Flipping the Switch

The 'surprisingly simple' arithmetic of smell

Jan 2022, phys.org

https://medicalxpress.com/news/2022-01-surprisingly-simple-arithmetic.html

The age of artificial olfaction is upon us.

This is now the second report in the last few months that presents a computational model for the olfactory bulb, which is the biological supercomputer on your face that crushes gigtons of databytes per attosecond (slight exaggeration).

The last paper came from a physicist working on information theory (Tavoni et al at Penn State). Another paper the month prior, which came from none other than the lab that discovered olfactory receptors, found, again, a computational model, discovered via machine learning, that compresses the n-dimensionality of odorant sensory data. 

But again, this new paper comes primarily from a department of electrical and systems engineering, in collaboration with the biomedical engineering department. I don't know everything that's going on, I only read the papers on the weekends, but that's a lot of papers about computing in olfaction, and from people who do not study olfaction exclusively.

And this is at the level of the bulb. We're not talking about the DREAM project, where big-data's worth of words and molecules are processed by GPT-3 to predict the names of smells. This is about looking at the hardware. How in the world does that bulb, which compresses thousands of receptors, themselves receiving information from un-countable stimuli, into dozens of signals that go on to control the entire enormity of a mammalian body via its limbic system. The bulb is the choke point for this system, and it's using magic that we are only now beginning to understand to the point of copying it. 

I doubt this is the earliest example, but as far back as 1991, scientists were talking about olfaction as a model system for computational neuroscience. These were neuroscientists and psychologists writing about this. But they could see the significance -- it's literally wired like the deep learning neural networks you hear about in the news (you know, powering the AI in your toaster, your tissue box and your alarm clock). 

It really looks like we're getting the hang of this. They started with a simple question -- how come things smell the same to us, even in different contexts or environments? Like how a plaid shirt looks okay in your sunlit bedroom, but later looks like a shit sandwich in the fluorescent lights of your office (or remember the black-and-gold dress? maybe you're trying to forget). 

If smells come from evaporating molecules, which are literally volatile, changing all the time based on environmental conditions, how come they always smell the same to us? Maybe olfaction would be a good model to investigate. 

So they did, by pairing locusts with a training smell, under all kinds of different conditions, hungry, full, hot, cold, humid, dry. Every time, the locust recognized the training smell (with the locust equivalent of a salivating dog). Yet, "The neural responses were highly variable," one of the researchers said. Same molecule, same response, but completely different receptor patterns, every time. It just doesn't make sense.

Deep learning to the rescue (obviously). The algorithm found that it's the interaction of activating and inhibiting neurons; I'll copy the copy directly:

Finding the features you want is similar to the information conveyed by the ON neurons. Absence of deal breakers is similar to silencing of the OFF neurons. As long as enough ON neurons that are typically activated by an odorant have fired—and most OFF neurons have not—it would be a safe bet to predict that the locust will open its palps in anticipation of a grassy treat.

via the Department of Electrical and Systems Engineering and the Department of Biomedical Engineering, Washington University in St. Louis: Srinath Nizampatnam et al, Invariant odor recognition with ON–OFF neural ensembles, Proceedings of the National Academy of Sciences (2022). DOI: 10.1073/pnas.2023340118

http://dx.doi.org/10.1073/pnas.2023340118

And further reading:

via University of Pennsylvania: Gaia Tavoni et al, Cortical feedback and gating in odor discrimination and generalization, PLOS Computational Biology (2021). DOI: 10.1371/journal.pcbi.1009479

http://dx.doi.org/10.1371/journal.pcbi.1009479

via Massachusetts Institute of Technology's McGovern Institute for Brain Research: Peter Y. Wang et al, Evolving the olfactory system with machine learning, Neuron (2021). DOI: 10.1016/j.neuron.2021.09.010.

http://dx.doi.org/10.1016/j.neuron.2021.09.010

via MIT: Davis J L & Eichenbaum H, eds. (1991). Olfaction: A Model System for Computational Neuroscience. Boston: Bradford Books/MIT Press.

https://mitpress.mit.edu/books/olfaction

Deep Nose, 2022

http://www.limbicsignal.com/2022/02/deep-nose.html

Signal to Noise for the Win, 2021

http://www.limbicsignal.com/2021/11/signal-to-noise-for-win.html

Olfatory Overload, 2021

http://www.limbicsignal.com/2021/05/olfactory-overload.html

Image credit: Inhibitory Synapse - TAO Changlu, LIU Yuntao, and BI Guoqiang; Image design: WANG Guoyan, MA Yanbing - 2021

Popcorn Pee

The bearcat, an animal from Southeast Asia, marks its territory with popcorn-smelling urine. Researchers, i.e. professional piss-sniffers, used gas chromatography-mass spectrometry to identify the compound 2-acetyl-1-pyrroline in the bearcat’s urine. This is the same compound that gives popcorn its smell. It forms when heat drives a reaction between the sugars and amino acids in the kernel. A similar thing happens when bread is toasted and when rice is cooked.

The researchers say that with the bearcat, the compound 2-AP is probably created when the animal’s urine combines with microorganisms living on its skin and fur. These microorganisms break down the urine in the same way human-armpit microorganisms turn our sweat into “body odor.”

Although popcorn is an unusual example of body odor, for sure, it is considered one of the ten primary categories of smell. It should be noted that this very recent method for classifying odors is pretty loose, meaning the categories are not rigidly defined, and by viewing this chart, one can see the slight alterations that are almost as acceptable. Researchers used  statistical analysis to condense the categories into the following list: Fragrant, Woody/resinous, Fruity (non-citrus), Chemical, Minty/peppermint, Sweet, Popcorn, Lemon, Pungent, Decayed.

Notice here the persistent difficulty in organizing smells – one category requires its own disambiguation (fruity non-citrus), and one isn’t even a smell (sweet).

The organization of smells is an arduous task, and although a handful of people have tried, none have been successful. Using statistical semantic analysis is a relatively new approach. They began with a standard catalogue of odors, taken from the Andrew Dravniek's 1985 Atlas of Odor Character Profiles (144 odors, I believe). Upon this, they do a form of statistical analysis called non-negative matrix factorization (NMF). NMF is a dimensionality-reduction technique, which makes it handy for categorizing the perceptual space of smell.  It has to do with confusing things like normalization and consensual matrices, but all we need to know here is that a huge network of smell descriptors are matched against each other to measure their similarity, both to each other, and to a baseline. Each descriptor is then given a kind of similarity score. The descriptors that are the most similar to others are then called the primary categories.

A hypothetical corpus of all possible smells is a multidimensional thing that has never been (and perhaps can never be) reduced to a small set of categories like colors or musical notes. Statistical methods such as NMF reveal that smells are not evenly spread throughout odor-space, meaning that they are not equally different from each other. Instead, they form clusters of similarity, albeit a very loose clustering. As the scientists note in their paper, “Because NMF is an iterative optimization algorithm, it may not converge to the same solution each time it is run (with random initial conditions).” -source

This method, which provides not answers but approximations, turns out to be quite appropriate. The odor lexicon is an ephemeral thing, like smell itself. It is a precognitive perception which bypasses the language centers of our brain, yielding an unpredictable set of descriptors that will change with the verbalizing person. I just wonder – in cultures where they don’t have popcorn, what would they call bearcat piss?

Notes:

Researchers pinpoint chemical compound that gives rare animal its popcorn-like scent

phys.org, April 2016

Castro JB, Ramanathan A, & Chennubhotla CS (2013). Categorical dimensions of human odor descriptor space revealed by non-negative matrix factorization. PloS one, 8 (9) PMID: 24058466.

see this chart listing of other potential primary categories