tag > OSC
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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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Hologram News: Holograms Influence Brain & Universe as hologram
Researchers use Ultrasound Holograms to Influence Brain Networks
For the first time, a new ultrasound technique allows researchers to stimulate multiple locations in the brain simultaneously. This opens up new possibilities for treating devastating brain diseases such as Alzheimer’s, Parkinson’s and depression in the future.
Information and Quantum Physics: The Universe as a hologram
The exploration of quantum information challenges objective reality, positing the universe as a hologram. This piece examines how informational algorithms drive everything from the emergence of physical laws and time to the ultimate nature of consciousness as an emergent property
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Why the Brain isn't a Computer—It’s a Wave Interference Engine
Most people think the brain "calculates" anomalies like a digital processor. They’re wrong. Digital is too slow. If you look at a 20x20 matrix and spot the "odd" numbers instantly, you aren't running an algorithm. You are performing Analog Subtraction.
The Theory
The brain doesn't process data; it manages Waves.
- The Prediction: Your internal model generates a "Counter-Wave" (Anti-Phase) based on expected patterns.
- The Reality: Sensory input hits as an incoming wave.
- The Interaction: When they meet, Destructive Interference occurs.
The Result
The predictable world—the "normal" numbers—simply cancels out into silence. No CPU cycles needed. No "processing" required. The "Oddness" (the anomaly) is the only thing that doesn't cancel. It survives the interference as a high-energy spike. Consciousness isn't the whole picture; it’s the "Residue" of the subtraction.
We don't "think" the difference. We feel the interference where the world fails to match our internal wave. Mathematics calls this a Fourier Transform. Nature calls it Perception. Memory must be wave-like: sensory inputs are converted into waves whose resonance generates meaning from reality.
Source: Cankay Koryak
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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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Zeroing In on Zero-Point Motion Inside a Crystal
A nanocrystal cooled to near absolute zero produces an unexpected light emission, which is shown to arise from quantum fluctuations in the crystal’s atomic lattice.
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The brain doesn’t “store data”; it maintains resonant attractors that compress meaning into dynamics. Each oscillatory pattern is a lossy, context-dependent summary of prior experience that can be expanded (decoded) when needed. In other words: The brain’s oscillations are not just rhythms, they are compressive, generative codecs of reality.
