Temporality in olfactory perception is understudied. But as our ability to measure spike activity over small timescales improves, so will our understanding of olfaction, and human behavior in general.
Facebook knows what's up; they're making Time Cards for computers and networks so they can perform better, and integrate information better. They understand the mismatch between the size of today's clocks vs the speed of information.
Today's clocks go down to the picosecond (trillionth of a second), and some things are being measured at the femtoscond (quadrillionth of a second). There's even some zepto-sensitive devices out there. The smaller the time-slice, the better your pattern recognition. Even back as far as Barabási's Bursts (2010) we knew that high resolution temporal patterns could be really informative of network behavior. More people in your study is also helpful, but networks like Facebook are running out of people* (but bots are infinite!), so they have to find another way of mining user data, hence an interest in temporal activity.
If you think the fine-grained timing of patterns isn't that big a deal, why don't you ask the New York Stock Exchange, which is actually not located on Wall Street, but a few miles away in Carteret, New Jersey, because high frequency trading algorithms compete with each other at such high speeds (the speed of light) that they need the extra light-seconds provided by placing their servers a few miles closer to the international trunk connecting the Internet to the United States (it's called a latency advantage; and that trunk actually lands in Manasquan, a town on the Jersey Shore; still, closer to Carteret than New York).
The point here is that light travels fast, and if we want to understand it, then we will need to look at smaller and smaller slivers of the clock, and add this to the growing body of knowledge about how.
On Olfactory Timing
Think about it -- the brain is a network of billions of blinking neurons. Consciousness, or any neural activity, is an intelligent organization of these blinking neurons into meaningful patterns. But when you have billions of lights blinking on and off at the same time, you need to start slicing up the "time," so it's not all happening "at once."
In the world of olfactory perception, a new look at the temporal side of things should supplement the intense interest we're seeing in this subject (check out the books Smellosophy or Nose Dive, and stutter at the thought of a $2 million grant in olfactory artifact research).
The author of Smellosophy, Barwich, actually gets into this aspect of olfaction. For her, it's a major distinction in the way we categorize olfactory information - between the historical approach to "mapping" the receptors of the two-dimensional olfactory bulb to "measuring" the timing of receptor activity, which includes the temporal dimension.
She argues that the measurement approach can reveal a key to the coding of the olfactory network. Olfaction is not just about identifying the different molecules present and their combinatorial patterns, but about when they appear and at what concentrations, and how these concentrations change over time. After all, olfaction is about detecting change, which implies time.
She says, "Rather than molecules, your brain depicts transient information patterns" (p246)
-Smellosophy: What the Nose Tells the Mind, A.S. Barwich, 2020, Harvard University Press
And here's a recent article on the topic:
Fast changing smells can teach mice about space
May 2021, phys.org
Mice can sense extremely fast and subtle changes in the structure of odors and use this to guide their behavior. The findings, published in Nature today, alter the current view on how odors are detected and processed in the mammalian brain.via The Francis Crick Institute: Ackels, T., Erskine, A., Dasgupta, D. et al. Fast odour dynamics are encoded in the olfactory system and guide behaviour. Nature (2021). doi.org/10.1038/s41586-021-03514-2
Start with the 2014 article from Princeton (cited below) that said Facebook is losing its users like a susceptible-infected-recovered model of disease transmission, and that it would be done by 2018. Granted the article came from the unlikely Department of Mechanical and Aerospace Engineering in Princeton, and despite all the bad press, we can now fast forward to 2018 when Facebook is suspected to be more bots than people. Or is it that the people are acting more bot-like as they co-evolve with engagement algorithms in the artificial arena of natural selection. Bottom line is, social media networks, and all technologies adopted by individuals in a society, will follow a similar disease model.
Not to mention, the bad press (possibly influenced by the same entity that took a full-page ad in the New York Times btw, and somewhere around 100K in case you were wondering) didn't age well.
But I'll let you be the judge of that:
"Facebook may be a massive drain on our attention that some people get sick of, but that doesn’t mean it actually operates like a virus." -TechCrunch, 2014
Epidemiological modeling of online social network dynamics, John Cannarell, Joshua A. Spechler. Department of Mechanical and Aerospace Engineering, Princeton University. arxiv:1401.4208v1 [cs.SI] 17 Jan 2014. https://arxiv.org/pdf/1401.4208v1.pdf
Post Post Script:
Zeptoseconds - New world record in short time measurement
Oct 2020, phys.org