Scientists use machine learning to predict smells based on brain activity in worms
Jan 2022, phys.org
Putting this here because they used graph theory aka network science to decode the otherwise cacophony of neuronal crosstalk involved in smelling.
Also, why C. elegans? It has only 302 neurons, that's why:
Chalasani's team set out to study how C. elegans neurons react to smelling each of five different chemicals: benzaldehyde (almond), diacetyl (popcorn), isoamyl alcohol (banana), 2-nonanone (cheese), and sodium chloride (salt).The researchers engineered C. elegans so that each of their 302 neurons contained a fluorescent sensor that would light up when the neuron was active.By looking at basic properties of the datasets—such as how many cells were active at each time point—Chalasani and his colleagues couldn't immediately differentiate between the different chemicals. So, they turned to a mathematical approach called graph theory, which analyzes the collective interactions between pairs of cells: When one cell is activated, how does the activity of other cells change in response?The algorithm was able to learn to differentiate the neural response to salt and benzaldehyde but often confused the other three chemicals.
via Salk Institute, Cold Spring Harbor Laboratory and UC San Diego: Javier J. How et al, Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans, PLOS Computational Biology (2021). DOI: 10.1371/journal.pcbi.1009591
Image credit: AI Art - AI Makes a Nose Face - 2022
a highly detailed, macro shot of a human nose, 8k, depth of field
The art of smell: Research suggests the brain processes smell both like a painting and a symphony
Apr 2022, phys.org
"These findings reveal a core principle of the nervous system," using a model to simulate the workings of the early olfactory system. This is a reminder that the olfactory system is an ideal model for understanding the brain.
In their computer simulation, they found that centrifugal fibers switched between two different modes -- one worked on a specific instant in time, while the other worked on the neural patterns across time.
This is where I make a further interpretation, which might be incorrect, but it seems like one is for comparing a smell to the body's repository (is this good or bad for me? have I smelled this before? where? who was I with?) and the other mode is for comparing the smell against itself, over time, perhaps to learn whether it's getting stronger or weaker. One uses autobiographical, physiological memory, and the other uses basic chemotaxis. One ontogeny and the other phylogeny?
Anyway, another reminder by one of the authors that the olfactory system is a good model: "Computational approaches inspired by the circuits of the brain such as this have the potential to improve the safety of self-driving cars, or help computer vision algorithms more accurately identify and classify objects in an image." -Krishnan Padmanabhan, associate professor of Neuroscience at University of Rochester School of Medicine and Dentistry
via University of Rochester Medical Center: Zhen Chen et al, Top-down feedback enables flexible coding strategies in the olfactory cortex, Cell Reports (2022). DOI: 10.1016/j.celrep.2022.110545
Sniffing out the brain's smelling power
Oct 2022, phys.org
(Out of order but seemingly related to the above) Here's another way of thinking of the two processes to smelling -- We said mitral cells are what do the smelling, but mostly because those were the ones we could see. Tufted cells were harder to see, up until now -- they find that the mitral cells were faster, more discriminating, and more broadly-tuned.
The authors think the mitral cells only enhance important smells, but the tufted cells are part of a background process for identity and intensity.
via Cold Spring Harbor Laboratory: Honggoo Chae et al, Long-range functional loops in the mouse olfactory system and their roles in computing odor identity, Neuron (2022). DOI: 10.1016/j.neuron.2022.09.005
a straight smooth vertical tube with the texture of human skin, highly realistic, hyper-real, 4k, Octane render
Researchers map mouse olfactory glomeruli using state-of-the-art techniques
Apr 2022, phys.org
While other research teams previously examined the organization of glomeruli in the olfactory bulb, so far they only identified the positions of a limited subset of these clusters. As a result, the relationship between the location of glomeruli and odor discrimination has been very difficult to infer.They used a combination of single-cell RNA sequencing, spatial transcriptomics and machine learning techniques. This allowed them to create a map that outlined the brain regions where most of the sensory neurons in the mouse olfactory bulb sent odor-related information.
via University of Massachusetts Medical School, Broad Institute of Harvard and MIT, and Stanford University: I-Hao Wang et al, Spatial transcriptomic reconstruction of the mouse olfactory glomerular map suggests principles of odor processing, Nature Neuroscience (2022). DOI: 10.1038/s41593-022-01030-8
How mosquito brains encode human odor so they can seek us out
May 2022, phys.org
Of the two nerve centers, one responds to many smells including human odor, essentially saying, "Hey, look, there's something interesting nearby you should check out," while the other responds only to humans. Having two may help the mosquitos home in on their targets, the researchers suggest.First genetically engineer mosquitos whose brains lit up when active, and then deliver human-flavored air (with decanal and undecanal)."When I first saw the brain activity, I couldn't believe it—just two glomeruli (out of 60) were involved. That contradicted everything we expected, so I repeated the experiment several times, with more humans, more animals. I just couldn't believe it. It's so simple."
via Princeton: Carolyn McBride, Mosquito brains encode unique features of human odour to drive host seeking, Nature (2022). DOI: 10.1038/s41586-022-04675-4