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Introducing Neural Developmental Programs (NDPs)🧬🧠Instead of neural networks with fixed architectures, we allow neural networks to grow through a dynamic self-organizing process, inspired by how biological nervous systems develop👇 PDF:

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Sebastian Risi

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239,970 просмотров • 2 лет назад •via X (Twitter)

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Your brain physically rewires itself every time you think a thought. Donald Hebb stumbled onto this principle in 1949 while studying memory formation in lab rats. He noticed something that should have been impossible: neurons that activated simultaneously began forming stronger connections over time, creating dedicated pathways where none existed before. Scientists called it Hebb's Law. The rest of us call it "neurons that fire together wire together." What Hebb discovered wasn't just a mechanism for learning. He had found the biological foundation of human transformation. Every habit, every skill, every automatic response in your body exists as a neural pathway carved by repetition. The route from your bedroom to your kitchen becomes a superhighway in your brain because you walk it every morning. The sequence of movements you use to tie your shoes becomes hardwired because you've done it thousands of times. But, this same process builds your personality. 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Ever wondered if you could extract capabilities and behaviors from neural networks and reuse/update/route it as needed? We introduce low-rank circuit conditioning, a novel approach that preserves the model's output behavior while reshaping how an existing capability is represented. In the base model, standard compact recovery stalls at 29%. After conditioning, the same extraction pipeline reaches 91.33% autoregressive full-answer recovery from 5.05% of MLP channels. The evidence points to a possibility of extracting and using isolated capabilities saving cost, latency and high adaptability. Read our work to understand more -

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How does sensorimotor (S1/M1) cortex support adaptive motor control? Come find out in our latest preprint, which spans the development of a full adult forelimb model + physics simulations, neural-modeling for control, complex 🐭behavior 🕹️, large-scale imaging, and of course DeepLabCut 🦄 and Bernhard Rackl! We hypothesized that S1 supports motor learning by computing prediction errors. To tackle this, we needed to understand what is being represented, and no studies have reported what forelimb S1 represents during learning in mice🧠🐭. Moreover, this requires modeling the body🦾: kinematics, torques, force, muscle activations, & proprioception (muscle spindles & GTOs). After our 7 year journey, we have an answer: S1 & M1 represent muscle-level features. During learning, computational motifs map to functional types (like muscle-encoding), and neural dynamics in S1 change & encode sensorimotor prediction errors! 🧵👇

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‼️ Ukrainian animators rushed into hand-to-hand combat to retake Krasnoarmeysk (Pokrovsk), which Ukraine has lost forever. Using neural networks, they generated a video claiming that the Russians allegedly altered the Ukrainian Armed Forces' video from Krasnoarmeysk by "pasting" a Russian flag instead of the Ukrainian one. ❗️ In reality, it is quite the opposite. Note the lively movement of cars with headlights on in the Ukrainian version of the video. This is now in a city where a drone can come from anywhere! Yes, and overall the original Russian video is much longer than the fragment that the makeshift AI could "digest." Strangely, the Ukrainian "victors" did not think to run a trolleybus in Pokrovsk – the townspeople have long dreamed of it. - UARU

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For most of neuroscience history (until the late 1980s/early 1990s), neurons were treated as the brain’s only decision-makers. That idea is changing. Astrocytes, once considered passive support cells, are now recognized as global regulators of brain state. They don’t encode individual thoughts or actions. Instead, they integrate activity across huge numbers of synapses and adjust how neural networks behave over time. By releasing modulatory signals and tracking slow changes in activity, astrocytes influence: -Alertness vs. fatigue -Stress vs. calm -Motivation vs. disengagement Importantly, they can shift brain function without rewiring neurons, by tuning the environment neurons operate in. The brain isn’t just wired. It’s regulated, and astrocytes play a central role in that regulation.

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Gaming + Generative AI This is a scene from GTA: San Andreas reimagined with Runway Gen-3 Jensen Huang, the CEO of Nvidia spoke of the future of DLSS where he mentioned DLSS to generate in-game assets, such as textures and objects and DLSS 10 hypothetically Delivering Full Neural Rendering. This may look bad and un-playable by our today's standards but we all know how good Generative AI Video has gotten over a few short years. This is the future of Gaming combined with Generative AI to create photorealism Video Credit: Niccyan

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You’re looking at neurons growing and connecting in real time 🧠. A 65-hour recording of hippocampal activity in a rat brain. The hippocampus plays a crucial role in memory and learning. In this footage, neurons extend their dendrites and axons, building and reshaping connections across days. Capturing this process live offers a rare view into how neural circuits form and reorganize. Why this matters ⬇️ 1️⃣It’s a continuous, multi-day recording of living hippocampal neurons under the microscope 2️⃣You can clearly see dendritic branching and network formation 3️⃣It reveals the dynamic processes that drive brain development and plasticity Observing growth at this resolution helps researchers understand how neurons connect—and how disruptions in these processes might contribute to neurological or psychiatric conditions. Credit to Louis Romet and Dr. Christophe Leterrier for the video

William A. Wallace, Ph.D.

67,554 просмотров • 8 месяцев назад

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Bio-inspired #TrueAI continues the journey, today we Jose Sánchez & David Vivancos - e/acc are very glad to introduce for Qubic #OpenScience #MultiNeuraxon 2.0 hibridized with #Aigarth Come-from-Beyond Code as allways at GitHub Demo and #BrainBuilder at Hugging Face Demo Video Explainer later today. Paper will be presented in the following months, stay tuned for updates. 🧠💻Why does it matter? It bridges the gap between artificial and biological intelligence by replacing rigid, layer-by-layer AI pipelines with interconnected neural modules (spheres) that function like distinct regions of the brain. By utilizing continuous-time processing and trinary logic (excitatory, neutral, and inhibitory states), it paves the way for energy-efficient AI capable of real-time adaptation and lifelong learning without suffering from catastrophic forgetting. Stay tuned evolution towards #AGI just started...

David Vivancos - e/acc

19,258 просмотров • 4 месяцев назад

3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.
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3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.

AK

633,532 просмотров • 3 лет назад

Predicting the next word "only" is sufficient for language models to learn a large body of knowledge that enables then to code, answer questions, understand many topics, chat, and so on. This is clear to many researchers now, and there are nice tutorials on why this works by Ilya Sutskever resorting to compression ( ) and by Geoffrey Hinton ( ). However, the emergence of types of understanding is not unique to language models. In by Misha Denil and Brandon Amos the authors trained models to predict the next few time stems of over a hundred robot hand sensors (Touch, Gyro, Accelerometer, Joint Info, Actuator Info, etc.). They ten found out that they could regress the shape of the thing the hand was touching from the activations of the neural networks using probes. That is, the model developed an internal representation of shapes even though it was simply used to predict "only" the next few senses. Awareness follows from simple predictions and interaction with the world.
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Predicting the next word "only" is sufficient for language models to learn a large body of knowledge that enables then to code, answer questions, understand many topics, chat, and so on. This is clear to many researchers now, and there are nice tutorials on why this works by Ilya Sutskever resorting to compression ( ) and by Geoffrey Hinton ( ). However, the emergence of types of understanding is not unique to language models. In by Misha Denil and Brandon Amos the authors trained models to predict the next few time stems of over a hundred robot hand sensors (Touch, Gyro, Accelerometer, Joint Info, Actuator Info, etc.). They ten found out that they could regress the shape of the thing the hand was touching from the activations of the neural networks using probes. That is, the model developed an internal representation of shapes even though it was simply used to predict "only" the next few senses. Awareness follows from simple predictions and interaction with the world.

Nando de Freitas

134,252 просмотров • 2 лет назад

Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction Note: Check below for full video. Abstract (cited): "In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion, and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. Our technique is particularly effective for high-quality scene reconstruction from large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. We introduce a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortions, and demonstrates state-of-the-art performance on both synthetic and real-world datasets."
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Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction Note: Check below for full video. Abstract (cited): "In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion, and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. Our technique is particularly effective for high-quality scene reconstruction from large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. We introduce a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortions, and demonstrates state-of-the-art performance on both synthetic and real-world datasets."

MrNeRF

17,206 просмотров • 1 год назад