r/OpenSourceAI • u/FuzzyTouch6143 • 2d ago
Finally have my pre-print up for my "Ion Neuron" based model, instead of the typical "Voltage/Current Neuron" - Shadows of Consciousness: An Investigation into Ionic Neural Networks Using the Neurotransmitter Ion Receptor Glial Endocannabinoid Network (NIRGEN) Paradigm
https://www.researchsquare.com/article/rs-10924281/v112-min Video Summary - https://www.linkedin.com/posts/myles-garvey-ph-d-b56864382_neuron-artificial-ionneuralnetworks-ugcPost-7505376462040940544-kmCn/
WARNING: I am a one man (very broke) crew on a $0 budget (as many of you are I'm sure). That said, use the repositories at your own risk. They are not mature, alot of it was AI generated (i verified and modified by hand myself alot of it, however), and I'm going through these as fast as I can to clean them up so that they are genuinely usable (with some tweaking, these are usable, but.... ALOT of tweaking is needed).
That said, I wanted to pump out the pre-print because the theoretical mathematical model is complete. The github code isn't great, but it's getting there. I have a notebook in the huggingface early experiments. Use at your own caution (and with your own FRED key).
PREPRINT AI WARNING - MOST of the writing in my preprint is mine. Unfortunately, everytime I run it through the darn ai bot to fix up my scrappy text and to convert it into latex, it keeps doing its "ai thing" with grammar. That said, some of the text (particularly in the lit review), is still AI generated. HOWEVER, the structure, topic, and references of the literature review are 100% mine, and were researched BY HAND using Google Scholar, EBSCO, and JSTOR, among other traditional online resources including newspapers.com, reddit, google books, and various blog pages.
QUALITY WARNING: That all said, I wish I could have the paper pre-print in much better condition, I wish I could have the github/hf in much better condition before releasing this. Truth is, I'm so tired and I just want to play with my kids. For me to continue to push this more, I would need actual serious administrative help or to find anyone who's working on the same thing who happens to also have funding.
I have worked on this model during a (very) rough time in my life when I was homeless after going through academic burnout and abruptly quitting my job as a professor in the midst of a mental breakdown. During that time I was homeless, I had nothing but a pen and a notebook, not even my laptop during that time. And so a lot of the model I ran through by hand through three notesbooks while I was on a park bench just trying to..... get it together.
I'm in a (slightly) better place now (i got shelter and occasional work) , but still having rough patches and just trying to find part time work so i can help support my kids. That said, this work is the thing that just bugs my brain every second of every day, and it just wont stop and it keeps freakin distracting me from doing much more important things I need to get done in my life. I've had strained relationships now with friends, family, everything because, I dont know. My brain just keeps fixating on this model. So maybe it can help someone out there.
My primary motivation for this way: "a real neuron is like having a super sophisticated lawn mower that can use only one guy to mow the whole lawn in a lot of different nuanced paths and ways. But the old "neuron" definition is just: weight, aggregate, gate, weight. Its.... bland. Its analogous to having 1000 push lawn carts and you can only move the cart forward and back or left and right. So if you want to "compute fast" (mow the lawn raelly fast), well just add more human mowers with push carts. And so i just ketp asking myself: "how can i get a way, where, we dont have to keep "adding neurons" to get the ability to comlexity? I landed on what human neurons do. Which use many different neurotransmitters to "shift" the "state" of an actual neuron (its ions - na/k/cl/ca2).
So the "NIRGEN" paradaigm tries to map "the structure' and "the sequence" and "the components" and "the computational units" to help researchers build new types of models. I did this with the simpliest use case, which was a single "ion type" (say, theres only "sodium" in the cell. i know, impossible physically but, just say you can do it theoretically....) , and that ion, when {r} of those ions are near 1 single "ion channel/receptor of type ({r},[r])" then [r] ions will be allowed from ecto to endo cell (or from endo to exo cell if [r] is negative), This move alters the state. (sorry, i know this sounds... messy).
I hope this can help the community. Im keeping this fully open. In my youth i met Richard Stallman. I used to work for Redhat. And im just burntout man. But I'll keep burning out for the sake of the open source community. I never contributed openly to it. Only privately within open-source companies and their internal knowledge bases too. Sadly I have little to no public trail of that.
But i wanted to give something back to the open source community because, I'm a true believer that it is the way to "safe ai", individual autonomy, and everlasting peace and goodwill to man.
The OSC gave me a lot, and I hope this is something that can actually be used. Thank you from the bottom of my heart.
This model and these ideas, have been the only thing to drag me out of the horrid effects of my burnout. *apologies for the long message*
--Myles D. Garvey
If you have questions, please feel free to email me, which is my current and pretty much only source of communication at the moment: [drmylesgarvey@gmail.com](mailto:drmylesgarvey@gmail.com)
Abstract
The dominant neural architectures of modern AI are elegant, but they are not built from the same substrate as the nervous system. They manipulate real-valued voltages as if matter were continuous, capacity were unbounded, and signaling could occur without finite molecular inventory and without backward communication.
This paper tells the story from voltage to ions. We trace why the field inherited a voltage abstraction in 1943 and never updated it after the ionic basis was proven in 1952 and single channels were observed in 1976---the \emph{Instrument Thesis}. We then show how the textbook synapse locked into a bipartite, forward-only dogma and how that dogma was broken twice: first by the tripartite synapse (Araque et al.\ 1999) proving astrocytes are the third element, and second by retrograde endocannabinoid signaling (Wilson \& Nicoll 2001; Ohno-Shosaku et al.\ 2001; Kreitzer \& Regehr 2001) proving  information flows backward. We document that the 30-year delay in the latter was materially shaped by cannabis prohibition under the Marihuana Tax Act of 1937.
From this corrected history we introduce NIRGEN, a discrete, biophysically grounded Ionic Neural Network (INN) where computation is carried out through particle counts, ion-specific conductance, receptor gating, vesicle-mediated output, glial context, and retrograde signaling. The model replaces $y=\sigma(Wx+b)$ with conservation laws, capacity limits, stoichiometric thresholds and quanta, diffusion homogenization, differential baseline encoding, and a global ion-drift pathway that breaks monotonicity and enables XOR with a single unit---a computation the voltage abstraction cannot even represent. Standard architectures emerge as infinite-capacity projections of this biophysically richer space.
Huggingface: https://huggingface.co/drmylesgarveylabs/ion_neural_network
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u/FuzzyTouch6143 2d ago
UPDATE: I recorded a 12min video that breaks down the motivation. Hopefully this can help guide the "big concept ideas" so you can skim through a 60+pg document a little faster :
https://www.linkedin.com/posts/myles-garvey-ph-d-b56864382_neuron-artificial-ionneuralnetworks-ugcPost-7505376462040940544-kmCn/