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Hopfield recall Spiking Neural network now this one is cool isn't it? -open source, link in comment- This is a Spiking Neural Network (SNN) that can learn and recall patterns through Hebbian learning, similar to a Hopfield network but using biologically-inspired spiking dynamics rather than energy-based settling.

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echo.hive

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31,452 görüntüleme • 8 ay önce •via X (Twitter)

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Implemented a visualization for the neural network today. This is a neural network being trained to learn the snake game in a terminal. It's built in Rust using Ratatui More input details in thread
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Implemented a visualization for the neural network today. This is a neural network being trained to learn the snake game in a terminal. It's built in Rust using Ratatui More input details in thread

Bones

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I built a biologically inspired spiking neural network from scratch and it learned with %5 accuracy to do addition :) There is no backpropagation, no artificial loss functions - just spikes, synapses, and dopamine-like reward signals. it uses STDP -> "Spike-Timing-Dependent Plasticity" with modulated rewards This is super fun and I will try to get it to learn with better accuracy. I also need to better understand how all the moving parts fit together Link to source code in comment which has a detailed readme and html with animations explaining how it all works
0:27

I built a biologically inspired spiking neural network from scratch and it learned with %5 accuracy to do addition :) There is no backpropagation, no artificial loss functions - just spikes, synapses, and dopamine-like reward signals. it uses STDP -> "Spike-Timing-Dependent Plasticity" with modulated rewards This is super fun and I will try to get it to learn with better accuracy. I also need to better understand how all the moving parts fit together Link to source code in comment which has a detailed readme and html with animations explaining how it all works

echo.hive

447,978 görüntüleme • 11 ay önce

Your brain doesn't search through memories — it falls downhill on an energy landscape and lands in the nearest valley. This is the Hopfield network from 1982, and the same math now lives inside every transformer: the attention mechanism in modern LLMs is mathematically equivalent to a Hopfield network with exponential storage capacity. The idea: neurons are +1 or -1, connected by symmetric weights. Memories are local minima sculpted by Hebbian learning — neurons that fire together, wire together. Give the network a noisy input, and it descends toward the nearest stored memory one GPU-style majority vote at a time. Vanilla Hopfield nets cap at ~14% of neuron count as usable memories, but the modern extension blows past that. 23 minutes. Bookmark & watch today.
23:14

Your brain doesn't search through memories — it falls downhill on an energy landscape and lands in the nearest valley. This is the Hopfield network from 1982, and the same math now lives inside every transformer: the attention mechanism in modern LLMs is mathematically equivalent to a Hopfield network with exponential storage capacity. The idea: neurons are +1 or -1, connected by symmetric weights. Memories are local minima sculpted by Hebbian learning — neurons that fire together, wire together. Give the network a noisy input, and it descends toward the nearest stored memory one GPU-style majority vote at a time. Vanilla Hopfield nets cap at ~14% of neuron count as usable memories, but the modern extension blows past that. 23 minutes. Bookmark & watch today.

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We built a Spiking Neural Network (SNN) to fully control a physical robot arm (6-DOF), and it's running entirely on a Raspberry Pi (no HAT). In our approach, the SNN starts untrained and drives the servos directly, learning in real time through local learning rules (no backprop or gradient descent). The goal with Twitch-y (the robot + SNN brain) is to let it explore and build an understanding of its own body until it can touch the plate on the mat, while also validating the SNN architecture and some of the math. In the video: - Top right: the SNN architecture - Bottom right: the mini-brain spiking in real time while controlling the arm - Left: Twitch-y (Hugging Face SO-101) If you want to learn more about Artificial Brains, come visit us this Friday (13th), at Founders Inc 👉
0:29

We built a Spiking Neural Network (SNN) to fully control a physical robot arm (6-DOF), and it's running entirely on a Raspberry Pi (no HAT). In our approach, the SNN starts untrained and drives the servos directly, learning in real time through local learning rules (no backprop or gradient descent). The goal with Twitch-y (the robot + SNN brain) is to let it explore and build an understanding of its own body until it can touch the plate on the mat, while also validating the SNN architecture and some of the math. In the video: - Top right: the SNN architecture - Bottom right: the mini-brain spiking in real time while controlling the arm - Left: Twitch-y (Hugging Face SO-101) If you want to learn more about Artificial Brains, come visit us this Friday (13th), at Founders Inc 👉

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Does GPT understand the world? Here is what Ilya Sutskever, co-founder of OpenAI, says during a discussion with Jensen Huang, CEO of Nvidia: (1) When we train a large neural network to accurately predict the next word in lots of different texts from the internet, the AI is learning a world model. (2) On the surface, it may look like learning correlations in text, but it turns out that to 'just learn' statistical correlations in text, to compress information really well, what the neural network learns is some representation of the process that produced the text. (3) This text is a projection of the world...what the neural network is learning is aspects of the world, of people, of the human conditions, their hopes, dreams, motivations, their interactions...the situations we are in. The neural network learns a compressed, abstract, usable representation." Do you think learning representations = understanding? Are large language models simply stochastic parrots, or are they much more?
3:10

Does GPT understand the world? Here is what Ilya Sutskever, co-founder of OpenAI, says during a discussion with Jensen Huang, CEO of Nvidia: (1) When we train a large neural network to accurately predict the next word in lots of different texts from the internet, the AI is learning a world model. (2) On the surface, it may look like learning correlations in text, but it turns out that to 'just learn' statistical correlations in text, to compress information really well, what the neural network learns is some representation of the process that produced the text. (3) This text is a projection of the world...what the neural network is learning is aspects of the world, of people, of the human conditions, their hopes, dreams, motivations, their interactions...the situations we are in. The neural network learns a compressed, abstract, usable representation." Do you think learning representations = understanding? Are large language models simply stochastic parrots, or are they much more?

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Canton Network developer documentation is now consolidated into a single entry point on One place to explore, build, test, and deploy, with role-based learning paths, open source contributions, and built-in AI tooling. Learn:
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Canton Network developer documentation is now consolidated into a single entry point on One place to explore, build, test, and deploy, with role-based learning paths, open source contributions, and built-in AI tooling. Learn:

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A Neural Network Can Grow New Neurons Where It Is Confused? In 1994, Bernd Fritzke published A Growing Neural Gas Network Learns Topologies. He introduced a network that starts small, follows incoming data, and inserts new neurons where its error is highest. In the animation, the fog is the drifting data. The glowing nodes are neurons. The fibers are learned connections. The network grows into a living skeleton of the manifold.

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I vibecoded this neural network visualization for my students and open sourced it. It shows a simple MLP trained on MNIST handwritten digits at several training steps. The visualization is using Three.js and it comes with training code in PyTorch . Link + repo 👇
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I vibecoded this neural network visualization for my students and open sourced it. It shows a simple MLP trained on MNIST handwritten digits at several training steps. The visualization is using Three.js and it comes with training code in PyTorch . Link + repo 👇

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Watch Spot crouch, jump, climb boxes and leap across gaps, controlled by a neural network trained with reinforcement learning (RL) and multi-expert distillation. Multiple expert policies were trained and distilled together into a single policy that was fine tuned to improve performance over diverse terrains. This work was inspired by ANYmal’s parkour capabilities. The neural network processes depth data from Spot's sensors to construct an understanding of the environment.

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Motion planning in complex tasks is hard and still done via slow, explicit, traditional planners. We present a generalist Neural Motion Planner -- a single neural network that plans complex dynamic motions quickly and accurately at test time. Building upon our lab's sim2real efforts, the key idea is to create many complex scenes in simulation and then distill classical motion planner trajectories into a single reactive neural network policy. More details in the thread below! 👇 Open-sourced:

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📁 Terence Tao, Fields Medalist mathematician, explains that neural networks do not understand concepts, but they uncover hidden patterns. In knot theory, a classical neural network discovered correlations between mathematical invariants no one had anticipated. It started as a black box, then became a real clue to deep structures humans had missed.

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Lockdown stocks are spiking. Oil infrastructure is burning in the UAE. Iran claims it hit a US warship. Daniel Davis Deep Dive says this energy crisis will be bigger than 1973, and something big is coming. He can feel it too.
2:58

Lockdown stocks are spiking. Oil infrastructure is burning in the UAE. Iran claims it hit a US warship. Daniel Davis Deep Dive says this energy crisis will be bigger than 1973, and something big is coming. He can feel it too.

Redacted

10,558 görüntüleme • 4 ay önce

A neural network can begin as a flat sheet and learn the shape of hidden data A self-organizing map turns learning into geometry. Each data point pulls one winning neuron toward it, but nearby neurons move too, and so the whole lattice bends without losing its neighborhood structure. The strange part is that the network is not given the roll shape. It discovers the shape through competition and local cooperation. Paper: Self-Organized Formation of Topologically Correct Feature Maps Authors: Teuvo Kohonen Year: 1982
0:30

A neural network can begin as a flat sheet and learn the shape of hidden data A self-organizing map turns learning into geometry. Each data point pulls one winning neuron toward it, but nearby neurons move too, and so the whole lattice bends without losing its neighborhood structure. The strange part is that the network is not given the roll shape. It discovers the shape through competition and local cooperation. Paper: Self-Organized Formation of Topologically Correct Feature Maps Authors: Teuvo Kohonen Year: 1982

Mathelirium

130,244 görüntüleme • 4 ay önce

Spiking Neural Network from scratch achieves 8% accuracy. no backpropagation or SGD I created a genetic hyper parameter optimizer and it now, on average, can get 8% accuracy which is ~3% above chance Link to source code with a detailed video and markdown explanations in comment it also usually starts lower than 5% and slowly improves then eventually can start dipping below 5%, all of which leads me to believe there is glimmers of learning taking place. sometims it is stabel around 7-8-9% for a long time there is no backpropagation or SGD. it learns via STDP (spike timing dependent plasticity) and a reward mechanism each example is presented n many times (500 in this case) which produces spike train which leads to eligibility list, at the end of a turn based on if the answer was correct or not we adjust the weights using the reward as a multiplier spike timing keeps track of the sequence of neuron firings and which ones were more likely to have lead to the correct answer let me know what you think
0:43

Spiking Neural Network from scratch achieves 8% accuracy. no backpropagation or SGD I created a genetic hyper parameter optimizer and it now, on average, can get 8% accuracy which is ~3% above chance Link to source code with a detailed video and markdown explanations in comment it also usually starts lower than 5% and slowly improves then eventually can start dipping below 5%, all of which leads me to believe there is glimmers of learning taking place. sometims it is stabel around 7-8-9% for a long time there is no backpropagation or SGD. it learns via STDP (spike timing dependent plasticity) and a reward mechanism each example is presented n many times (500 in this case) which produces spike train which leads to eligibility list, at the end of a turn based on if the answer was correct or not we adjust the weights using the reward as a multiplier spike timing keeps track of the sequence of neuron firings and which ones were more likely to have lead to the correct answer let me know what you think

echo.hive

225,484 görüntüleme • 11 ay önce

Training a Whole-Body Control Foundation Model -- new work from my team at Agility A neural network for controlling our humanoid robots which is robust to disturbances, can handle heavy objects, and is a powerful platform for learning new whole-body skills learn more in our blog post ->
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Training a Whole-Body Control Foundation Model -- new work from my team at Agility A neural network for controlling our humanoid robots which is robust to disturbances, can handle heavy objects, and is a powerful platform for learning new whole-body skills learn more in our blog post ->

Chris Paxton

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Ever wondered what’s happening inside an AI’s “brain”? This is a neural network in action, taking raw data and transforming it into decisions through thousands of layered calculations. Each layer extracts patterns, refines signals, and passes smarter information forward, slowly turning noise into meaning. It’s not thinking like a human but it is learning, adapting, and improving with every pass. What looks like chaos is actually pure math shaping intelligence in real time
0:35

Ever wondered what’s happening inside an AI’s “brain”? This is a neural network in action, taking raw data and transforming it into decisions through thousands of layered calculations. Each layer extracts patterns, refines signals, and passes smarter information forward, slowly turning noise into meaning. It’s not thinking like a human but it is learning, adapting, and improving with every pass. What looks like chaos is actually pure math shaping intelligence in real time

Kekius Maximus

37,481 görüntüleme • 8 ay önce

Using light as a neural network, as this viral video depicts, is actually closer than you think. In 5-10yrs, we could have matrix multiplications in constant time O(1) with 95% less energy. This is the next era of Moore's Law. Let's talk about Silicon Photonics... 1/9
0:27

Using light as a neural network, as this viral video depicts, is actually closer than you think. In 5-10yrs, we could have matrix multiplications in constant time O(1) with 95% less energy. This is the next era of Moore's Law. Let's talk about Silicon Photonics... 1/9

Deedy

725,807 görüntüleme • 1 yıl önce