tag > Complexity
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Introducing OscNet: A JAX library for oscillatory neural networks and dynamical systems.
OscNet provides a framework for building and training neural networks based on oscillatory dynamics — coupled oscillator networks, continuous-time neural networks, and general dynamical systems. Built on JAX and Equinox for differentiable, high-performance computation. https://github.com/samim23/oscnet
Features
- Oscillator models: Harmonic, Van der Pol, Stuart-Landau, Kuramoto, FitzHugh-Nagumo
- Coupling topologies: Hierarchical fractal, power-law, log-periodic
- Analysis tools: Edge-of-chaos, Floquet analysis, bifurcation, stability
- Training utilities: Criticality initialization, stochastic forcing, schedulers
- Visualization: Phase space, network dynamics, oscillator analysis
More on the projects background: In Resonance with Nature - Toward a New Kind of Machine Learning with Oscillatory Neural Networks
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Machine Learning for the Genetic Engineering of Fungal Secondary Metabolite Clusters: A Novel Framework for Alien Communication
The Search for Extraterrestrial Intelligence (SETI) has long scanned the skies for radio signals. But what if an advanced civilization's signature isn't a broadcast, but a biological program?
Hypothesis
Mastery over a planet's biology is the ultimate technology. Instead of fleeting electromagnetic waves, a persistent, self-replicating message could be encoded into the very blueprint of life - an engineered gene cluster in a hardy organism like a fungus, designed to survive deep space and seed itself on new worlds.
Fungal Gene Clusters: Nature's Data Cassette
Fungal secondary metabolite (SM) clusters are modular genetic programs. Enzymes like Polyketide Synthases (PKSs) are built from domains (A-T-C-KR-ER-ACP...) in a precise order. This sequence is a code. In theory, it could be repurposed. A non-functional, hyper-complex cluster discovered in a space-borne spore might not be for making a toxin - it could be a biochemical QR code, a message etched in DNA that can last for eons and travel between stars.
Machine Learning is the Decoder
Decoding such a message, however, would be impossible by eye. It would require advanced machine learning—trained on every known natural and engineered biological pattern—to first recognize the cluster as artificially designed, then reverse-engineer its domain sequence into a payload: an image, a mathematical constant, a map.
This isn't just speculation - it's a new lens for our own research. As we use AI to genetically engineer fungal SM clusters for new drugs and materials, we are learning to write and read this deep language of biology. We are developing the very tools needed to one day ask: Is this fungus from Earth, or is it a message in a bottle, waiting for a reader smart enough to understand it?
We're not just engineering fungi. We're building the translator for a conversation that may have already begun.
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VortexNet Anniversary. What's next?
The first "VortexNet: Neural Computing through Fluid Dynamics" anniversary is coming up. It's impact over the past year has been odd and noticeable. How should we continue this thread? Feedback welcome.
There is no shortage of ideas what to explore next in this context..
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There's a bacteriophage that turns bacteria into “liquid crystals.”
Specifically, Pseudomonas aeruginosa bacteria make Pf phages, which are rod-shaped, negatively-charged, and measure about 2 micrometers in length (roughly the length of an E. coli cell). These phages leave the cells and enter their surroundings. There, they mix with polymers, also secreted by the cells, to form a crystalline matrix.
Surprisingly, this is good for the cells. Although the phages kill some of them, it also makes their biofilms stickier and able to withstand certain antibiotics. These bacteria + phages are prevalent in cystic fibrosis patients; they've formed a sort of symbiotic relationship.
The Pf phages are made from thousands of repeating copies of a coat protein, called CoaB, which wraps around a single-stranded, circular DNA genome. These genes are integrated directly on the bacterial chromosome.
The bacteria “turn on” these phage genes when placed in a viscous environment with low oxygen levels. This is like a trigger to start forming a biofilm. And the cells make a lot of phages; about 100 billion per milliliter.
These liquid crystals form because of a physics principle called “depletion attraction.” If you just mix a bunch of loose or flexible polymers together (such as long carbon chains) they will not form a liquid crystal. But if you mix stiff rods (the phages) with loose polymers at a high enough concentration, the polymers will force the phages close together to create a material that flows like a liquid despite being ordered like a crystal. See the video below.
These liquid crystal biofilms are hard to get rid of. The negatively-charged phages block many antibiotics (like aminoglycosides, which are positively-charged) from entering cells. Liquid crystals also retain water, so these biofilms can survive on drier surfaces.
Paper: Filamentous Bacteriophage Promote Biofilm Assembly and Function
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Toward a Grand Unified Theory of Snowflakes
Ever since the Japanese physicist Ukichiro Nakaya produced his seminal catalog of snowflake shapes 77 years ago, researchers have sought to understand the interplay of factors that lead to different shapes.
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Grief is stored in the fascia and diaphragm, anger in the jaw and hands, fear in the gut, shame in the shoulders. Trillions of cells & bacteria collaborate in a embodied, extended cognition that makes up “you.” Nature evolved intelligence very different from our current machines.
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Presentation: In Resonance with Nature - Toward a New Kind of Machine Learning with Oscillatory Neural Networks
Slides for a Presentation i gave in June 2025 at the Singer Lab at the Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society.
