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#neuromorphic — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #neuromorphic, aggregated by home.social.

  1. RE: mathstodon.xyz/@gconstantinide

    We're hiring for a project on #SpikingNeuralNetworks and #neuromorphic computing, to start in October this year, for 36 months. Can hire at pre- or post-PhD level. Feel free to email me informally, or apply at the link below. Please do share with your networks if you know someone who would be interested.
    #ComputationalNeuroscience

  2. 9/
    The Constraints: Physics, chemistry, energy consumption.

    The Emergent Architecture: The Octopus.

    The paper is essentially trying to turn the "Jazz Band" chaos of evolution into a repeatable engineering formula. It’s "old wine in a new bottle," but the bottle is now high-performance computing.

    youtu.be/ucQnsxjOTDA

    #AI
    #neuromorphic
    #DistributiveIntelligence
    #evolution
    #octopus

  3. 8/
    3. The "Neuromorphic" Hardware Angle

    The "new" part is often the Hardware (Neuromorphic). We are finally building chips that can actually handle these distributed rules without a central "Boss" CPU bottlenecking everything.

    The "Mind Club" Connection
    If you feel like you’ve heard this before, it’s because you have—it’s the story of Evolution.

    #AI
    #neuromorphic
    #DistributiveIntelligence
    #evolution

  4. 7/
    2. Declarative vs. Procedural

    Most AI today is "Procedural"—it’s fed millions of examples to learn a procedure. A "Declarative Constraint" system is different. It’s like telling a robot: "You are not allowed to touch the walls," and letting it figure out navigation, rather than showing it 10,000 videos of robots not touching walls.

    #AI
    #neuromorphic
    #DistributiveIntelligence

  5. 6/
    1. From "Bio-Inspiration" to "Mechanical Necessity"

    Usually, AI researchers try to mimic the brain (Neural Networks). Kinney is suggesting we stop trying to copy the brain's look and instead copy its limitations. He’s arguing that if you set the right "Universal Constraints," the system is forced to develop a brain-like structure because that's the only mathematically efficient way to solve the problem.

    #AI
    #neuromorphic
    #DistributiveIntelligence

  6. 5/
    The core idea of emergence from rules is nothing new. It’s what John Conway was doing with the Game of Life in 1970, and it’s how Stephen Wolfram has viewed the universe for decades.

    What makes this specific paper by Kinney "new" (or at least a fresh take) isn't the concept of emergence itself, but the application:

    #AI
    #neuromorphic
    #DistributiveIntelligence

  7. 4/

    The Big Idea
    Stephen Kinney is essentially arguing that we can create "Artificial Life" (or at least more flexible AI) by focusing on Limitations (Constraints) rather than Commands.

    #AI
    #neuromorphic
    #DistributiveIntelligence

  8. #Neuromorphic explained:

    In plain language, this paper is describing a way to build a digital "brain" (a neuromorphic architecture) not by programming every step, but by simply giving the system a set of Universal Rules (constraints) to follow.

    Think of it like this:

    The "Lego" Analogy
    Traditional AI: You give the computer a massive instruction manual on how to build a specific castle, brick by brick. If you want a tower, you have to code "tower."

    #AI
    #DistributedIntelligence
    #DigitalBrain

  9. Ok. I think the last widget I'll be building for my Bio-Inspired #AI and Optimization course this semester.

    Spiking Neural Network ( #SNN ) explorer

    LIF Neuron model, Rate coding, STDP, Survey of HW&SW implementations, #Neuromorphic applications & learning
    tpavlic.github.io/asu-bioinspi

  10. Ok. I think the last widget I'll be building for my Bio-Inspired #AI and Optimization course this semester.

    Spiking Neural Network ( #SNN ) explorer

    LIF Neuron model, Rate coding, STDP, Survey of HW&SW implementations, #Neuromorphic applications & learning
    tpavlic.github.io/asu-bioinspi

  11. Ok. I think the last widget I'll be building for my Bio-Inspired #AI and Optimization course this semester.

    Spiking Neural Network ( #SNN ) explorer

    LIF Neuron model, Rate coding, STDP, Survey of HW&SW implementations, #Neuromorphic applications & learning
    tpavlic.github.io/asu-bioinspi

  12. Ok. I think the last widget I'll be building for my Bio-Inspired and Optimization course this semester.

    Spiking Neural Network ( ) explorer

    LIF Neuron model, Rate coding, STDP, Survey of HW&SW implementations, applications & learning
    tpavlic.github.io/asu-bioinspi

  13. Ok. I think the last widget I'll be building for my Bio-Inspired #AI and Optimization course this semester.

    Spiking Neural Network ( #SNN ) explorer

    LIF Neuron model, Rate coding, STDP, Survey of HW&SW implementations, #Neuromorphic applications & learning
    tpavlic.github.io/asu-bioinspi

  14. Together with Clara Wanjura, we are delighted to announce the second instalment of our workshop on neuromorphic computing taking place 10-12 June at the @MPI_ScienceOfLight Max Planck Institute for the Science of Light in Erlangen, Germany.

    👉 Registration and abstract submission will be open until 12 April.

    For further information, please visit the event website: indico.mpl.mpg.de/event/27/

    #artificialintelligence #neuromorphic #workshop

  15. Between commodification, edge computing, and (eventually) #neuromorphic approaches (instead of oodles of multiply-accumulate #hardware), some of the #AI spend is going to look rather foolish eventually
    asymco.com/2026/03/10/the-most

  16. Decima-8: Нейроморфная архитектура, оперирующая уровнями энергии

    Современные нейроморфные системы сталкиваются с двумя независимыми проблемами. Проблема 1: Кодирование информации Бинарные спайковые сети (SNN) передают градации сигнала через: Частотное кодирование (множество тактов на одно значение) Увеличение количества линий передачи Проблема 2: Аппаратная реализация Аналоговые мемристорные кроссбары обещают естественную нейроморфность, но содержат следующие проблемы: Шум и дрейф параметров Недетерминизм вычислений Каждый чип требует индивидуальной калибровки Традиционные Network-on-Chip (NoC) добавляют overhead: ~40% площади кристалла уходит на маршрутизаторы ~70% энергии тратится на пересылку данных, а не вычисления Decima-8 предлагает: Level16: кодирование уровня активации (0..15) в одном такте на одной линии. Это компромисс между бинарным представлением и аналоговой непрерывностью. Цифровые кроссбары (эмуляция мемристорных матриц): детерминизм, воспроизводимость, отсутствие шума Эстафетную активацию вместо пакетной маршрутизации: тайлы не передают данные друг другу, активация распространяется через граф зависимостей Результат: фиксированная задержка, предсказуемое поведение, 0% площади на роутеры.

    habr.com/ru/articles/1005762/

    #neuromorphic #realtime

  17. Neuromorphic computing has proved to be good at complex mathematics, against expectations. New #neuromorphic hardware from #Intel can solve differential equations using the finite element method. spectrum.ieee.org/neuromorphic...

  18. Neuromorphic computing offers a different path: brain inspired, event driven chips that keep compute and memory close and only react when something actually happens.

    - Spiking neural networks and event driven processing
    - Why neuromorphic systems can dramatically boost performance per watt
    - 2026 use cases: robotics, edge AI, healthcare, cybersecurity, and neuroscience research

    Read the full piece: techglimmer.io/neuromorphic-co

    #Neuromorphic #AI #EdgeAI #Robotics #FOSS #TechGlimmer

  19. # 🌟 **φ⁴³ AQARION-BUNDLE TikTok Presentation** 🎬 (3773 chars)

    ```
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    94.1% accuracy 📈 0.9ms latency ⚡
    $85/MO vs $900K/YR → $450K SAVINGS! 💰

    ONE COMMAND:
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    73-NODE HYPERGRAPH DASHBOARD 🖥️
    Three.js LIVE φ-heatmap! ✨
    Green nodes = φ=1.9102 locked ✅
    Edge glow = 0.9ms latency 🌟
    #aquarius #neuromorphic #hypergraph

  20. #Neuromorphic computing can now solve partial differential equations - a non-obvious, but exciting use case beyond #neuralnetworks

    phys.org/news/2026-01-nature-g

  21. Neuromorphic Artificial Skin Mimics Human Touch for Efficient Robots Researchers have developed neuromorphic artificial skin for robots, mimicking the human nervous system with efficient spike-base...

    #RobotRevolutionPro #energy-efficient #sen #humanoid #robot #technology #neuromorphic #artificial #skin #robotics #breakthrough

    Origin | Interest | Match
  22. Yet another commentary (and rant) on why following the original #neuromorphic approach, using *also* #analog circuits to emulate the physics of cortical circuits, can be fascinating:
    pnas.org/doi/10.1073/pnas.2525

  23. @giacomoi @albertcardona @RuthMalan

    I am hoping to see lots more practical developments along the lines of: This physical phenomenon does something (by virtue of being itself) that is isomorphic to some computation. How do we couple it to the environment so that the naturally occurring computation corresponds to something we care about?

    To be honest, I am a little surprised that there haven't been more attempts at modern analog computers. This paper by Jaeger et al gives some clue as to the potential for extracting computational services from physic: arxiv.org/abs/2307.15408

    I suspect that one contributor to the lack of progress on modern analog computing is the difficulty of mapping from the user's problem of interest to the computation offered by the physical substrate. In the AOC case you need to map the user's problem to a fix-point problem (because that's all the hardware can solve). This paper by Kleyko et al deals with that issue by proposing that Vector Symbolic Architectures could be used as an intermediate abstraction between the user's problem and a wide range of nonstandard computational hardware: arxiv.org/abs/2106.05268

    #neuromorphic #AnalogComputing #VectorSymbolicArchitectures #VSA #HyperdimensionalComputing #HDC

  24. #Spiking #neural #networks (#SNN) running on continuous-time, noisy, and highly variable computing substrates can learn reliably with #ReinforcementLearning ... Not only in real brains, but also in mixed-signal #neuromorphic hardware! 😇

    Neuromorphic dreaming as a pathway to efficient learning in artificial agents

    doi.org/10.1088/2634-4386/ae0a

  25. One more step toward EEG based epileptic seizure detection using #neuromorphic #spiking neural network chips! rdcu.be/ek2SC Jim Bartels, Olympia Gallou, Hiroyuki Ito, Matthew Cook, Johannes Sarnthein, Giacomo Indiveri & Saptarshi Ghosh