Peer review of: Rapid Bayesian learning in the mammalian olfactory system (2019)

Reviewers' Comments:
I do have some issues with the paper from an ethological perspective:
Reviewer #1:
Remarks to the Author:
Hiratani and Latham present a Bayesian framework for learning in the olfactory bulb. I feel unqualified
to comment on the computational aspects of the work, but as an experimentalist I see a lot to like.
The paper makes strong, testable predictions of how odor responses should evolve during latent and
reward-based learning. I predict that this paper will have great value for both theorists and
experimentalists.
L106-2009 "This is at odds with the demands of a realistic environment, where the existence of an
odor is almost
always far more important than its concentration"
Nothing could be further from the ethological truth! Detecting and identifying an odor is useless
without being able to locate its source, and most animals use olfaction to find things (Baker, ... Nagel,
and Smear 2019). Therefore, as in other sensory modalities, olfaction faces both "what" and "where"
problems. "Where" can only be judged by spatiotemporal patterns of odor concentration. All of which
is to say that estimating odor concentration is _not_ a "drawback" of the model.
Another biological truth of the olfactory system is that it is fast (eg, Shusterman et al 2011) and can
enable very short reaction times (eg., Uchida & Mainen 2003, Resulaj & Rinberg, 2014), and especially
needs to be so when an animal navigates an odor plume using odor concentration cues. In this
connection, fig 3C&D shows that odor responses diverge more rapidly after learning. I would like to
get a better intuition for why the model shows this behavior. Furthermore, I wonder how the learning
curves of figure 4 will look under realistic timing constraints (ie, one sniff decisions).
Lastly, I question the "spike-and-slab" prior distribution, specifically the slab part. We know very little
about natural olfactory scene statistics, but I'm not sure the shape of the slab is realistic. Are non-zero
odor concentrations most often very low? I don't think so. An animal quite often experiences intense
odors, and must retain dynamic range to sense concentration changes in this regime. How sensitive
are the conclusions of the modelling to the assumed shape of the slab? Does a model with a flatter
slab still learn and discriminate as well?
-Matt Smear
Reviewer #2:
Remarks to the Author:
Hiratani and Latham present a paper entitled "Rapid bayesian learning in the mammalian olfactory
system". The proposed ideas seem interesting from a mathematical point of view, but a lack of clarity
makes it difficult to evaluate how this applies to the olfactory system or sensory systems in general.
In the end a neural model is constructed that learns (what?) and seems disconnected from the math
show in the beginning.
- It is not clarified what is being learned? At first one may think its the "weights" between odors and
 sensory neurons (this should really be referred to as affinities), because these are referred to as :the
 system needs to learn what they are", but then it seems that weights between mitral and granule cells
 
are being learned
 - what is the goal? The performance measure seems to indicate the goal is to reconstruct the inputs,
 but where and why?
 - the authors talk about odor learning but later explain that the model learns to detect the
 concentration of an odor ?Which is it ? Identity or concentration?
 - How is this "rapid" learning evaluated? What is the goal of the learning? It is to create different
 representations for different odorants? This is not at all clear. One can see that MC outputs change as
 a function of learning but nowhere is it stated what the expectations are here
 - Is the system learning odor concentrations, odor identities? Or just learning to differentiate? What
 are the limits of these differentiation? How similar can odors be?
 - It seems impossible to learn an odorant in an unsupervised manner. What does that mean? If the
 system is to learn the identity of an odorant, how would it know this in an unsupervised manner?
 - It seems that maybe the authors are going after separation of odors, which would be plausible from
 the figures, but they are not testing the limits of their approach or showing how well it works for more
 odors, more overlapping odors ..
 - do all odors have to be learned at the same time?
 - the assumption that odors are sparse is crucial to the approach yet seems far fetched. The
 mammalian olfactory system is constantly inhaling odorized air and odors are every where. How well
 can this work without this assumption?
 - the comparison the sparse coding is only useful if one is told what is "good" . The performance
 measures says to reconstruct the input, but this is definitely not what the olfactory system wants to
 do ..
 - it is not clear how the theoretical statements in the beginning aid the model. why not just make a
 model since it then includes hebbian plasticity and so on? While the neural model makes some
 interesting discoveries it is not clear how the bayesian theory contributes to the model in the end.
 Overall there might be an interesting model of olfactory learning here, but to many details as to what
 is being learned and how have been omitted. The manuscript is very unclearly written and jumps from
 topic to topic. Assumptions are not clearly explained and the goals are not clarified.
 Reviewer #3:
 Remarks to the Author:
 The paper by Hiratani and Latham outlines a clever piece of theory which uses Bayesian inference
 arguments and assumptions of a sparse stimulus world to derive physiology and circuitry structure in
 olfactory processing. The work is well done and introduces some novel concepts that can easily extend
 beyond olfaction to more general neuronal coding. Overall, I enjoyed the paper. I do have some
 queries/suggestions that I hope improve the manuscript.
 Major
 1. The authors show that the transfer nonlinearity F has features consistent with neuronal recording
 when the prior is sparse, or at least biased to very weak concentrations. This is a nice result and the
 underlying mathematics in deriving Eqs 31 and 32 is elegant. However, the authors also derive a
 nonlinearity G for the second moment of the concentration in Eq 33. Is there anything to be gained in
 understanding how the variance of c_j depends on the prior? Basically, is the analogue of Fig 5A but
 for G (or G - F^2) at all interesting or predictive?
 2. Fig 5A shows how both the `spike-and-slab’ prior and the exponential prior give different cellar
 transfer functions F. The text surrounding line 142ish argues that both are more consistent with
 
physiology than the non-negative prior. This is certainly an interesting point, and arguably one the the
 triumphs of the study. The fact that sparse stimulus distributions predict physiology is indeed quite
 cool. However, the paper seems to favor the spike-and-slab prior over the exponential prior. Why?
 What is the evidence for the spike-and-slab other than it has a non-infinitesimal mass at zero
 concentration? Indeed, the transfer function F for the exponential prior seems more in line with the in
 vivo whole cell recordings when mean spike rate is plotted against mean Vm (a la D. Ferster).
 3. The authors should discuss why that in Fig. 6A the fixed fast rate learning gives better performance
 early on than the fixed slow rate learning, yet for large trial numbers the order is switching. I assume
 that slow rate learning remembers more past experience and thus is the better performer after
 sufficient experience is given.
 4. While the main text does introduce Bayesian inference reasonably well, any intuition for the the
 variational approximation outlined in sections 4.1.1 and 4.1.2 is absent. I understand the demands of
 streamlining the work for a general audience, but the variational approximation is central to the work.
 It is a shame that no intuition to this work can be given to a general reader.
 Minor
 1. The paragraph beginning on line 100 discuss the optimality of algorithm, in particular how the
 approximation via q affects optimality. Indeed, the Bayesian algorithm compares well against the
 sparse coding model. However the paragraph focuses primarily on the speed of learning rather than
 the overall performance. Fast, efficient learning is all well and good, but this does not really test the
 optimality as the first sentence of the paragraph suggest. I understand that a brute force calculation
 of optimal performance is not feasible. Then a simple rewrite of the paragraph so that it is clear how
 the sparse and the variational Bayesian algorithms are actually compared to one another is warranted.
 2. Maybe I missed this, but does the dendro-denritic synaptic connections between granule and mitral
 cells for a certain symmetry between w^L and w^F? Basically, if a mitral cell couples to a granule cell
 then the reverse must also be true. The Hebbian plasticity rule in 5a and 5b also suggests that the
 reciprocal connections are either both strong or are both weak. Is there any experimental evidence for
 this?