
How does the hippocampus encode an episodic experience? The main finding of this study was 289
the identification of a hippocampal code that organizes around discrete subdivisions of the episode 290
(i.e. events) and their sequential relationships to one another—the chunking code. While there has 291
emerged a view that the hippocampus tracks an episode as a sequence generator (5-7, 16-25, 39, 292
40) by tracking the moment to moment continuous variations for both spatial or non-spatial 293
moments, in this study the chunking code tracks the same episodic experience within the same 294
CA1 cells via a different organizing principle: organizing it around discrete chunks of experience 295
and their relationships (Fig. 2). In our task (Fig. 1d) the chunking of experience into events did not 296
require any differential sensory cues (5-7, 16-25, 39) to define an event or permit the distinction 297
of these events from one another, nor even require any differential past or future sensory cues (19, 298
25). Instead, these events had an abstract nature, unchanged by variations in distance travelled 299
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(Fig. 2c-f), time duration (Fig. 2b), changes in spatial trajectories (Extended Data Fig. 7), or even 300
spatial rotation of the entire episode (Fig. 4). Even when the chunking code changed (Fig. 3d), it 301
changed along the segments of the episode—in a discrete, lap-specific manner—rather than 302
gradually through the course of the task experience, showing that a lap event is treated as a discrete, 303
unitary entity by the chunking code. 304
305
In the end, organizing an episode based on the discrete chunking code tracking events and their 306
relationships, and a continuous code tracking moment to moment changes during the same episode, 307
coexist in the hippocampus. In fact, the discrete chunking code and continuous code are jointly 308
represented in the same cells for both spatial and non-spatial episodes (Fig. 4-5). This supports the 309
concept that the simultaneous tracking of episodes in both a segmented and continuous manner is 310
a fundamental and general encoding principle, which the hippocampus uses. Nevertheless, the two 311
codes are separate representations: in fact, the chunking code and spatial code can be 312
independently altered without affecting the other (Fig. 3k, 3p, Fig. 4f, 4g). In fact, the two joint 313
codes represent different aspects of the same episodic experience. Indeed, the tracking of 314
immediate experience within an event likely requires a level of detail that would be best served by 315
a continuous (spatial or non-spatial) neural representation. On the other hand, tracking the 316
meaningful episodic events above the moment-to-moment variational details is best served by a 317
flexible and discrete representation (Fig. 6). This study provides experimental evidence for a novel 318
“chunking code” in the hippocampus tracking events and their relationships, which could be a 319
fundamental code by which episodic experience is encoded efficiently and flexibly in the brain. 320
321
Acknowledgements 322
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We thank T. Kitamura, J. Z. Young, D. Roy, M. Wilson, E. Brown, M. Sur, M. Harnett, M. 323
Hasselmo, S. Muralidhar, B. Sun, J. Tao, N. Chen and C. MacDonald for comments; F. Bushard, 324
A. Hamalian, C. Ragion, C. Lovett, D. King, and Ella Maru Studio for technical assistance; L. 325
Brenner for paper preparation, and the members of Tonegawa lab for their support. This work 326
was supported by the RIKEN Center for Brain Science, the Howard Hughes Medical Institute, 327
and the JPB Foundation (to ST). 328
329
Author Contributions 330
C.S., and S.T. designed the study. C.S., W.Y., and S.T. interpreted the data. C.S. and J.M. 331
conducted the surgeries, behavior experiments, and computational analyses. C.S., W.Y., and S.T. 332
wrote the paper. All authors discussed and commented on the manuscript. 333
Data availability 334
The data and code that support the findings of this study are available from the corresponding 335
authors upon reasonable request. 336
337
338
REFERENCES AND NOTES 339
1. W. B. Scoville, B. Milner, J Neurol Neurosur Ps 20, 11 (1957). 340 2. I. Kant, (Encyclopedia Britannica, Chicago, 1955). 341 3. J. N. O'Keefe, L., (Clarendon Press, Oxford, 1978). 342 4. E. Tulving, (Academic Press, New York, 1972). 343 5. J. O'Keefe, J. Dostrovsky, Brain research 34, 171 (Nov, 1971). 344 6. C. J. MacDonald, K. Q. Lepage, U. T. Eden, H. Eichenbaum, Neuron 71, 737 (Aug 25, 2011). 345 7. E. Pastalkova, V. Itskov, A. Amarasingham, G. Buzsaki, Science 321, 1322 (Sep 5, 2008). 346 8. G. Buzsaki, R. Llinas, Science 358, 482 (Oct 27, 2017). 347 9. G. Buzsaki, D. Tingley, Trends in Cognitive Sciences 22, 853 (2018). 348
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10. A. Whitehead, Gifford Lectures Delivered in the University of Edinburgh During the Session 349 1927-28 (Free Press, 1979). 350
11. N. Rescher, G. Leibniz, Eds., (University of Pittsburgh Press, 1991). 351 12. J. Sergent, S. Ohta, B. MacDonald, Brain, (1992). 352 13. N. Kanwisher, J. McDermott, M. Chun, Journal of Neuroscience, 4302 (1997). 353 14. N. Fujii, A. M. Graybiel, Science 301, 1246 (2003). 354 15. H. K. Inagaki, L. Fontolan, S. Romani, K. Svoboda, Nature 566, 212 (2019). 355 16. T. A. Allen, D. M. Salz, S. McKenzie, N. J. Fortin, Journal of Neuroscience 36, 1547 (Feb 3, 2016). 356 17. D. Aronov, R. Nevers, D. W. Tank, Nature 543, 719 (Mar 29, 2017). 357 18. H. Eichenbaum, M. Kuperstein, A. Fagan, J. Nagode, Journal of Neuroscience 7, 716 (Mar, 1987). 358 19. L. M. Frank, E. N. Brown, M. Wilson, Neuron 27, 169 (Jul, 2000). 359 20. M. Fyhn, T. Hafting, A. Treves, M. B. Moser, E. I. Moser, Nature 446, 190 (Mar 8, 2007). 360 21. S. Leutgeb et al., Science 309, 619 (Jul 22, 2005). 361 22. J. R. Manns, M. W. Howard, H. Eichenbaum, Neuron 56, 530 (Nov 8, 2007). 362 23. Y. Sakurai, Neuroscience 115, 1153 (2002). 363 24. S. Terada, Y. Sakurai, H. Nakahara, S. Fujisawa, Neuron 94, 1248 (Jun 21, 2017). 364 25. E. R. Wood, P. A. Dudchenko, R. J. Robitsek, H. Eichenbaum, Neuron 27, 623 (Sep, 2000). 365 26. E. R. Wood, P. A. Dudchenko, H. Eichenbaum, Nature 397, 613 (1999). 366 27. T. W. Chen et al., Nature 499, 295 (Jul 18, 2013). 367 28. T. Kitamura et al., Science 343, 896 (Feb 21, 2014). 368 29. T. Okuyama, T. Kitamura, D. S. Roy, S. Itohara, S. Tonegawa, Science 353, 1536 (Sep 30, 2016). 369 30. Y. Ziv et al., Nat Neurosci 16, 264 (Mar, 2013). 370 31. A. Czurko, H. Hirase, J. Csicsvari, G. Buzsaki, Eur J Neurosci 11, 344 (Jan, 1999). 371 32. B. L. McNaughton, C. A. Barnes, J. O'Keefe, Exp Brain Res 52, 41 (1983). 372 33. S. Leutgeb, K. E. Ragozzino, S. J. Y. Mizumori, Neuroscience 100, 11 (2000). 373 34. P. Ravassard et al., Science 340, 1342 (2013). 374 35. A. Tsao et al., Nature 561, 57 (2018). 375 36. J. Suh, A. J. Rivest, T. Nakashiba, T. Tominaga, S. Tonegawa, Science 334, 1415 (2011). 376 37. T. T. G. Hahn, J. M. McFarland, S. Berberich, B. Sakmann, M. R. Mehta, Nat Neurosci 15, 1531 377
(2012). 378 38. J. Yamamoto, J. Suh, D. Takeuchi, S. Tonegawa, Cell 157, 845 (2014). 379 39. J. L. Gauthier, D. W. Tank. (bioRxiv, 2017). 380 40. Behrens, T. E. J. et al., Neuron 100, 490 (2018). 381 41. E. A. Mukamel, A. Nimmerjahn, M. J. Schnitzer, Neuron 63, 747 (Sep 24, 2009). 382 42. W. E. Skaggs, B. L. McNaughton, K. M. Gothard, in Advances in neural information processing 383
systems. (1993), pp. 1030-1037. 384
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Figure 1: Experimental design to study segmentation of episodic experience 387
a) Illustration of episodic experience as (top): sequence of continuous, moment to moment 388
variations (middle): sequence of discretely segmented events. (bottom): Skeletal task experience 389
stripped of sensory and spatial differences, still discretely segmented. 390
b) Implantation of microendoscope into dCA1 of Wfs1-Cre mice with AAV2/5-Syn-DIO-391
GCaMP6f virus injected in dCA1. 392
c) Top: Coronal section of hippocampus showing area of cortex aspiration (white line) and Wfs1+ 393
cells labelled (green). Bottom: ΔF/F calcium traces of Wfs1+ (pyramidal) cells in CA1, where 394
(red) denotes significant calcium transients identified. 395
d) During the standard 4-lap-per-trial experiment reward was delivered to the animal at the 396
beginning of lap 1 in the reward box, once every 4 such laps. 397
e) Mean run time among trials (n = 14 animals); 398
f) CA1 calcium activity sorted by spatial position and lap number, normalized and Gaussian 399
smoothed (σ = 25cm) (263 cells from example animal). Red label indicates reward box spatial bin, 400
and green label indicates the 100 cm long maze track. Reward box activity during lap 1 (reward 401
eating period) was excluded. 402
g) Trial-by-trial calcium activity of representative neurons preferring lap 1, 2, 3, and 4, with 403
spatially binned calcium activity along the track (reward box, plus 16 spatial bins along 100cm 404
track). Left panel: trial-by-trial calcium activity; Right panel: trial averaged calcium activity 405
(mean ±SD). Standard deviation was cut off at 0 because negative activity does not exist). 406
h) Left: representative neuron with raw calcium activity strength sorted by lap number (Light 407
blue), and plotted with calcium rate explained by the speed and head orientation fitted linear model 408
(Grey trace, See Methods). Right: Lap specific remaining calcium rate after the linear model was 409
subtracted, resulting in ‘model corrected’, or MC, calcium activity. Orange: peak spatial bin to 410
examine lap modulation of calcium activity 411
i) Summary statistics: Percentage of chunking cells in the whole CA1 pyramidal population that 412
were tuned to lap 1, 2, 3, or 4, in the standard 4-lap experiment (n = 14 animals). 413
j – k) Representative lap 1, 2, 3, and 4 neurons matched across 2 consecutive test days showing 414
spatial code (j) and chunking code (k), as measured by MC calcium activity. For each example 415
cell, Pearson correlations between its chunking codes across days were computed (k, bottom). 416
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l) Summary data: the chunking code for individual chunking cells, Pearson correlated across days, 417
plotted separately for lap 1, 2, 3 and 4 cell populations. (Orange): Chunking activity of each cell 418
on day 1 correlated with its own chunking activity on day 2. (Grey): Chunking activity of each 419
cell on day 1 correlated with chunking activity of arbitrary cells (i.e. shuffled cell identities) from 420
day 2. The proportion of cells with highly correlated (i.e. highly preserved) chunking code across 421
days (cells with Pearson’s r > 0.6 threshold: i.e. within the Blue box) was significantly greater 422
compared to shuffles: χ2 and p values shown in the figure (622 cells, n = 8 animals). See Methods 423
for detailed calculations. 424
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Figure 2: The chunking code is unaffected by variations within events 433
a) Most chunking cells had statistically enhanced activity on only one lap. 434
b) Left: Distribution of running time across trials of animal 197 during 18 trials; 435
Right: Coefficient of variation (σµ) for run time among trials, (n = 14 animals). 436
c) Fixed maze elongation experiment: 4-lap-per-trial task on: Day 1: the standard maze and Day 437
2: the elongated maze. 438
d) Chunking code correlations across standard and elongated maze sessions (448 cells, n = 6 mice). 439
See Fig. 1(l) for description and methods. 440
e– f) Representative lap 1, 2, 3, and 4 neurons matched across standard and elongated sessions. 441
g) Left: Task schedule design for the random maze elongation experiment, where laps were 442
randomly elongated every 2 laps. Right: Illustration of the random maze elongation experiment 443
with consistent 4-laps per reward despite variability within the lap events. S denotes a “short” lap. 444
L denotes a “long” lap. 445
h) Example lap 2 cell: its chunking code and spatial code during SSSS, LLSS, SSLL, and LLLL 446
trials (top to bottom respectively). 447
i—l) Chunking code correlations: (i) standard 4-lap trials (SSSS) vs LLSS trials, (j) SSSS vs SSLL 448
trials, (k) SSSS vs LLLL trials, or (l) SSLL vs LLSS trials. (331 cells, n = 6 mice). 449
N.S. denotes ‘non-significant’. See Fig. 1(l) for description and methods. 450
451
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453
Figure 3: Chunking code tracks the sequential relationship between events and relies on 454
entorhinal input 455
a) Left: Task schedule: reward was given to the animal every lap. Right: The percentage of 456
significant chunking cells was significantly reduced in the reward-every lap task, compared with 457
the same animals running the standard 4-lap-per-trial task (Blue lines: 5 mice, χ2 =128.7, p = 0.0). 458
b) Summary statistics: Percentage of chunking cells in the whole CA1 pyramidal population that 459
were tuned to lap 1, 2, 3, or 4, during the reward every lap experiment. 460
c) Lap addition experiment: Day 1: 4-lap-per-trial experiment and Day 2: 5-lap-per-trial 461
experiment. 462
d) Chunking code correlations across the 4-lap and 5-lap experiment sessions (382 cells, n = 4 463
mice). See Fig. 1(l) for description and methods. 464
e) Two representative neurons matched across 4-lap and 5-lap experiment sessions that 465
transformed from lap 3 to lap 4 preference. 466
f) Percentage of cells that transformed from lap 3 to lap 4 preference (Blue marks: 4 mice). 467
g - j) (g) Two representative neurons matched across 4-lap and 5-lap experiment sessions that 468
transformed from lap 4 to lap 5 preference. (h) Percentage of cells that transformed from lap 4 to 469
lap 5 preference (Blue marks: 4 mice). (i) MC activity of these cells from (h) during lap 4 on day 470
1 is significantly decreased during the same lap on day 2. (j) MC activity of these cells from (h) 471
during lap 4 on day 1 is not statistically different from MC activity during lap 5 on day 2 (Mean 472
±SEM, Paired student t-test conducted). 473
k) Pearson correlation of lap 3 chunking cells’ Blue: spatial code and Orange: chunking code 474
across days during the lap addition experiment. See Fig. 1(l) for description and methods. 475
l) Left: Viral injections permitting CA1 imaging and MEC terminal inhibition in CA1, 476
simultaneously Right: During the standard 4-lap-per-trial experiment, optogenetics light-On and 477
light-Off conditions alternated every 2 trials, for a total of 32-40 trials. 478
m) Coronal section of hippocampus showing area of cortex aspiration (white line) and MEC inputs 479
labelled (red). S.L.M. = stratum lacunosum moleculare. S.P. = stratum pyramidale. 480
n) Example lap 2 cell: chunking code and spatial code during the light-Off vs light-On trials. 481
o) Chunking code correlations across light-On vs light-Off conditions (182 chunking cells, n = 3 482
mice). 483
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p) Pearson correlation of Blue: spatial code and Orange: chunking code across light-On vs light-484
Off conditions, for same cells as in (o). 485
*** denotes p < 0.001, * denotes p < 0.05, N.S., not significant. 486
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505
Figure 4: Chunking and spatial codes are jointly represented yet independently 506
manipulatable 507
a) Illustration: chunking and spatial codes: jointly yet orthogonally represented in the same cells. 508
b) Rotation experiment: Day 1: 4 lap experiment; Day 2: same maze experiment, rotated 90 degrees 509
relative to external cues. 510
c) Chunking code correlations across un-rotated and rotated maze session (692 cells, n = 6 mice). 511
d – e) Representative lap 1, 2, 3, and 4 neurons matched across un-rotated and rotated maze 512
sessions. 513
f) Pearson correlation of spatial code and chunking code where the maze was 90 degree rotated to 514
external cues on Day 2 as (a) (617 cells, n = 5 mice). 515
g) Pearson correlation of spatial code and chunking code across days during the standard 4-lap 516
addition experiment (404 cells in the same 5 mice as (f)). 517
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Figure 5: The discrete chunking code is also jointly represented with continuous time code 519
a) Left: 4-lap-per trial experiment with 12s treadmill period on each lap. Right: cartoon of mouse 520
running during the treadmill period. The maze and door are not transparent in the task; shown 521
transparent here for illustration of the treadmill below. 522
b) CA1 calcium activity sorted by duration time (s) on the treadmill and lap number, normalized 523
and Gaussian smoothed (σ = 2s) (222 cells from example animal). 524
c) Trial-by-trial calcium activity of representative neurons preferring lap 1, 2, 3, and 4, temporally 525
binned calcium activity during the treadmill period (0.5s bins). Left panel: trial-by-trial calcium 526
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activity; Right panel: trial averaged calcium activity (mean ±SD). Standard deviation was cut off 527
at 0 because negative activity does not exist). 528
d) Trial-by-trial calcium activity of representative neurons that do not have lap preference 529
(ordinary time cells). 530
e) Summary statistics: Percentage of chunking cells in the whole CA1 pyramidal population that 531
were tuned to lap 1, 2, 3, or 4, in the 4-lap treadmill experiment (n = 5 mice) 532
f) Chunking code correlations across between even numbered trials and odd numbered trials (243 533
cells, n = 5 mice). See Fig. 1(l) for description and methods. 534
g) Task schedule: reward was given to the animal following every lap. Every lap contains a 12s 535
treadmill period. 536
h) Summary statistics: Percentage of chunking cells in the whole CA1 pyramidal population that 537
were tuned to lap 1, 2, 3, or 4, during the reward every lap experiment (with treadmill period). 538
i) The percentage of significant chunking cells was significantly higher during the treadmill period 539
of the 4-lap-per-trial task compared with the same animals during the reward-every lap task (Blue 540
lines: 3 mice, χ2 =65.0, p = 7.8*10-16). 541
*** denotes p < 0.001, * denotes p < 0.05, N.S., not significant. 542
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554
Figure 6: Illustration of a dinner episode 555
The dinner is organized by the chunking code around discrete events regardless of modality: 556
whether spatial (left event: being led the dinner table) or non-spatial (middle event: time waiting 557
for the food to arrive, right event: enjoying the jazz music) in nature. 558
559
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Extended Data Fig. 1: Spatial and Reward properties of CA1 cells on the maze. 561
a) Summary of mice running in a single session of the standard 4-lap-per-trial task. Mice did not 562
miss a visit into the reward box on any run. 563
b) CA1 calcium activity sorted by spatial position and lap number, normalized and Gaussian 564
smoothed (σ = 25cm) calcium activity (3506 cells, n = 14 animals). Red label indicates reward 565
box spatial bin, and green label indicates the 100 cm long maze track. Reward box activity during 566
lap 1 (reward eating period) was excluded. 567
c) Characterization of mean spatial properties of CA1 cells active in the lap maze: Left: sparsity, 568
and Right: spatial field size, plotted mean ± SEM; n = 14 mice. In total, 72% (2509/3506) of CA1 569
cells from 14 animals were significant place cells. 570
d—e) Spatially binned calcium activity along the track (reward box, plus 16 spatial bins along 571
100cm track) showing (d) 2 representative cells that were active in response to reward, and (e) 2 572
representative place cells that did not have lap modulated activity. Left panel: trial-by-trial activity 573
Right panel: trial-averaged activity with mean ± SD. Standard deviation was cut off at 0 because 574
negative activity does not exist. 575
576
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578
Extended Data Fig. 2: Lap 1-4 cells (more examples) 579
Representative lap 1, 2, 3, and 4 neurons, spatially binned calcium activity along the track (reward 580
box, plus 16 spatial bins along 100cm track). Left panel: trial-by-trial activity, Right trial averaged 581
activity with mean ± SD. Standard deviation was cut off at 0 because negative activity does not 582
exist. 583
584 585
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Extended Data Fig. 3: Model correction for Speed and Head direction modulations of CA1 587 cell activity 588
a) – b) Two representative cells with calcium activity level plotted against mean animal running 589 speed (top subpanels), and head direction tuning (bottom subpanels). r denotes Pearson’s 590 correlation. 591
c) Shuffling procedure preserves the mean calcium activity level as prescribed by the linear model 592 (See Methods) (r denotes Pearson’s correlation from 14 animals). 593
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Extended Data Fig. 4: The chunking code is learning dependent 612
Left: Experimental schedule for pre-trained vs post-trained animals on the standard 4-lap-per-trial 613
task. Right: The percentage of significant chunking cells was significantly less for pre-trained 614
comparing with post-trained in the same mice (Blue lines: 5 mice; χ2 =37.9, p = 7.4*10-10) 615
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625
Extended Data Fig. 5: Chunking code preservation across sessions as calculated using raw 626 ΔF/F activity 627
a) Left: The same representative lap 1, 2, 3, and 4 neurons matched across 2 consecutive test days 628
as Fig 1j-k above, measured by raw (i.e. non-model corrected) ΔF/F calcium activity. 629
Right: Pearson correlation of chunking code across days, calculated using raw ΔF/F activity. The 630
cells here were the same animals and experimental sessions as Fig. 1l above, plotted separately for 631
lap 1, 2, 3 and 4 cell populations. 632
b) – f) Pearson correlation of chunking code across sessions, calculated using raw ΔF/F activity, 633
for the (b) fixed maze elongation experiment from Fig. 2c-f, (c) lap addition experiment from Fig. 634
3c-k, (d) random maze elongation experiment from Fig. 2g-k, (e) spatial alternation experiment 635
from Extended Data Fig. 7, and (f) maze rotation experiment from Fig. 4b-f. The cells here were 636
the same animals and experimental sessions as the corresponding plots in the main figures, plotted 637
separately for lap 1, 2, 3 and 4 cell populations. 638
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Extended Data Fig. 6: Task schedule for the random maze elongation experiment 647
The first 3 consecutive trials of the random elongation experiment. Each animal underwent a 648 pseudorandom sequence of 28 consecutive 4-lap trials. Within these 28 trials, 7 trials took place 649 on the standard short maze (SSSS), and 7 trials were of each of the other 3 types SSLL, LLSS, 650 LLLL, where the maze was randomly elongated during L “long” laps. The entire 28 consecutive 651 sequence of trials was: SSSS, LLSS, LLLL, SSSS, SSSS, SSLL, SSLL, LLSS, LLLL, LLSS, 652 LLSS, SSLL, LLLL, LLLL, SSSS, LLLL, LLLL, LLSS, LLLL, LLSS, SSSS, LLSS, SSLL, SSSS, 653 SSSS, SSLL, SSLL, SSLL in this order. 654
S denotes a “short” lap and L denotes a “long” lap. 655
656
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658
Extended Data Fig. 7: CA1 chunking cells tracks abstract “lap events” despite differential 659
spatial trajectories 660
a) Alternation experiment: Day 1: standard 4-lap experiment and Day 2: alternating trajectory 661
version. 662
b) Chunking code correlations across the standard and alternating maze sessions (371 cells, n = 4 663
mice). See Fig. 1(l) for description and methods. 664
c-d) Representative lap 1, 2, 3, and 4 neurons matched across standard and alternating maze 665
sessions. 666
667
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668
Extended Data Fig. 8: Chunking cells during MEC inactivation (more examples) 669
Representative lap 1, 2, 3, and 4 neurons matched across Light-OFF and Light-ON trials. 670
671
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Extended Data Fig. 9: Chunking code during the treadmill period 673
a) Distribution of top: animal running speed, and bottom: animal head direction during the maze 674
running portion (purple) versus the treadmill running portion (yellow) of the task, for animal 481 675
in 20 trials. 676
b) Summary data: comparison of standard deviation of top: animal running speed (tstat = 19.65, 677
df = 4, p = 4.0 * 10-5), and bottom: animal head direction (tstat = 9.32, df = 4, p = 7.4 * 10-4) 678
during the maze running portion (purple) versus the treadmill running portion (yellow) of the task, 679
for 5 animals. 680
c) CA1 calcium activity sorted by spatial position and lap number, normalized and Gaussian 681
smoothed (σ = 2s) calcium activity (1222 cells, n = 5 animals). 682
d) Representative lap 1, 2, 3, and 4 neurons, temporally binned calcium activity during the 683
treadmill period (0.5s bins). Left panel: trial-by-trial activity, Right trial averaged activity with 684
mean ± SD. Standard deviation was cut off at 0 because negative activity does not exist. 685
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42
703
704
Extended Data Fig. 10: Method of image field of view registration across days 705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
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43
Materials and Methods 725
Animals 726
All procedures relating to mouse care and treatment conformed to the institutional and NIH 727
guidelines. Animals were individually housed in a 12 hour light (7pm-7am)/dark cycle. Nineteen 728
male Wfs1-Cre mice aged between 2-4 months were food restricted to 85-90% normal body weight 729
for the experiments. For each of the six main maze manipulation imaging experiments (fixed maze 730
elongation experiment, random maze elongation experiment, lap addition experiment, spatial 731
rotation experiment, treadmill experiment, spatial alternation experiment) the number of animals 732
used (at least 4) is indicated in the main text for each experiment. In each of these experiments, at 733
least two of these tested animals did not previously undergo any of the other manipulative 734
experiments. The other animals were experienced animals from the other manipulative 735
experiments. The exact number of animals used in each of these experiment is indicated in the text 736
directly. Three pOxr1-Cre mice, aged 2-4 months, were also implanted with Inscopix 737
microendoscope into CA1 for dual imaging and optogenetics experiments, and food restricted and 738
trained in the same manner as the Wfs1-Cre mice. 739
Histology and Immunohistochemistry 740
Mice were transcardially perfused with 4% paraformaldehyde (PFA) in phosphate buffered saline 741
(PBS). Brains were then post-fixed with the same solution for 24 hours, and brains were sectioned 742
by using a vibratome. Sections were stained by DAPI. Micrographs were obtained using a Zeiss 743
AxioImager M2 confocal microscope using Zeiss ZEN (black edition) software. 744
Preparation of Adeno-Associated Virus (AAV) 745
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44
The AAV2/5-Syn-DIO-GCaMP6f was generated by and acquired from the University of 746
Pennsylvania Vector Core, with a titer of 1.3*10^13 genome copy/ml. The AAV2/5-CamKII-747
GCaMP6f- WPRE-SV40 was generated by and acquired from the University of Pennsylvania 748
Vector Core, with a titer of 2.3*10^13 genome copy/ml. The AAV2/2-EF1a-DIO-eNpHR3.0-749
mCherry was generated by and acquired from the University of North Carolina (Chapel Hill) 750
Vector Core, with a titer of 5.3*10^12 genome copy/ml. 751
Stereotaxic Surgery 752
Stereotactic viral injections and microendoscope implantations were all performed in accordance 753
with Massachusetts Institute of Technology (MIT)’s Committee on Animal Care guidelines. Mice 754
were anaesthetized using 500 mg/kg avertin. Viruses were injected by using a glass micropipette 755
attached to a 10 µl Hamilton microsyringe through a microelectrode holder filled with mineral oil. 756
A microsyringe pump and its controller were used to control the speed of the injection. The needle 757
was slowly lowered to the target site and remained for 10 minutes after the injection. 758
For CA1 imaging experiments, unilateral viral delivery into the right CA1 of the Wfs1-Cre mice 759
was aimed at coordinates relative to Bregma: AP: -2.0 mm, ML, +1.4 mm, DV, -1.55 mm. Wfs1-760
Cre mice were injected with 300 nl of AAV2/5-Syn-DIO-GCaMP6f. Approximately one month 761
after injection, a microendoscope was implanted into the dorsal part of CA1 of the Wfs1-Cre mice 762
aimed at coordinates relative to Bregma, at: AP: -2.0 mm, ML, +2.0 mm, and DV at approximately 763
-1.0 mm. To implant at the correct depth, the cortex was vacuum-aspirated which resulted in the 764
removal of corpus callosum, which is visible under surgery microscope as fibers running in the 765
medial-lateral direction. The fibers of the alveus, which are visible as fibers running in the anterior-766
posterior direction, were left intact by the procedure. 767
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45
For CA1 optogenetic and imaging experiments, 300 nL (AAV2/5-CamKII-GCaMP6f- WPRE-768
SV40) unilateral viral delivery into the right CA1 of the pOxr1-Cre mice was aimed at coordinates 769
relative to Bregma: AP: -2.0 mm, ML, +1.4 mm, DV, -1.55 mm, and 300 nL (AAV2/2-EF1a-DIO-770
eNpHR3.0-mCherry) bilateral viral delivery into the MEC of these mice were aimed at coordinates 771
relative to Bregma: AP: −4.85 mm, ML, ±3.45 mm, DV, -3.35 mm. Following these virus 772
injections, the micro-endoscopy lens was implanted in the same manner for these dual optogenetic 773
and imaging experiments, as described above for CA1 imaging experiments. 774
The baseplate for miniaturized microscope camera was attached above the implanted 775
microendoscope in the mice. After experiments, animals were perfused, and post hoc analyses 776
were examined to determine actual imaging position in CA1 (Fig. 1c, Fig. 3m). 777
778
Apparatus description and Experimental conditions 779
The apparatus was a square maze 25 cm in length and width, with a 5cm wide track width, and 7 780
cm height. A 10 cm x 10 cm square reward box was located in one corner of the square maze. 781
Sugar pellets (Bio-Serve, F5684) were placed in the reward box at the beginning of lap 1 of each 782
trial. Four versions of this apparatus were used. Version 1 was used in Fig. 1-4 except Fig. 2. 783
Version 2 used in Fig. 2 had a length elongation to twice the standard length (50 cm = 2 ×25 cm), 784
but was otherwise identical to Version 1 in other dimensions. Version 3, used in Fig. 5’s treadmill 785
experiment, had a 18 cm long treadmill installed in the arm of the maze that immediately faces the 786
reward box (Fig. 5a). Version 3 otherwise used the same dimensions as Version 1. Version 4, used 787
in Extended Data Fig. 7, had an 8-maze configuration, with the other square of the 8-maze being 788
25 cm in length and width as well. Version 4 otherwise used the same dimensions as Version 1. 789
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46
All maze experiments were done on under dim light conditions, with prominent visual cues within 790
50 cm on all sides of the box. Ca2+ imaging in the maze lasted at least 20 minutes in order to 791
collect a sufficient number of Ca2+ transients to power our statistical analyses. The maze surface 792
was cleaned between sessions with 70% ethanol. Immediately before and after imaging sessions, 793
the mouse rested on a pedestal next to the maze. 794
The basic task in this manuscript is the standard 4-lap-per-trial task, where animals traversed round 795
a square maze 25cm in length (1m journey in total) (Fig. 1d). The task was designed so that a sugar 796
pellet reward was delivered manually to the reward box, at the beginning of lap 1, once every 4 797
such laps, which we call a single ‘trial’ (Fig. 1d). Identical motions were made on each lap, 798
regardless whether a pellet was delivered or not. During the testing, animals completed 15-20 of 799
such trials in repetitive succession without interruption. For any behavioral session in which the 800
animal missed going into the reward box more than once in the entire sequence of runs (15 to 20 801
x 4 = 60 to 80 runs in total), the experiment was excluded. Crucially, for all experiments, animals 802
first underwent task training before the final testing days. Training procedures are described below: 803
804
Habituation to reward in the maze: All behavior experiments took place during the animals’ 805
dark cycle. All implanted mice were habituated to human experimenters as well as the 806
experimental room for 1 week. At the same time, they were mildly food restricted and habituated 807
to sugar pellet reward. The criterion for habituation to sugar pellets and the maze was running 808
counter-clockwise around the maze and eating a sugar pellet in the reward port of the maze 809
(described below) in 15 successive repetitions without missing a single pellet. 810
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47
Reward periodicity-training: Animals were trained for approximately 8 days. If during any 811
training day, the mice appeared unmotivated or too satiated to complete the 15 trials, that training 812
day was repeated the following day. Animals were pre-trained for 2 days on the maze to habituate 813
to receiving sugar pellet rewards in the reward port: on each of these days, they did a 1-lap-per-814
trial task, that is, they receiving reward every run around the maze, and ran 15 such trials. For the 815
next 3 days (days 3-5), animals were trained to receive periodic rewards. On day 3, animals ran 15 816
trials of a 2-lap-per-trial task, that is, they receiving reward every 2 laps around the maze. On day 817
4, animals ran 15 trials of a 3-lap-per-trial task. On day 5, animals ran 15 trials of a 4-lap-per-trial 818
task. Finally, animals ran 3 more days (days 6-8), 15 trials per day of a 4-lap-per-trial task, before 819
they were considered well trained on the basic 4-lap-per-trial task. 820
Pre- versus Post-Training Experiment Protocol: In the particular case of the pre-versus post-821
trained animal experiment (Extended Data Fig. 4), animals that had only been habituated to the 822
reward (described above) were immediately tested/imaged by running 15 trials of the standard 4-823
lap-per-trial task. Following this initial testing, these animals then underwent the reward 824
periodicity-training (described above). Following periodicity-training, animals were tested/imaged 825
again on 15 trials of the standard 4-lap-per-trial task, to compare the chunking cells seen post-826
training, compared to pre-training. 827
Reward on every lap experiment: Animals in this experiment were given a sugar pellet on every 828
lap, completed a total of 60-80 laps total. This is equivalent to the total number of laps in the 15-829
20 trials of the 4 lap-per-trial experiment. This experiment did not require extra or task-specific 830
training. 831
832
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48
TASK-SPECIFIC TRAINING 833
Each of the main maze manipulation experiments (fixed maze elongation experiment, random 834
maze elongation experiment, lap addition experiment, spatial rotation experiment, treadmill 835
experiment, spatial alternation experiment) required its own special ‘task’ training after completing 836
the habituation and reward periodicity-training. 837
Fixed maze elongation experiment (Fig. 2): For the fixed maze elongation experiments, animals 838
were tested/imaged in a 2-day experiment. On Day 2, 2 hours prior to experimentation, animals 839
were habituated (allowed to run) for 3 minutes on the distorted maze without any rewards. 840
Random maze elongation experiment (Fig. 2): For the random maze elongation experiment, 841
animals were tested/imaged on 28 4-lap trials. The maze was elongated on random laps of random 842
trials, such that each of the 4 types of trials (SSSS, SSLL, LLSS, LLLL, where S denotes a “short” 843
lap and L denotes a “long” lap) were presented in pseudorandom order (Extended Data Fig. 6 for 844
full schedule) and appeared 7 times each within the 28 trials. Prior to test day, animals underwent 845
3 days (days (-3) to (-1)) of habituation training to the short and long laps, where SSSS, SSLL, 846
LLSS, LLLL trials were presented randomly. 847
Five-lap-per-trial experiment (Fig. 3): For the 5-lap-per-trial experiment, animals were 848
tested/imaged in a 2-day experiment. These animals underwent 3 days (days (-3) to (-1)) of 849
habituation training before the first test day. On the first 2 training days (day-3 to -2), animals each 850
day ran 15 trials of a 5-lap-per-trial task. On the 3rd day of training, (day (-1)) animals ran 15 trials 851
of a 4-lap-per-trial task again, to get them habituated to test day. 852
Optogenetics experiment (Fig. 3): Calcium imaging used the Inscopix nVoke miniature 853
optoscope, occurring at 20 Hz. During periods of optogenetic manipulation as defined by our 854
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49
protocol (Fig. 3l), the Inscopix nVoke miniature optoscope’s orange light (590-650 nm) 855
stimulation was turned on, at 10 mW/mm2 power, at a uniform and constant level. Orange light 856
delivery was done manually and was turned on or off at the start of the relevant trial as soon as 857
animals entered the box. 858
For optogenetic manipulation experiment, animals were tested/imaged in a single day. These 859
animals underwent 2 days of habituation training before the first test day with 2 days in between 860
each of the training days to allow recovery from the light. On each of the training days, animals 861
each day ran 16 trials of a 4-lap-per-trial task with the light schedule according to the alternating 862
schedule shown in Fig. 3l. 863
Spatial rotation experiment (Fig. 4): For the spatial rotation experiment, animals were 864
tested/imaged in a 2-day experiment. On Day 2, 2 hours prior to experimentation, animals were 865
habituated (allowed to run) for 3 minutes on the distorted maze without any rewards. 866
Treadmill experiment (Fig. 5): For the treadmill experiment, animals were tested/imaged in a 867
single day. These animals underwent 6 days of habituation training running on the maze before 868
the first test day. On the first day of training, animals ran 15 trials of a 1-lap-per-trial task. During 869
each lap, the animal ran onto the first arm of the square maze, and ran for 12s (time period 870
accurately indicated via Arduino) on the treadmill at a constant 14 cm/s, before running around 871
the rest of the square maze and entering the reward box. On the next five days of training, animals 872
ran 15 trials of a 4-lap-per-trial task again, with 12s on the treadmill, to get them habituated to test 873
day. 874
Alternation maze experiment (Extended Data Fig. 7): For the spatial alternation experiment, 875
animals were tested/imaged in a 2-day experiment. These animals underwent 5 days (days (-5) to 876
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50
(-1)) of habituation training before the first test day. On the first 4 training days (day-5 to -2), 877
animals each day ran 15 trials of a 4-lap-per-trial task where the laps alternated in their spatial 878
trajectories according to Extended Data Fig. 7a. On the 5th day of training, (day (-1)) animals 879
underwent ran 15 trials of an ordinary (non-alternating) 4-lap-per-trial task again, to get them 880
habituated to test day. 881
882
Behavioral analysis and Ca2+ events detection 883
The animal’s position was captured by an infrared camera (Ordro infrared camcorder, 30 fps) via 884
infrared light emitting diodes (LEDs) attached to the animal. Calcium events were captured at 20 885
Hz on an Inscopix miniature microscope. Imaging sessions were time stamped to the start of the 886
behavioral recording by the turning on of an LED that is fixed to the animal, at the beginning of 887
the session, and turning off of the LED at the end. 888
Analysis of the calcium images and extraction of independent neuronal traces were done akin to 889
previous methods (30, 41). Specifically, the calcium movie was then binned 4x spatially along 890
each dimension, and then processed by custom made code written in ImageJ (dividing each image, 891
pixel by pixel, by a low-passed (r = 20 pixels) filtered version). It was then motion corrected in 892
Inscopix Mosaic software 1.2.0 (correction type: translation and rotation; reference region with 893
spatial mean (r = 20 pixels) subtracted, inverted, and spatial mean applied (r = 5 pixels)). A spatial 894
mean filter was applied to it in Inscopix Mosaic (disk radius = 3), and a ΔF/F signal was calculated. 895
Four hundred (400) cell locations were selected from the resulting movie by PCA-ICA (600 output 896
PCs, 400 ICs, 0.1 weight of temporal information in spatio-temporal ICA, 750 iterations 897
maximum, 1E-5 fractional change to end iterations) in Inscopix Mosaic software. Region of 898
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51
interest (ROIs), half-max thresholded, that were not circular (if its length exceeded its width by > 899
2.5 times) or smaller than 5 pixels in diameter (~12 um), were discarded. For each ROI filter, 900
pixels less than 75% of the filter’s maximum intensity were zeroed. 901
ΔF/F calcium traces were calculated for the resulting ROI filters for each processed movie. Slow 902
variations in the calcium traces were eliminated by subtracting the median percentile ΔF/F value 903
at each timepoint, this value calculated from the calcium trace values ±15s within this timepoint, 904
similar to Ziv et al, 2013(30). The calcium trace was smoothed by 4-temporal bin rolling average 905
(each bin 50 ms). Significant calcium transients (Fig. 1c) were detected as traces that exceeded 3 906
Standard Deviations above baseline, and furthermore, remained above 1.5 Standard Deviations 907
above baseline for at least 500 ms. The rest of the ΔF/F calcium traced, aside from its significant 908
transients, were zeroed similar to Dombeck, 2010. Only cells that had a total of at least 25 909
significant transients during the entire session and nonzero activity in at least 10 trials separately 910
were considered for further analysis in this study. In the sole case of the treadmill experiment, a 911
lesser total of at least 10 significant transients was used, since the cumulation of all the treadmill 912
periods was only 12-16 min (15-20 trials). 913
914
Chunking cell calculation 915
A) Calcium event filtering 916
For each CA1 cell detected, the calcium activity was filtered so that only activity occurring while 917
the mice were in an active state (animal speed > 4 cm/s) were analyzed further. The behaviorally 918
tracked times of interest were also filtered in this way, considering only the times with animal 919
speed > 4 cm/s. The maze was divided into 9 spatial bins: the reward box (spatial bin of length and 920
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52
width 10 cm) was one spatial bin, and each of the 4 arm lengths of the maze were divided in half 921
(8 spatial bins, each of which was 12.5 cm in length and 5 cm in width). 922
Next, for each identified cell, individual calcium activity epochs were analyzed by calculating the 923
mean calcium activity in each of the 9 spatial bins during each individual lap across trials. Thus, 924
for a session of 15-20 trials, there were 540 to 720 calcium activity epochs in total (15 to 20 × 9 925
×4 = 540 to 720). 926
Each CA1 neuron possesses a spatially tuning, and in this model, the spatial tuning was captured 927
by a parameter p defined as the probability of having nonzero calcium activity in each separate 928
spatial bin. p was calculated for each neuron for each of its spatial bins. It differed for different 929
spatial bins, reflecting the spatial code. 930
Linear model fitting 931
For each activity epoch for each neuron, the mean ΔF/F calcium activity, the mean speed (s), and 932
the head direction tuning (o) were calculated. The nonzero calcium activity epochs were fit by a 933
linear regression of the mean ΔF/F calcium activity versus speed and head direction tuning. In this 934
regression, the coefficients a, b, c were fit: 935
𝑅𝑅[𝐶𝐶𝐶𝐶] ~ (𝐶𝐶 ∗ 𝐬𝐬 + b ∗ 𝐨𝐨 + c) [1] 936
Where R[Ca] is the mean ΔF/F calcium activity level of this neuron during this activity epoch. s 937
is the mean speed of the animal during this activity epoch, and o is the head orientation deviation 938
from the preferred head orientation of this neuron during this activity epoch. In Matlab code, we 939
used the function: fitrlinear with lambda = 0.01, to fit the equation [1] using regularized 940
linear regression applied to the calcium activity epochs of all cells. 941
942
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53
B) Identification of Chunking cells 943
For each identified cell, we shuffled its calcium transients across the lap epochs, such that the 944
probability of assigning any particular calcium transient into any particular lap epoch varied 945
according to equation [1]. Calcium transients were only shuffled (using randperm in matlab) 946
between different epochs taking place in the same spatial field in order to preserve p. We checked 947
that this shuffle generation procedure gave a mean ΔF/F calcium activity level that matched the 948
model-predicted (equation [1]) calcium activity level (Extended Data Fig. 3c). These shuffles 949
simulated the calcium activity of the cell explained by spatial field (p), head direction (o) and 950
animal speed (s). A total of 5000 such shuffles were computed, and a ‘model-explained mean ΔF/F 951
calcium activity level’ was computed as 952
𝑅𝑅𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚[𝐶𝐶𝐶𝐶, 𝐿𝐿 = 𝑖𝑖, 𝑆𝑆 = 𝑗𝑗] = 𝑚𝑚𝑚𝑚𝐶𝐶𝑚𝑚(𝑅𝑅[𝐶𝐶𝐶𝐶, 𝐿𝐿 = 𝑖𝑖, 𝑆𝑆 = 𝑗𝑗])𝑠𝑠ℎ𝑢𝑢𝑢𝑢𝑢𝑢𝑚𝑚𝑚𝑚𝑠𝑠 953
Where 𝑅𝑅[𝐶𝐶𝐶𝐶,𝑅𝑅 = 𝑖𝑖, 𝑆𝑆 = 𝑗𝑗] is the ‘model-explained calcium activity’ computed as the mean 954
activity in lap i and spatial bin j across all the shuffles for this cell. 955
For every neuron on all four individual laps, the model-explained mean calcium activity level in 956
each individual spatial bin was subtracted from the real mean ΔF/F calcium activity, to yield 957
‘model corrected’ (MC) ΔF/F calcium activity which excluded spatial, mean speed, and mean head 958
direction tuning (Fig. 1h). Thus, this model corrected effect would mainly reflect difference in 959
calcium activity due to lap number. 960
For every neuron, the model-explained mean ΔF/F calcium activity level was subtracted from the 961
mean ΔF/F calcium activity level obtained from the 5000 shuffles, to yield a distribution of MC 962
ΔF/F activities for chance level statistics. Cells whose peak, lap-specific MC ΔF/F was outside the 963
95th percent confidence of shuffled MC ΔF/F were called ‘significant chunking cells’. 964
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54
If the peak MC calcium activity happened to occur during the reward eating lap (lap 1) while the 965
animal was in the reward box spatial bin, then the peak MC calcium activity from the next highest 966
spatial bin was selected, because we excluded cell activity that was directly driven by reward 967
eating. 968
We also considered looking at raw ΔF/F activity of these chunking cells to examine if similar 969
results were obtained (Extended Data Fig. 5). 970
Spatial information 971
The tracked positions were sorted into 16 spatial bins of size 6.25cm x 5cm around the track and 972
4 spatial bins of size 5cm x 5cm in the reward box and the mean ΔF/F calcium activity of each 973
CA1 cell was determined for each bin. The bins which had animal occupancy < 100 ms were 974
considered unreliable and discarded from further analysis. Without smoothing, the spatial tuning 975
was calculated for each cell according to: 976
�𝑝𝑝𝑖𝑖𝜆𝜆𝑖𝑖 log2𝜆𝜆𝑖𝑖𝜆𝜆
𝑖𝑖
977
Where 𝜆𝜆𝑖𝑖 is the mean ΔF/F calcium activity of a unit in the i-th bin, 𝜆𝜆 is the overall ΔF/F calcium 978
activity, and pi is the probability of the animal occupying the i-th bin for all i. This formulation, 979
derived in Skaggs et al, 1993(42), was applied to calcium activity levels, which have a known 980
monotonic relationship to spike rates (Chen et al, 2013). All cells’ event times were shuffled 2000 981
times in an analogous manner to Wills et al, 2010 by shifting the calcium activity time series 982
around the position data by a random translation of > 20 s and less than the session duration minus 983
20s. Cells with significant spatial information were determined above the 95th percentile of all 984
shuffles. 985
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55
Registering cells across days 986
Our approach to register cells across days was to do so on the basis of the anatomy of the field of 987
view seen on both days (i.e. the pattern of blood vessels, etc.), rather than on the spatial locations 988
of cells directly (Extended Data Fig. 10). Then, after an appropriate image registration was found 989
for the fields of view based on anatomy, the ROIs on day 1 were identified, and calcium traces 990
were calculated based on the resulting ROI filters for day 1 applied directly to the processed movie 991
on day 2. To register two movies across days, a mean projection of the ImageJ filtered and motion 992
corrected movie (see above methods) on each day was computed, and these two movies were 993
registered with respect to one another by the Inscopix Mosaic motion correction software. 994
Chunking and Spatial correlations across days 995
For chunking correlations across days: for a given significant chunking cell on day 1, its chunking 996
code (defined in the main text) was concatenated into a vector. A similar vector was produced for 997
this same cell on day 2. This was done for each significant chunking cell matched across days. The 998
Pearson correlation between the day 1 chunking code vector and day 2 chunking code vector was 999
calculated to examine chunking code preservation across days. The day 2 chunking code vector 1000
was produced from the same spatial bin as day 1 to allow for direct chunking code comparison, 1001
except for the spatial rotation and spatial trajectory alternation experiment. In these cases the 1002
spatial bin in which peak activity occurred were calculated anew, since the space was substantially 1003
changed in these experiments relative to room cues. 1004
For spatial correlations across days: The raw calcium events, speed filtered (> 4 cm/s) were sorted 1005
into the 9 spatial bins defined above and the calcium activity level of each neuron was determined 1006
for each bin, and an activity map composed of all the spatial bins was produced. The activity maps 1007
.CC-BY-NC-ND 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted March 5, 2019. . https://doi.org/10.1101/565689doi: bioRxiv preprint
56
for each individual chunking cell was treated as a vector (list of numbers) and Pearson correlation 1008
between the spatial activity maps of the two days was calculated. 1009
Statistics 1010
All statistical tests in this study were two tailed. 1011
1012
.CC-BY-NC-ND 4.0 International licensecertified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprint (which was notthis version posted March 5, 2019. . https://doi.org/10.1101/565689doi: bioRxiv preprint