mteb/BrightProPsychologyRetrieval huggingface dataset:
To test whether hippocampal activity was required for this task, we
inactivated CA1 bilaterally by injecting mice with the GABA  A
(γ-aminobutyric acid type A) receptor agonist muscimol. Muscimol acutely
reduced licking selectivity to near-chance levels, whereas injection of saline
on neighbouring days did not impair behavioural performance (Fig.  1d,e
 and Extended Data Fig.  1b,e
 ). Hippocampal inactivation did not
appear to affect the engagement or motivation of mice, as mice continued to
traverse the track and lick at a similar rate (Extended Data Fig.  1c,d,f,g
 ). Additionally, even though the running
speed differed between muscimol and saline sessions, when we subsampled
sessions with similar mean running speed, the licking selectivity was still
significantly reduced in the muscimol sessions (Extended Data Fig.  1h–k
 ). Therefore, hippocampal activity
appears to be necessary for remembering the learned goal location.
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###  Virtual reality and behavioural hardware
Mice running on an air-supported ball traversed a linear 2-m-long track that
repeated in a circular topology in virtual reality (Fig.  1a
 ). To receive rewards, mice were required
to lick a spout within a 20-cm reward zone (Fig.  1a
 ). We quantified each mouse’s behavioural
performance as its licking selectivity, defined by comparing the number of
licks near the reward zone with the number of licks in an equally sized
portion of the track away from the reward zone. Novice mice licked uniformly
across the track and had near-chance licking selectivity (Fig.  1b
 ). Licking selectivity increased over
several days as mice learned the location of the reward, after which mice
maintained stable, high-selectivity performance (Fig.  1b,c
 ). In all experiments involving the task
below, we studied mice that were well trained and thus highly familiar with
the task and the environment.
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We also observed significant differences in the place coding of Fos-KO and
wild-type cells. Fos-KO cells had activity that was significantly less
spatially selective than that of nearby wild-type cells, observed as lower
spatial information in Fos-KO cells (Fig.  3h
 ). In addition, Fos-KO cells were less
likely to have a place field (Fig.  3i 
). Of the cells that had place fields, Fos-KO cells exhibited less reliable
activity from trial to trial, measured as the correlation in activity between
trials, and had activity in their place fields on a smaller fraction of trials
(Fig.  3j,k  ). These cells also had
higher activity outside their place field that resulted in decreased spatial
selectivity when compared with their wild-type neighbours (Fig.  3l
 and Extended Data Fig.  10d,e
 ).
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###   Extended Data Fig. 4 Method for detecting significant calcium
transients.
###  Behaviour task
(E) Correlations focusing on 25 cm around doors.
See also Figures S6 and S7.
##  Code availability
###   Extended Data Fig. 2 Detailed time course of changes in Fos-GFP
expression after behavior.
* Received  :  27 April 2021
**a** , Example field of view, with a zoomed-in image (right). **b** ,
Representative place cells imaged on consecutive sessions. Top, jRGECO1a and
fold-induction images. Bottom, average activity as a function of track
position. Shading indicates the 1st and 99th percentiles for permuted activity
relative to position. **c** , Example of place field stability for a subset of
Fos-high ( _n_ = 289) and Fos-low ( _n_ = 230) place cells in one mouse. Black
lines denote significant spatially binned activity in that portion of the
track. A cell might appear more than once if it was induced on multiple
sessions. Same ordering of cells across sessions. **d** , Schematic of
stability calculation. For every pair of sessions, the correlation _x_ between
the spatially binned activity vectors is computed for each cell. Stability is
the weighted mean of _x_ in each bin, where weights _w_ are proportional to
the normalized activity of cells in that bin (  Methods
 ). **e** , Average stability maps for
Fos-high and Fos-low populations. For each session-to-session comparison, Fos-
high and Fos-low groups were determined on the basis of induction on session
_n_ . Data are shown as the mean ± s.e.m. _n_ = 21, 17, 14 and 8 session-to-
session comparisons, from left to right. **f** , **g** , Average stability in
the peri-reward zone and no-reward zone of the track (zones indicated in **h**
, **i** ) across sessions. Data are shown as the mean ± s.e.m. _n_ = 21, 17,
14, 8, 5 and 3 session-to-session comparisons, from left to right. **h** ,
**i** , Difference in stability between Fos-high and Fos-low ( **h** ) and
Fos-KO (Cre  \+  ) and wild-type (WT) ( **i** ) cells as a function of track
position. Black markers indicate bins where _P_ < 0.05. _***P_ < 0.001 ( **h**
) and ** _P_ = 0.005 ( **i** ) for comparison of the mean stability difference
in peri-reward and non-reward zones, two-sided bootstrap permutation test
(1,000 shuffles). Data are shown as the mean ± s.e.m. _n_ = 68 ( **h** ) and
114 ( **i** ) session-to-session comparisons.
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###   The dynamic state of a prefrontal–hypothalamic–midbrain circuit
commands behavioral transitions
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Article  Open access  18 March 2024
###   Source Data Extended Data Fig. 3
Our results show that cells with high Fos induction form high-quality spatial
maps that are regulated in part by Fos function. However, we do not know
whether these Fos-expressing place cells have characteristics expected of an
engram. An engram is predicted to be composed of neural ensembles that are
made up of functionally connected and correlated neurons. Correlated activity
among neurons could explain how activation of only a subset of neurons within
an ensemble can trigger memory recall   4 .") ,  7
.") ,  8
.") . In addition, these ensembles are predicted to have stability
across days to serve a role in memory   5 .") ,  7 .") ,  39 .") .
* Article
*  Open access 
* Published:  24 August 2022
A previous study proposed that Fos-expressing neurons encode context and are
unrelated to spatial maps in novel environments   10
.") . This
study found that Fos-expressing cells had less spatial information per spike
than non-Fos-expressing cells and were more likely to remap following re-
exposure to a novel environment   10 .") , which is different from what we report
here. While our work and this previous study used different methods to explore
spatial behaviours, to identify Fos-expressing neurons and to measure neural
activity, these differences are unlikely to explain the results. For example,
studying place cells with calcium imaging and virtual reality, as we did here,
is a well-established approach that has been shown to find similar place cell
properties as other methods  42  ,  43  ,  44
.") . Instead, a key difference
between the studies is that the previous work focused on Fos-expressing cells
in novel environments, whereas we studied these cells in familiar
environments. Fos is induced more broadly in novel environments   45
.") , and
it is likely that novelty-related cues, such as new odours, sounds, sights and
textures, strongly drive induction in these environments. As environments
become familiar with repeated exposures, induction of Fos by non-spatial
novelty-related factors declines, possibly leaving spatial factors as a
primary driver of Fos expression. In this case, because place cell firing
induces Fos expression and, in turn, Fos is necessary for creating a stable
spatial map in familiar environments, as we showed here, this feedback loop
may strengthen the place code and sharpen the link between Fos and place codes
as environments become familiar. Indeed, the CA1 place code transitions from
being unstable in novel environments to stable in familiar environments   46
.") ,  47
.") . Thus, it is possible that Fos-expressing neurons encode the
feature that is most relevant to the hippocampus, which may be context in
novel environments and space in familiar environments. Finally, it is also
possible that spatial codes and non-spatial contextual codes co-exist in Fos-
induced neurons, as not all Fos-high neurons we recorded exhibited place
fields.
These results indicate that Fos-high cells collectively form a spatial map of
the environment. We performed two additional experiments to test this idea.
First, we asked whether Fos-high cells are still related to a spatial map in
the absence of a behavioural task. A separate cohort of mice ran through
virtual environments but without performing a spatial learning task or
receiving rewards. In these experiments, a place code is still present but
other task-related factors that could drive Fos expression are absent, such as
reward. The differences between Fos-high and Fos-low cells were qualitatively
identical to those in mice trained on the task: Fos-high cells had
significantly higher spatial information, a greater prevalence of place
fields, more reliable place field activity and higher integrated calcium
levels (Extended Data Fig.  7  ).
###   Source Data Fig. 2
Here we developed a behavioural and imaging approach to study the relationship
between Fos expression and place cells. We find that cells with high Fos
induction function as ensembles of highly correlated place cells, organized as
sequences along a virtual reality track, with reliable activity within their
place fields from trial to trial within a session. Moreover, the place fields
in these Fos-induced cells uniformly tile the environment, with high across-
day stability when compared with non-Fos-induced cells. Using a mouse genetic
loss-of-function approach, we further define a causal role for Fos in
regulating hippocampal place codes. Cells lacking Fos have place fields with
less reliability, spatial selectivity and cross-day stability than their wild-
type neighbours. Together, our results indicate that Fos-expressing cells
contribute to spatial coding by forming reliable, long-lasting and spatially
unbiased maps of an environment and further identify an instructive role for
the Fos protein in this process. These findings suggest a link between
contextual memories encoded by engrams   4
.") ,  6 .") and cognitive
map theories   1 .") of hippocampal function.
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###   Co-dependent excitatory and inhibitory plasticity accounts for quick,
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We were motivated to expand our investigations beyond only using Fos as an
activity marker and aimed to identify a causal role for Fos in shaping
hippocampal activity and place codes. We reasoned that, if Fos were implicated
in this process, this could serve as a molecular inroad to begin to establish
a mechanistic framework for understanding the cellular, synaptic and circuit
bases of episodic memory regulation. In line with this idea, our results
implicate Fos in shaping place codes but not in the initial establishment of
place fields. We have recently shown that Fos, through a downstream effector,
Scg2, affects long-lasting changes at distinct subtype-specific inhibitory
synapses onto CA1 pyramidal neurons but does not appear to affect excitatory
synaptic transmission onto these cells   24
.") . Overall, our results are consistent with previous work
suggesting that place fields may initially form through excitatory synaptic
mechanisms   51 .") ,  52 .") , while
inhibition is critical for modulating the firing of place cells that have
already formed, including for suppression of out-of-field firing and
regulation of cellular activity levels and timing   27
.") ,  28
.") . Fos may therefore regulate place
coding by setting up the appropriate sources and levels of inhibition required
to maintain functional place cell networks. However, this is just one of many
possible mechanisms by which Fos may shape neural activity in CA1. Future work
will examine the role of Fos-mediated inhibitory plasticity directly in
behaving animals and explore other potential pathways by which Fos can
influence place cell function. In addition, it is likely that Fos acts in
conjunction with other pathways, as expected for a process as complex as
learning and memory of spatial maps. Although we did not evaluate the effect
of Fos expression on behaviour here because we disrupted Fos function in only
a sparse set of neurons, the degradation in the spatial code that we observed
in Fos-KO cells is in line with our recent findings that knocking out Fos in a
large fraction of CA1 cells impairs spatial learning in the Morris water maze
 24 .") .
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* Noah L. Pettit   ORCID:  orcid.org/0000-0001-8699-7443  1  ,
* Ee-Lynn Yap   ORCID:  orcid.org/0000-0001-6537-508X  1  ,
* Michael E. Greenberg   ORCID:  orcid.org/0000-0003-1380-2160  1  &
* …
* Christopher D. Harvey   ORCID:  orcid.org/0000-0001-9850-2268  1
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Together, these results demonstrate that cells with strong Fos induction are
functionally distinct from neighbouring cells that have weak Fos induction.
Fos-high cells are more likely to have place fields, have more reliable trial-
to-trial activity and have higher spatial information at the population level.
Moreover, their place field distribution is relatively uniform, in line with
Fos-high cells encoding position rather than reward or valence   38
.") .
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Changes in _Fos_ promoter-driven GFP expression were measured using time-lapse
two-photon imaging   29 .") ,  70
.") . To compute the fold change in GFP
fluorescence 2–4 h after the start of behaviour, the registered post-behaviour
GFP image was divided by the pre-behaviour GFP image. To account for changes
due to non-uniformities in brightness across the field of view, the resulting
image was normalized by dividing by an estimated background image. The
background image was obtained by two-dimensional median filtering the fold-
induction image with a filter size of ~50 × 50 µm  2  , approximately 30 times
the area of individual cell sources. This normalization accounts for changes
in fluorescence that could be due to changing imaging conditions rather than
changing GFP expression, including non-uniform changes across the field of
view, and it assumes that GFP fluorescence is relatively sparse and primarily
localized to the nucleus, assumptions that are well supported by the
literature. Regions of the fold-induction image that contained artefacts due
to errors in registration or large changes in imaging quality were manually
excluded from further analysis. For each cell source, fold-induction values
were determined by averaging the pixel values of the fold-induction image
within a circular ROI centred on the cell body, 10 µm in diameter. Fos-high
cells were defined as cells with the 20% highest fold-induction values on a
given session, and Fos-low cells were defined as cells with the 20% lowest
fold-induction values on a given session. To determine the time course of
induction (Extended Data Fig.  2  ), the
same procedure was used with the addition that, before computing the fold-
induction images, all images in the time course were standardized using the
following procedure. Each image was standardized by its median and median
absolute deviation and then rescaled by a multiplicative factor _A_ and
additive offset _B_ , where _A_ was the median absolute deviation of pixel
values across all images in the series and _B_ was the median pixel value of
all images in the series. The median and median absolute values were chosen
under the same rationale and set of assumptions as described above. The
recovered time course was consistent with previous studies using this, and
other, Fos-GFP reporters   17 .") ,  29 .") ,  30
.") .
* Aniruddha Das
* Sarah Holden
* Hod Dana
###  Spatial information
* Issue Date  :  08 September 2022
We first asked whether Fos-high neurons have activity that is highly
correlated with the activity of other Fos-high neurons. Because a place code
is made up of cells that are active in a sequence across the track, we wanted
to measure whether Fos-high neurons tend to have their highest activity on the
same trials as other Fos-high neurons, regardless of the location of their
place fields. We therefore computed correlations between place cells using the
mean activity within their place fields on every trial, instead of using the
raw time series of their activity (Fig.  4a,b
 ). This approach quantifies the degree to
which a pair of place cells have shared trial-by-trial fluctuations in their
place field activity, even if their place fields are at different locations.
It does not consider temporal correlations of the raw traces, which would
instead reflect neurons tending to be active at similar locations. Within a
session, Fos-high neurons had high correlations with other Fos-high neurons,
compared with the correlations among Fos-low cells (Fig.  4e
 ). Notably, this correlation structure
was preserved at similar levels for several days following Fos induction (Fig.
 4g  ).
where _i_ is the _i_ th spatial bin, _a_ is the overall mean activity, _a_ _i_
is the mean activity in the _i_ th bin and _p_ _i_ is the fraction of time
spent in the _i_ th bin. For each cell, we computed the real value and the
value for each of 1,000 shuffles (see ‘Place cell definition’). We report the
normalized spatial information for each cell as the real information divided
by the mean across shuffles   71 .") .
\(2021\).") ,  56 .") , using the red-shifted calcium indicator jRGECO1a. Raw traces extracted by Suite2p were further processed as follows: a baseline fluorescence estimate was computed as the 30th percentile in a 60-s moving window. ∆ _F_ / _F_ traces were computed by subtracting and dividing the raw trace by the baseline. For most analyses, significant transient traces were used   52 .") ,  67 .") ,  68 .") . Significant transients were identified using the following procedure, based on previous work   68 .") (Extended Data Fig.  4  ). ∆ _F_ / _F_ traces were standardized by subtracting the median and dividing by the standard deviation. Threshold levels in units of standard deviation (σ) were chosen between 1 and 4 in 0.2σ increments. For every threshold level _t_ , putative transients were identified as positive samples exceeding that threshold. For each putative transient _n_ frames in length, we estimated a false positive rate as the number of negative-going transients (<– _t_ ) with at least _n_ frames divided by the number of positive-going transients with at least _n_ frames. Transients with a false positive rate of less than 0.001 (0.1%) were considered to be significant at that threshold level _t_ . Frames that were significant at at least one threshold level were considered significant in the final output (union across threshold levels). Finally, transients separated by less than two frames were merged, and transients less than two frames in duration were removed. The final significant transient traces are original zero-baseline ∆ _F_ / _F_ traces, where all frames without significant transients were set to zero.
_Cell Research_ (2022)
\(2017\).") (Python version,  https://github.com/MouseLand/suite2p  ). The resulting sources were classified into two groups: putative cell body and non-cell body sources. Classification was performed using a simple convolutional neural network trained in MATLAB on manually labelled data, using a network architecture, hyperparameters and training procedure described previously   64 .") with the exception of two output classes rather than three. Non-cell sources were discarded from further analysis.
Correspondence to  Michael E. Greenberg
 or  Christopher D. Harvey
 .
**a** ) Schematic of bilateral injections of muscimol or saline into dorsal
CA1. For each mouse, sessions alternated between saline and muscimol across
days. Adapted, with permission, from Franklin and Paxinos   79
.") . **b** )
Performance measured as licking selectivity on alternating muscimol and saline
sessions. Each set of connected dots is one mouse. n = 4 mice, 8–10 sessions
per mouse. **c** ) Fraction of spatial bins with a lick for saline and
muscimol sessions. Each pair of connected dots is a single mouse. p = 0.019,
two-sided permutation test, 1000 shuffles. n = 4 mice, 10 sessions per mouse.
**d** ) Lick rate for saline and muscimol sessions. Each pair of connected
dots is a single mouse. p = 0.17, two-sided permutation test, 1000 shuffles. n
= 4 mice, 10 sessions per mouse. **e** – **g** ) Fraction of trials with a
lick, running speed, and fraction of trials with a consumption lick as a
function of maze position for saline and muscimol sessions for individual
mice. **h** ) Histogram of session average running speed for saline and
muscimol sessions. Sessions with mean speeds between 10 and 30 cm/s were
chosen for the speed-matched analysis, resulting in 8 saline sessions from 4
mice and 7 muscimol sessions from 2 mice. **i** ) Spatially binned running
speed and fraction of trials with a lick for speed-matched saline and muscimol
sessions. **j** ) Mean running speed for speed-matched saline and muscimol
sessions. Saline vs. muscimol: p = 0.76; two-sample _t_ -test. Error bars,
mean ± s.e.m. n = 8 sessions (saline) and 7 sessions (muscimol). **k** ) Mean
lick selectivity for speed-matched saline and muscimol sessions. Saline vs.
muscimol: p = 3.2 x 10  −7  ; two-sample _t_ -test. Error bars, mean ± s.e.m.
n = 8 sessions (saline) and 7 sessions (muscimol).
Naive Bayes’ decoders were used to decode position   72
.") ,  73
.") from the
activity of groups of neurons within each session. The decoder assumed Poisson
firing and independence between neurons and adopted a uniform prior for all
spatial bins. The inclusion criteria for frames used for either training or
testing were the same as those used for place field calculation. Even-numbered
trials were always used for training (computing place field activity
templates) while odd-numbered trials were used for testing. Decoding error was
defined as the absolute difference between the true spatial bin and the
decoded bin, ranging between 0 and 100 cm owing to the circular structure of
the track. For each frame, the posterior probability of the mouse being in a
given position bin ‘pos’ was computed as
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##  Measuring task-related Fos induction
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