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New neural network architecture inspired by neural system of a worm

quantamagazine.org

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Re: New neural network architecture inspired by neural system of a worm

#61

Post by heavenlyblue »

babblingfish wrote: post #19
dvh wrote: post #14

The old neuroscience saying goes like this: "Human brain have billions of neurons and so it is too complex to understand, that's why neuroscience study simpler organisms. Flatworm's brain have 52 neurons. We have no idea how it works". Did finally something changed in this regard?

There have been projects to systematically catalog all the synapses in the flatworm. The problem is that neural plasticity means these connections change dynamically over time based on the needs of the organism. Since the only way we can study the flatworm at the synapse level is by killing the worm and mounting it on slides and staining it and viewing it through a high power microscope, we can only analyze its struc…

> This is why even a simple organism can have basically an unlimited amount of complexity. To understand a dynamic system like this would require very precise measurements of very small particles in vivo which is currently impossible with our tools.

Even a computer has an unlimited amount of complexity (2 pow GIGABYTES_OF_RAM possible memory states), yet we have abstracted it into an abstract machine that is well understood.

Re: New neural network architecture inspired by neural system of a worm

#62

Post by heavenlyblue »

mr_toad wrote: post #40
rmorey wrote: post #16

Earlier quoted context omitted.

Yes. The C. Elegans brain (~300 neurons) was the first organism to be completely mapped to a connectome (the map of all connections). The first complete connectome of any centralized brain, the fruit fly, is about to be completed by the Flywire project ( https://home.flywire.ai/ ) ~100,000 neurons and ~70,000,000 synapses. We have just a little idea how it works ;)

A bit of extrapolation might suggested we could map out the connectome of a human brain in 40-50 years. Not that I’d suggest a linear extrapolation from two data points…

Are you willing to kill as many humans as there were flies killed when doing that?

Re: New neural network architecture inspired by neural system of a worm

#63

Post by ly3xqhl8g9 »

laughingman2 wrote: post #60
ly3xqhl8g9 wrote: post #21

Earlier quoted context omitted.

"I’m skeptical that biological systems will ever serve as a basis for ML nets in practice" First of all, ML engineers need to stop being so brainphiliacs, caring only about the 'neural networks' of the brain or brain-like systems. Lacrymaria olor has more intelligence, in terms of adapting to exploring/exploiting a given environment, than all our artificial neural networks combined and it has no neurons because it is…

I am working on RL and robotics. I came across Levin in Lex's podcast. And then went on a binge of his other podcast appearences. I agree totally with you, I would very much like to build agents that adapt to different circumstances like "simple organisms". I am not familiar with biology, but I plan to build competence here to follow Levin's work to a point that I could potentially collabrate with biologists or learn…

I also focused on the work done by the Levin lab after the Sean Carroll podcast [1]. In order to familiarize myself with the subject matter in a more practical manner I started writing a wrapper and a frontend, BESO [2], BioElectric Simulation Orchestrator, for BETSE [3], the Bio Electric Tissue Simulation Engine developed by Alexis Pietak which is used by the Levin lab to simulate various tissues and their responses based on world/biomolecules/genes/etc. parametrization. Reading the BETSE source code, the presentation [4], and some of the articles referred through the source code has been a rewarding endeavour. Some other books I consulted, somewhat beginner friendly were:

    2018, Amit Kessel, Introduction to Proteins. Structure, Function, and Motion, CRC Press
    2019, Noor Ahmad Shaik, Essentials of Bioinformatics, Volume I. Understanding Bioinformatics. Genes to Proteins, Springer
    2019, Noor Ahmad Shaik, Essentials of Bioinformatics, Volume II. In Silico Life Sciences. Medicine, Springer — less basics, more protocol-oriented
    2021, Karthik Raman, An Introduction to Computational Systems Biology. Systems-Level Modelling of Cellular Networks, Chapman and Hall
    2022, Tiago Antao, Bioinformatics with Python Cookbook. Use modern Python libraries and applications to solve real-world computational biology problems, Packt
    2023, Metzger R.M., The Physical Chemist's Toolbox, Wiley — a beautiful story of mathematics, physics, chemistry, biology; gradually rising in complexity as the universe itself, from the whatever (data) structure the universe was before the Big Bang to us, today.

    somewhat more technical:
    2014, Wendell Lim, Cell Signaling. Principles and Mechanisms, Routledge
    2021, Mo R. Ebrahimkhani, Programmed Morphogenesis. Methods and Protocols, Humana
    2022, Ki-Taek Lim, Nanorobotics and Nanodiagnostics in Integrative Biology and Biomedicine, Springer
In video format I particularly watched Kevin Ahern's Biochemistry courses BB 350/2017 [5], BB 451/2018 [6], Problem Solving Videos [7].

[1] https://www.youtube.com/watch?v=gm7VDk8kxOw

[2] not functional yet, https://github.com/daysful/beso

[3] https://github.com/betsee/betse

[4] BETSE 1.0, https://www.dropbox.com/s/3rsbrjq2ljal8dl/BETSE_Documentatio...

[5] https://youtu.be/JSntf0iKMfM?list=PLlnFrNM93wqz37TUabcXFSNX2...

[6] https://youtu.be/SAIFs_Mx8D8?list=PLlnFrNM93wqyay92Mi49rXZKs...

[7] https://youtu.be/e9khXFSU6r4?list=PLlnFrNM93wqzeZvsE_GKes91C...

Re: New neural network architecture inspired by neural system of a worm

#64

Post by d4rkp4ttern »

Maybe I have missed it but I don’t know why there is not much talk about simulating evolution at high speed: brains evolved over millions of years to adapt to the environment and ensure survival. So instead of trying to understand and reproduce brain structures, we instead simulate evolution of embodied agents ultra high speed and see if some paths lead to brains comparable to today’s organisms.

Re: New neural network architecture inspired by neural system of a worm

#65

Post by jamesk_au »

d4rkp4ttern wrote: post #64

Maybe I have missed it but I don’t know why there is not much talk about simulating evolution at high speed: brains evolved over millions of years to adapt to the environment and ensure survival. So instead of trying to understand and reproduce brain structures, we instead simulate evolution of embodied agents ultra high speed and see if some paths lead to brains comparable to today’s organisms.

For a fun exploration of this, see the short story “Crystal Nights”, by Greg Egan: https://www.gregegan.net/MISC/CRYSTAL/Crystal.html

Re: New neural network architecture inspired by neural system of a worm

#66

Post by dekhn »

ly3xqhl8g9 wrote: post #56
dekhn wrote: post #47

Earlier quoted context omitted.

If I had done synthetic biology my goal would have been to create cells that could reliably compute sine waves... by digitally computing taylor series polynomial approximations. Turns out engineering digital systems from cells is a remarkably challenging problem. Examples of "switches" in biology abound, my favorite simple one is the Mating Type of Yeast: yeast have two sex types, and swap a small region of DNA in-pl…

Not sure about polynomials, but how about "Genetic Regulatory Networks that count to 3" [1]. One of the interesting, counter-intuitive highlights from the paper: "Counting to 2 requires very different network design than counting to 3." [1] https://pubmed.ncbi.nlm.nih.gov/23567648

Unfortunately, that's entirely analog. my goal was to do digital computing- with all the reliability and predictability.

Re: New neural network architecture inspired by neural system of a worm

#67

Post by akomtu »

bGl2YW5j wrote: post #57
akomtu wrote: post #36

Imo, the next step in ML is unleashing the electron a bit. Right now we keep probabilistic electrons on leash in transistors, so they behave deterministically. This despotic method has taken us far, but without giving electrons some freedom back, we won't advance further.

I'm interested; could you expand on this please? How can we give electrons more 'freedom' and what would that result in?

Transistors could have microscopic chambers where electrons could go for a walk and exercise limited freedoms. Their behavior would be modulated by the external magnetic field to prevent riots.

Re: New neural network architecture inspired by neural system of a worm

#68

Post by mr_toad »

heavenlyblue wrote: post #62
mr_toad wrote: post #40

Earlier quoted context omitted.

A bit of extrapolation might suggested we could map out the connectome of a human brain in 40-50 years. Not that I’d suggest a linear extrapolation from two data points…

Are you willing to kill as many humans as there were flies killed when doing that?

You’d only need a fraction of the 100 million+ people who die every year. There are probably bigger ethical questions when it comes to simulating a human mind.

Re: New neural network architecture inspired by neural system of a worm

#69

Post by skibidibipiti »

mr_toad wrote: post #39
skibidibipiti wrote: post #13

Earlier quoted context omitted.

> There is no fundamental difference between information processing in silicon and in vivo A neuron has dozens of neurotransmitters, while artificial neurons produce 1 output. I don't know much about neurology, but how is the information processing similar? What do you mean are running the same instructions? > there is no other way for it to work Plants exhibit learned behaviors

> A neuron has dozens of neurotransmitters, while artificial neurons produce 1 output. I don't know much about neurology, but how is the information processing similar? What do you mean are running the same instructions? ANNs are general function approximations. You can get the same behaviour from a complex network of simple neurons that you get from a single more complex neuron.

[dead]

Re: New neural network architecture inspired by neural system of a worm

#70

Post by westurner »

ly3xqhl8g9 wrote: post #21
sillysaurusx wrote: post #3

It makes a good headline, but reading over the paper ( https://www.nature.com/articles/s42256-022-00556-7.pdf ) it doesn’t seem biologically-inspired. It seems like they found a way to solve nonlinear equations in constant time via an approximation, then turned that into a neural net. More generally, I’m skeptical that biological systems will ever serve as a basis for ML nets in practice. But saying that out loud fee…

"I’m skeptical that biological systems will ever serve as a basis for ML nets in practice" First of all, ML engineers need to stop being so brainphiliacs, caring only about the 'neural networks' of the brain or brain-like systems. Lacrymaria olor has more intelligence, in terms of adapting to exploring/exploiting a given environment, than all our artificial neural networks combined and it has no neurons because it is…

> Once you stop caring about the brain and neurons and you find out that almost every cell in the body has gap junctions and voltage-gated ion channels which for all intents and purposes implement boolean logic and act as transistors for cell-to-cell communication, biology appears less as something which has been overcome and more something towards which we must strive with our primitive technologies: for instance, we can only dream of designing rotary engines as small, powerful, and resilient as the ATP synthase protein [2].

But what of wave function(s); and quantum chemistry at the cellular level? https://github.com/tequilahub/tequila#quantumchemistry

Is emergent cognition more complex than boolean entropy, and are quantum primitives necessary to emulate apparently consistently emergent human cognition for whatever it's worth?

[Church-Turing-Deutsch, Deutsch's Constructor theory]

Is ATP the product of evolutionary algorithms like mutation and selection? Heat/Entropy/Pressure, Titration/Vibration/Oscillation, Time

From the article:

> The next step, Lechner said, “is to figure out how many, or how few, neurons we actually need to perform a given task.”

Notes regarding Representational drift* and remarkable resilience to noise in BNNs) from "The Fundamental Thermodynamic Cost of Communication: https://news.ycombinator.com/item?id=34770235

It's never just one neuron.

And furthermore, FWIU, human brains are not directed graphs of literally only binary relations.

In a human brain, there are cyclic activation paths (given cardiac electro-oscillations) and an imposed (partially extracerebral) field which nonlinearly noises the almost-discrete activation pathways and probably serves a feed-forward function; and in those paths through the graph, how many of the neuronal synapses are simple binary relations (between just nodes A and B)?

> The group also wants to devise an optimal way of connecting neurons. Currently, every neuron links to every other neuron, but that’s not how it works in C. elegans, where synaptic connections are more selective. Through further studies of the roundworm’s wiring system, they hope to determine which neurons in their system should be coupled together.

Is there an information metric which expresses maximal nonlocal connectivity between bits in a bitstring; that takes all possible (nonlocal, discontiguous) paths into account?

`n_nodes*2` only describes all of the binary, pairwise possible relations between the bits or qubits in a bitstring?

"But what is a convolution" https://www.3blue1brown.com/lessons/convolutions

Quantum discord: https://en.wikipedia.org/wiki/Quantum_discord

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