Showing posts with label information. Show all posts
Showing posts with label information. Show all posts

Monday, November 29, 2021

Signal to Noise for the Win


A new model for how the brain perceives unique odors
Oct 2021, phys.org

So here is a scientist, a physicist by name, with an interest in the information-processing abilities of biological, and neurological systems. It didn't take him long, I would imagine, to realize that olfaction is a prime model for complex information-processing systems; it was the first sense, used by the first bacteria to detect chemicals in the primordial soup, later used by animals to make a big, complex mammal brain. If you want to look at how information processing happens in biological systems, this would be the ideal place to look.

But first, you have to throw into the garbage everything you know about olfaction already, which should be easy for a computational information scientist. What they did here was to look far out, all the way out -- beyond molecules and their myriad physico-chemical characteristics, of which the molecules themselves number in the billions; beyond genetics and their 30% variation across the global population; beyond cultural effects that are almost too surreal to quantify for methodical research purposes, like where Americans prefer peppermint since it's associated with candy, yet older folks from England don't like mint since it's associated with pain-relief products of their era, or the easier comparison of preference for durian fruit or Époisses de Bourgogne cheese.

Too messy, said the computational information scientist. And so they put it all in the blender, all those variables together. And they called it noise. Boil it all down, cancel it all out, all that dirty data of molecules and genes and cultural and personal association. Throw it all in the same bin, and call it noise. That's what they did.

Actually, they didn't call it noise, they called it "context" --

 "If you experience odors in a similar context, even if they were initially rather different in the responses they evoked in the nose, they begin to be represented by similar neural responses so they become the same in your head," Balasubramanian says.

The researchers found that their simplified model could be used to reproduce the same types of results seen in olfaction experiments. It's something that Balasubramanian did not expect to see, as he thought that such a complex process would require "learning and plasticity" in order to adapt and change neural synapses to modify the brain's representation of smells. "We may have found a general strategy of using certain kinds of randomized signals to entrain those effects," he says about their results. "It doesn't have to be just olfaction; it can be elsewhere, too." -medicalexpress

Did you see that? "It doesn't have to be olfaction; it can be elsewhere too." Olfactively-piqued, computational information neuroscientist, where have you been? Proving that the nose-brain is the neural model par excellence, while showing us how it actually works, both at the same time.

*If you want to know more about why olfaction is the ideal model for growing an artificial brain from scratch, it's a constant theme in Hidden Scents.

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

Post Script:
This study shouldn't be mentioned without this other study, where they taught an artificial network how to smell, 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

Post Post Script:
Might as well put this here, since it has to go somewhere -- what happens when you take an information scientist and give them an olfactory science problem? This is what happens. They come up with an answer that is so simple it just makes you look stupid. This is an example, although not a true example, of the other half of the coming dark ages. After a global pandemic, it's inevitable to experience a kind of dark ages, where lots of people died, but way more people got sick, and also a lot of people retired. That's a post-pandemic-pandemic of institutional knowledge loss, like a collective long covid brain fog on our culture -- we forget how to do stuff, because the guy who did it for the past 30 years isn't doing it anymore. That guy either died, got too sick to work, or retired for a million other reasons, of which many of them could be pandemic-related. And there's lots of those guys (and even more of them gals). Who knows what that will look like for us today or tomorrow, but it's happening as we speak, and years from now we might notice, we might even call it the great forgetting. The flip side to the dark ages is the renaissance, which comes from all the new people in new roles and at new jobs. These people are coming in new, with nobody around to teach them how to do things "right," and although that makes for a bumpy road ahead, it also gets you things like this discovery, one of the hardest problems of olfaction taken on by someone who has not much at all to do with olfaction (although he should, because olfaction has been an information science problem all along).

Wednesday, July 26, 2017

On Very Large Databases


The American Society for Biochemistry and Molecular Biology has this prescription for a periodic table of proteins, organizing protein complexes based on simple rules, tens of thousands of protein complexes each with their own 3-d structures, let us recall the hypothetical smell network of all possible smells as they occur to all people – the Lingua Anosmia.

There is a strong connection between olfaction and the growing databases of bioinformatics, because smells are organic entities themselves.

There is another database I envy, the human metabolome. It contains 40,000 entries, all the metabolites that exist within and among the human body. This one has even closer affinity with olfaction, because lots of metabolites smell; and if they don't smell, they are the molecules that eventually separate and combine to make something that does smell. Knowing the relationships among the molecules associated with smelly activity can help to organize the resulting smells of said metabolic activity. Your body odor does not come from your body - unless we consider our microbiome to be part of our body. Molecules that exit your body via sweat are deposited on the skin, a buffet plate for the colonies of bacteria that live with us. They eat your sweat and shit the body odor that you tend to consider yours. The smell of the beach is a secondary metabolite of seaweed, which means the same thing – sea bacteria eat the waste, or the metabolites, of seaweed.

Yes, that beautiful, intoxicating, deep and alluring scent of the seashore is to the ocean what body odor is to our bodies.

In conclusion, metabolites, and many things biological, and in their new supersized databasable format, are a step closer to the realization of the hypothetical smell network, the Lingua Anosmia.

I’d like to ask the driven and capable reader to hook-up this human metabolome with some smell data; I’d love to see it. Had I the time and expertise, I'd like to hook it up myself, but alas; it's on my list.

“We’re bringing a lot of order into the messy world of protein complexes”
-Sebastian Ahnert

Long form description of the Human Metabolomic Database:
The database is designed to contain or link three kinds of data: 1) chemical data, 2) clinical data, and 3) molecular biology/biochemistry data. The database contains 41,993 metabolite entries including both water-soluble and lipid soluble metabolites as well as metabolites that would be regarded as either abundant (> 1 uM) or relatively rare (< 1 nM). Additionally, 5,701 protein sequences are linked to these metabolite entries. Each MetaboCard entry contains more than 110 data fields with 2/3 of the information being devoted to chemical/clinical data and the other 1/3 devoted to enzymatic or biochemical data. Many data fields are hyperlinked to other databases (KEGG, PubChem, MetaCyc, ChEBI, PDB, UniProt, and GenBank) and a variety of structure and pathway viewing applets. The HMDB database supports extensive text, sequence, chemical structure and relational query searches. Four additional databases, DrugBank, T3DB, SMPDB andFooDB are also part of the HMDB suite of databases. DrugBank contains equivalent information on ~1600 drug and drug metabolites, T3DB contains information on ~3600 common toxins and environmental pollutants, SMPDB contains pathway diagrams for ~700 human metabolic and disease pathways, whileFooDB contains equivalent information on ~28,000 food components and food additives.

Citing the Human Metabolome Database:
1. Wishart DS, Tzur D, Knox C, et al., HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007 Jan;35(Database issue):D521-6. 17202168
2. Wishart DS, Knox C, Guo AC, et al., HMDB: a knowledgebase for the human metabolome.Nucleic Acids Res. 2009 37(Database issue):D603-610. 18953024

3. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, et al., HMDB 3.0 — The Human Metabolome Database in 2013. Nucleic Acids Res. 2013. Jan 1;41(D1):D801-7. 23161693

Sunday, May 8, 2016

Recognizing Science Communication


Actor, director, writer, and science communicator Alan Alda in his MASH days

Science can be confusing. It’s great to see science communication being recognized as a public welfare.

From the Alan Alda Center for Communicating Science:
The National Academy of Sciences is presenting its 2016 Public Welfare Medal to actor, director, writer, and science communicator Alan Alda in recognition of his "extraordinary application of the skills honed as an actor to communicating science on television and stage, and by teaching scientists innovative techniques that allow them to tell their stories to the public." The medal is the Academy's most prestigious award, established in 1914 and presented annually to honor extraordinary use of science for the public good.”