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\begin{document}

\title{BU NeuroPreprint JC review of Taxidis et al., 2018 ``Emergence of stable
sensory and dynamic temporal representations in the hippocampus during
working memory''~~~~}



\author[1]{BU NeuroPreprint Journal Club}%
\affil[1]{PREreview}%


\vspace{-1em}



  
  \date{June 10, 2024}


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\subsection*{Emergence of stable sensory and dynamic temporal
representations in the hippocampus during working
memory}

{\label{720048}}

\subsection*{Jiannis~Taxidis,~Eftychios~Pnevmatikakis,~Apoorva
L~Mylavarapu,~Jagmeet S~Arora,~Kian D~Samadian,~Emily
A~Hoffberg,~Peyman~Golshani
~}

{\label{720048}}

Posted on Biorxiv,~ November 20, 2018

\subsubsection*{\texorpdfstring{\url{https://doi.org/10.1101/474510}}{https://doi.org/10.1101/474510}}

{\label{236037}}

\section*{}

{\label{463319}}

\emph{Note: Below is our review of the Biorxiv preprint article by
Taxidis and colleagues, ``Emergence of stable sensory and dynamic
temporal representations in the hippocampus during working memory.''
There has recently been an interesting discussion about best practices
in open peer review. Accordingly, we decided to reach out to the authors
with our review prior to posting online. The authors were extremely
receptive and engaged and responded to our review. Below our comments
are in} plain text \emph{and~}\textbf{author responses are in
bold\emph{.~}}\emph{We are grateful to the Golshani lab, and in
particular Jiannis Taxidis, for engaging in open dialogue.~}

\par\null

Summary

In this manuscript, Taxidis et al. recorded sequential activity in CA1
during a delayed olfactory non-match-to-sample task in mice over
multiple days. They describe two populations of cells active during the
task. One population, termed ``odor-cells'', are active during odor
delivery and another, termed ``time-cells'' are active at specific time
intervals in the intervening delay between two odor presentations. They
showed that classifiers trained on these cells can reliably decode the
presented odor and also the time interval on the delay, suggesting that
items might be held in working memory by these cells. They also found
that odor cells generally are reactivated more across days than time
cells, and that temporal and odor information can be reliably extracted
(by Bayesian classifiers) up to 5 days later. In additional experiments,
the delay was elongated and the authors found that odor cells generally
retained their firing field whereas time cells shifted their fields.
Over behavioral training, the number of time cells increased as well as
odor decoding and temporal interval decoding fidelity, which all
correlated to behavioral performance. On the other hand, the number of
odor cells stayed relatively constant. Lastly, mice exposed to a passive
version of this task with no memory demand showed far fewer time cells
and no change in these metrics over days.

~

The manuscript is a technical tour de force involving complex behavior,
learning data, and sophisticated analyses. The results add valuable
knowledge to how the hippocampus produces and maintains neural
sequences. Particularly interesting is how these sequences evolve while
the mice learn the task. Overall, we are excited about this impressive
body of data and believe that it would greatly enhance our understanding
of encoding mechanisms in the hippocampus. We have several suggestions,
though some may have been addressed in the supplemental figures, which
we were unable access through the preprint servers.

\textbf{We are very grateful for this impressively thorough and
insightful review! And we are very happy that you found our work
interesting. Your feedback is very useful and we are currently working
on implementing some of your points, along with those we received by
journal reviewers.}

\textbf{}

\textbf{It is surprising that you couldn't access the supplemental
figures. I just checked again and they are still available on BiorXiv. A
link to the pdf should show up when clicking on Supplemental Material.}

\par\null

\textbf{}Major concerns

-~~~~~~ The authors propose a dichotomy between so-called ``odor cells''
and ``time cells'' in CA1, however this distinction may be overly
simplified and is only partially supported by the data presented here.
Fig. 3D shows that odor cells decode odor better than time cells,
suggesting they might be functionally different populations. However, it
is equally fair to say that ``early time cells'' decode odor better than
``late time cells'' without considering this odor/time dichotomy. That
is because the criterion distinguishing an odor cell from a time cell is
whether it fires during the odor delivery window, which does not
actually assess selectivity to odor per se. Additionally, odor
presentations only occur at 1-2 sec, so odor delivery is conflated with
the beginning time intervals of the trial. And since cells firing later
in the sequence have higher variance than those firing earlier (which
frequently occurs in these types of sequences), that variance is
probably contributing to worse performance, explaining why time cells
may have worse decoding properties than odor cells.

-~~~~~~ In a similar way, it would be equally fair to say that early
time cells (versus odor cells) are more stable across days than late
time cells (Fig. 4C). To test whether odor cells are actually a distinct
population from time cells, the authors would need to present odors
outside of the task and confirm that they activate the neurons labeled
as odor cells (but not the neurons labeled as time cells) when there is
no delay. Another possibility would be to run the same analyses on odor
versus time cells using an odor selectivity criterion to separate these
populations rather than selectivity to the odor delivery time window.

\textbf{This is a very good point, also raised by our reviewers. It is
true that the class distinction is initially based on the time point of
a cell's field (during the odor delivery, versus during the delay) and
in that sense is arbitrary. But this distinction is then supported in a
phenomenological way by the differences we find in stability,
adaptability and shaping during learning. However these are not enough
to safely conclude that these cells are indeed separate classes of
neurons. We are thus currently working on some follow up experiments,
including the out-of-context odor presentation suggested here, to
further investigate if these are truly two separate but interdependent
classes or whether they are in fact, simply a sequence of cells
initiated by the odor-presentation.}

\par\null

- The authors report better decoding performance from classifiers
trained on odor cells compared to those trained on time cells. However,
Fig. 3A suggests that there are significantly more odor cells than time
cells, so the improved performance may simply be due to higher
dimensionality. Are the classifier results consistent if the odor cell
population is downsampled to match that of time cells? This logic also
applies to the analyses assessing field stability across days. Is there
really more instability in late time cells or is this a consequence of
there being a smaller sample of time cells late in the delay?

\par\null

\textbf{The numbers of odor versus time-cells are compared in Figure 2D
(bottom) and are actually similar (in fact one sequence yields
significantly less odor-cells on average). Figure 3A does not suggest a
lower number of time-cells, but rather that their distribution decreases
over the delay period, so that late time points are represented by less
time cells than early ones. This should not affect our analysis however,
since in SVM classifications we consider all odor-cells vs all
time-cells and in Bayesian decoders the whole sequence is used to decode
at each time-point. We therefore do not expect that the improved
decoding performance of odor-cells is due to their higher number, even
though the suggested downsampling control was not explicitly performed.}

\textbf{}

\textbf{The reason why time-cells yield lower decoding performance is
demonstrated better in Figure 3B. There, we show that these cells
(particularly later in the delay) are more sparsely activated across
trials and have a larger variability in spiking time. In contrast,
odor-cells spike significantly more often and within their field (i.e.
during the odor-presentation), yielding much better decoders.}

\textbf{}

\textbf{Regarding instability across days: Indeed less time-cells exist
in late delay points than in earlier ones, but we don't expect that
their distributions of field-shifts are affected by their sample size,
to the extent that would question the finding. We believe it is evident
from displays of the distributions (like in Fig 3G), that by randomly
picking subsamples of early vs late time-cells, the difference in
average shifts will still hold. We would also like to add here that,
even when normalizing these shifts by the maximum shift a cell could
exhibit (e.g. a cell in the middle of the delay could not exhibit a
shift a large as a cell in the very beginning or end of the delay) our
results still hold. We will explicitly state this in the revised
manuscript.}

\par\null

- Fig. 2C shows distinct sequences for each odor, so in a sense, the
entire sequence ensemble is composed of odor cells. It may be more
accurate to say that most cells involved in the task are really
conjunctive odor-time cells.~

\par\null

\textbf{We chose to name cells that encode time-points in a delay
following a particular stimulus as ``time-cells'', as in MacDonald et
al. 2013. As you suggest here, these could also be called ``odor-time
cells'' since they are odor-specific.}

\par\null

- The authors present interesting data on the development of sequences
over learning and non-learning. Figs. 6 and 7 were our favorites out of
the whole manuscript. This aspect of temporal sequence-building has
never been described in much detail and the community would benefit from
more analyses from this portion of the experiment. For example, Alcino
Silva's work suggests that more excitable cells are more likely to be
recruited to a memory trace. Do cells with higher calcium activity tend
to enter the sequence over learning? Are sequences generally more
plastic early in learning? We think that the manuscript can grow
stronger from focusing in this area as other studies have investigated
stability of sequence over days (Mau et al., 2018) and how changing the
delay duration affects time cells (MacDonald et al., 2011). This angle
would also diminish what we believe to be potentially problematic
interpretations of odor selectivity versus temporal selectivity outlined
above.

\par\null

\textbf{These are indeed some great questions. We plan to delve deeper
into this line of investigation in a future study that will be more
focused on the emergence details during learning. For this one, our goal
was to focus on the overall stability/adaptability/emergence differences
between the two encoding categories and demonstrate how CA1 circuits
allow for two encoding regimes.}

\par\null

- In analyses involving comparisons of fields across days, cells that
lost their field were considered ``unstable''. That means that analyses
that look at the field shift of these unstable cells are comparing a
field on one day and a non-field on another. Therefore, field shift
analyses in Figs. 4 and 5 should not compare between stable and unstable
cells since this comparison is unfair and not informative. If a field
disappears, the authors should not be able to say that the field
``shifted''. Along those lines, we would recommend removing the blue
data points (as in Fig. 4F) from these analyses since they demarcate a
cell's non-field, which has questionable informational content.

\par\null

\textbf{It is true that non-significant fields (simply peaks in mean
activity) are not carrying statistically sound information. We simply
use them as a proxy of where cells ``tend'' to fire once they lose their
field. We show this information for completeness and to draw a better
distinction with cells that retain a stable field, though a direct
comparison is avoided. We will examine the text and make any necessary
changes to explain this point better.}

\par\null

Minor concerns

- A figure describing the performance of the mice in this task is needed
early in the manuscript.

\textbf{We initially included one but it carried essentially the same
information as Figure 6A so we removed it.}

\par\null

- In Figure 2C, there is an interesting ``shadow sequence'' following
delivery of the second odor, possibly due to relaunching of specific
odor-sequences. But this is likely obscured due to some trials being
odor A-odor A and others odor A-odor B (and vice versa). It may be
beneficial to partition trials into the four odor A/odor B combinations
to visualize odor-specific sequences more clearly.

\par\null

\textbf{The ``echo'' after the second odor is indeed due to the sequence
repeating if the second odor is the same as the first. We are working on
a supplemental figure on this.}

\par\null

- Regarding Fig. 2D, please comment on why there might be more activity
associated with odor A on average (for both the odor delivery and
delay).

\par\null

\textbf{We have no insight on this. It probably has to do with how the
olfactory neural circuits (leading up to the hippocampus) deal with the
two odors.}

\par\null

- In Fig. 3, paradoxically and interestingly, the PCA trajectories of
the two sequences converge over the delay, yet they are distinguishable
using the SVM. Please comment on this discrepancy. Perhaps it would be
more suitable to use a multivariate distance metric across the
trajectories rather than Euclidean distance to the PC trajectory.

\par\null

\textbf{We have a comment on the text on how PCA can be heavily
influenced by sparseness in firing rates. This is why we actually shift
from this technique and then stick to better decoding approaches for the
rest of the text, although improved PCA methods could also be used, as
you suggest.}

\par\null

- The middle and right columns in Fig. 3D are confusing. Consider
placing them in supplemental figures instead.

- While interesting, the explanation for the analysis behind Fig. 3E is
too brief. The authors should explain this analysis and its motivation
in more detail.

\par\null

\textbf{Our goal here was to examine the decoding power of individual
neurons instead of whole sequences or grouped odor vs time-cells. This
could be done by testing how well can an SVM classifier distinguish
odors through the activity of single neurons.}

\par\null

\textbf{-} In the first sentence of p. 15, the authors suggest that
improved classifier performance over training was a result of increasing
number of sequence cells, but this is not explicitly tested. If the
sequence cells used to train the classifier were downsampled, is
decoding constant over days?

\par\null

\textbf{This is a good point, that we did not explicitly test. Our
conclusion was drawn indirectly by the fact the spiking properties of
sequence cells did not change over days (Supp Fig 8), only their
number.}

\par\null

- Please describe the passive exposure to the DNMS task in more detail
in the main text.

\par\null

\selectlanguage{ngerman}\textbf{These were naïve mice that were never trained to perform the
DNMS task (or even lick the water tube). They were only water deprived
for consistency. So when we exposed them to DNMS trials, they simply
passively smelled the odors which had no task-context.}

\par\null

Other comments

- All x axes that say ``Firing fields (sec)'' would be better described
with ``Firing field times (sec)''.

- In the last paragraph of p. 3, it is not clear how the
non-match-to-long-duration-sample task tests the mice's reliance on
lingering odor during the delay. Was the odor concentration during the
delay measured to be below this 0.01\% threshold?

- In the text, please make it more clear that a ``sequence-cell'' was a
cell that was either an odor-cell or time-cell rather than simply
stating that they are ``in each sequence''.

- In the third paragraph of p. 8, ``odor-cells had significantly higher
activation probability and lower spiking variance that time-cells''
should read ``\ldots{} than time-cells''.

- In the second to last paragraph of p. 9, ``few time-cells recurred for
more that 2 days'' should read ``\ldots{}than 2 days''.

- In the third to last paragraph of p. 11, the last sentence beginning
with ``Odor-cell stability\ldots{}'' is grammatically problematic.

- In the first paragraph of p. 13, ``decoded time the 10 sec trials''
should read ``decoded time during the 10 sec trials''.

- In the last paragraph of p. 29, ``thought'' should be ``though''.~

- In the first paragraph of the last page, ``Chance baselines where
computed'' should be ``Chance baselines were computed''.

Figure 1.

- If possible, replace the red or green channel with a different color
to accommodate colorblind individuals.

- In the legend for E, state that the green and yellow bars indicate
odor delivery.

Figure 4.

- In A, The cell ROI examples on the bottom are hard to see. Consider
increasing the contrast in those insets to better display the cells.
Particularly for the second ROI, there is a clearly visible cell right
below the ROI that might serve as a better example.

Figure 6

- What are the y-axes for the Bayesian decoder in 6F and 6H? The legend
says they are the same as in 6C/6D but the y-axes there are number of
neurons. This issue appears to occur again in Figure 7. It looks like
there is normalization occurring, so we suggest using z-scores or
renaming the y axis to be less vague.

Figure 7

- The x axis of Fig. 7D appears to be mislabeled and should read ``Days
in training-stage'' rather than ``Performance''.

\textbf{We will correct all mentioned typos and make the suggested
alterations in figures and text. These are all great points and we are
very grateful for bringing these to our attention!}

\par\null

-BU NeuroPreprint JC

\par\null

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