• Electro-active polymer hydrogels are being explored as low-cost, 3-D-printable “probabilistic reservoir automata” that can perform reservoir-style computation and exhibit emergent memory when embodied in game-like environments (Cell iScience 2022, Cell Reports Physical Science 2024).

• Participants discussed translating trained machine-learning models directly into hydrogel structures, then using machine-learning again to optimize the fidelity of this translation process.

• Hydrogels’ self-healing, stimulus-responsive swelling/shrinking, and compatibility with embedded nanoparticles (piezoelectric, photocleavable, etc.) were highlighted as levers for programmable mechanical and electrical behavior.

• Links to open-source and DIY projects were shared, including the Arbortron simulator for “large physically evolving networks” (GitHub: ZhangWS/arbortron) and Knowm’s wet-ware memristor/gel prototypes.

• Physical neural networks were extended to non-hydrogel media: 3-D-printed plastic slabs that function as diffractive optical networks at microwave or optical frequencies (Science 2018, Nature Sci. Rep. 2016).

• Reservoir computing concepts were reinforced with recent papers on FORCE-trained living neuronal cultures, attention-enhanced reservoirs, and biological neurons acting as generalization filters (APL 2021, arXiv 2023, arXiv 2022).

• A 1998 report on “passive microwave logic” was resurfaced as prior art for all-printed, non-semiconductor computing substrates.

• The conversation repeatedly connected these physical substrates to morphological computing and energy-based models, citing the Extropic.ai vision of stochastic analog circuits as a unifying framework.