--- Log opened Fri Aug 23 00:00:30 2024
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01:11 < hprmbridge> kanzure> "Electroactive polymer gels as probabilistic reservoir automata for computation" https://www.cell.com/iscience/fulltext/S2589-0042(22)01830-2
01:12 < hprmbridge> kanzure> "Electro-active polymer hydrogels exhibit emergent memory when embodied in a simulated game environment" https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(24)00436-3
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07:23 < hprmbridge> kanzure> "Biological neurons act as generalization filters in reservoir computing" https://arxiv.org/abs/2210.02913
07:24 < hprmbridge> kanzure> "Physical reservoir computing with FORCE learning in a living neuronal culture" https://pubs.aip.org/aip/apl/article/119/17/173701/1065020/Physical-reservoir-computing-with-FORCE-learning
07:25 < hprmbridge> kanzure> "Recent advances in physical reservoir computing: A review" https://www.sciencedirect.com/science/article/pii/S0893608019300784#sec8
07:28 < hprmbridge> kanzure> "The idea of outsourcing computation to a physical body is known as morphological computing in the field of robotics (
07:28 < hprmbridge> kanzure> Pfeifer & Bongard, 2006). Soft and compliant robots are typically composed ofdeformable bodies. A primitive model available as a physical reservoir is a mass–spring network, which can be regarded as coupled mechanical oscillators as described in Section 3.3. A mass–spring network reservoir where mass points are randomly connected to neighboring mass points via nonlinear springs was proposed in
07:28 < hprmbridge> kanzure> Hauser, Ijspeert, Füchslin, and Maass (2011)"
07:30 < hprmbridge> kanzure> sounds like the energy-based models described here https://www.extropic.ai/future "There is a direct connection between this mechanical picture and the parameterized stochastic analog circuits that make up Extropic accelerators. The lightweight particles represent electrons, and the liquid molecules represent the atoms of the conducting medium, which can transfer energy to the electrons upon
07:30 < hprmbridge> kanzure> collision. The springs represent circuit components that confine the motion of the electrons, such as inductors or transistors. Control voltages/currents can be applied to tune the values of these components, changing the distribution from which the circuit samples"
07:31 < hprmbridge> kanzure> anyway, if we can get the same results with hydrogels that's pretty exciting because among other things we can probably print pre-designed hydrogels
07:31 < hprmbridge> kanzure> and there's a wide variety of materials we can mix into the hydrogels to explore different performance across a range of voltages etc
07:32 < hprmbridge> kanzure> hope the performance is ok.
07:32 < hprmbridge> kanzure> maybe we can directly translate machine learning models from software into hydrogels after training.
07:33 < hprmbridge> kanzure> (and the fidelity of that process itself can be subjected to machine learning)
07:42 < hprmbridge> kanzure> 3d printing of hydrogels https://onlinelibrary.wiley.com/doi/abs/10.1002/adfm.201900971
07:43 < hprmbridge> kanzure> hm that's not the best review
07:45 < hprmbridge> kanzure> they probably have self-healing properties and maybe you can excuse, cut, and surgically recombine different gels together to get aggregate behavior you want
07:46 < hprmbridge> kanzure> excuse -> excise
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08:23 < hprmbridge> kanzure> "Certain hydrogels can repair themselves after being cut or damaged. This self-healing ability is due to reversible bonds or dynamic crosslinking within the polymer network. Hydrogels can swell or shrink in response to specific stimuli, such as changes in temperature, pH, light, or the presence of certain ions or chemicals. They can also shrink due to the evaporation of their water and later be
08:23 < hprmbridge> kanzure> re-hydrated."
08:25 < hprmbridge> kanzure> you could embed piezoelectric nanoparticles.
08:31 < hprmbridge> kanzure> you could do photocleavage, photooxidation or photoactivation for ion patterning, or projection photolithography to cure polymer crosslinking in predefined patterns (low resolution is not a problem because you can have very large gels if you want to, instead of needing everything to be nano lithography scale)
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10:51 < hprmbridge> kanzure> "Advances in the labelling and selective manipulation of synapses" https://www.nature.com/articles/s41583-024-00851-9
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11:12 < hprmbridge> kanzure> https://github.com/ZhangWS/arbortron
11:13 < hprmbridge> kanzure> "Fabrication and programming of large physically evolving networks" https://scholar.archive.org/work/uxoexwgmnnhmhbxsc2g2pqqlo4/access/wayback/http://server17.how-why.com/blog/netfiles/arbortronsimplycomplexfinal.pdf (2011)
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11:19 < hprmbridge> kanzure> well that's a cool paper
11:29 < hprmbridge> kanzure> https://knowm.org/how-to-build-the-ex-machina-wetware/
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11:31 < geneh2> https://knowm.org/how-to-build-the-ex-machina-wetware/
11:36 < hprmbridge> kanzure> oh he actually built some stuff?
11:37 < hprmbridge> kanzure>  https://cdn.discordapp.com/attachments/1064664282450628710/1276611343662710814/image0.jpg?ex=66ca2876&is=66c8d6f6&hm=6fd22f8aa351a58dbd241366e07c34a183ea910ecc7000b87916591dec52f479&
11:38 < hprmbridge> kanzure> has someone who actually knows modern machine learning looked into this?
11:39 < geneh2> don't know. they also mention some other gel learning stuff too
11:39 < hprmbridge> kanzure> like it seems like the obvious thing to do is large scale training runs and see what happens
11:40 < geneh2> there are also physical neural networks. you could print some weird shaped plastic and have it work as a NN when hit with microwaves
11:40 < hprmbridge> kanzure> why does plastic work for that?
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11:41 < geneh2> dielectric I think. I know you can make microwave lenses from wax, so I'm extrapolating a bit here
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11:53 < superkuh> More often just done with optical light rather than microwaves. But as adjusting phase by altering the effective dielectric constant is easy with 3d printing, then you just need some way to do power coupling and you've got your gates, ie, http://erewhon.superkuh.com/library/Electromagnetics/Radio%20Defined%20Software/Ultra%20High%20Speed%20Digital%20Processing%20for%20Wireless%20Systems%20using%20Passive%20Microwave%20Logic_%20B%20Krishnamachari_%
11:53 < superkuh> 20S%20Lok_%20C%20Gracia_%20S%20Abraham_%201998.pdf
12:01 < hprmbridge> kanzure> https://gershmanlab.com/pubs/memory_synthesis.pdf
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12:21 < hprmbridge> poshify> hey everyone
12:22  * L29Ah poshifies poshify
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12:54 < hprmbridge> kanzure> arbortrons playing tetris https://research.physics.illinois.edu/ICMT/Workshops/Fluctuations2011/Talks/Hubler_Alfred_ICMT_May_2011.pdf
12:56 < hprmbridge> poshify> so @kanzure
12:56 < hprmbridge> poshify> https://arstechnica.com/science/2021/09/liquid-metal-encased-in-hydrogel-makes-a-promising-energy-harvesting-device/
12:56 < hprmbridge> kanzure> there's a lot of weird stuff you can do with hydrogels
12:59 < hprmbridge> poshify> anything that can be twisted and locked into position can function as memory and potential energy
12:59 < hprmbridge> poshify> that's what it reminded me of
12:59 < hprmbridge> kanzure> in molecular biology they use gels for size separation of proteins, DNA, etc
13:01 < geneh2> https://www.nature.com/articles/srep32197
13:03 < geneh2> https://www.science.org/doi/10.1126/science.aat8084
13:04 < geneh2> diffractive neural networks made from printed plastic
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13:58 < hprmbridge> kanzure> "Attention-enhanced reservoir computing" https://arxiv.org/abs/2312.16503
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18:33 < hprmbridge> kanzure> "3D patterning within hydrogels for the recreation of functional biological environments" https://onlinelibrary.wiley.com/doi/abs/10.1002/adfm.202009574
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--- Log closed Sat Aug 24 00:00:31 2024