Neuralink's brain data collection and analysis for neurological disorders

【From U-Net to Swin-Unet Transformers: The Next-Generation Advances in Brain Tumor Segmentation with Deep Learning】 Full article: https://lnkd.in/gxurGNGB (Authored by Mushtaq Mahyoob Saleh and Bharat B. Biswal, from University of Electronic Science and Technology of China, China.) #Brain_tumor_segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, advancements in machine learning (ML) and #deep_learning (DL) have revolutionized segmentation accuracy. This review paper comprehensively surveys the evolution of brain tumor segmentation techniques, emphasizing the transition from conventional U-Net models to cutting-edge Swin UNET transformer architectures, discusses the impact of novel activation functions on improving gradient stability and segmentation accuracy, addresses ongoing challenges such as data heterogeneity, real-time clinical applicability, and integration barriers, and proposes future directions for developing robust, interpretable, and scalable brain tumor segmentation systems. #Vision_Transformers  Additionally, if you have any new manuscripts ready for submission, please feel free to send them directly as an attachment to the email address below. As a valued contributor, you will enjoy priority access to discounted publication fees and regular updates on the progress of your manuscript throughout the review process. Email: Hellen Wang <kelseytan.scirp@gmail.com> Hellen Wang <wqs0823@gmail.com>

Detecting causality in neural spike trains using a new technique Understanding the brain's functional architecture is a fundamental challenge in neuroscience. The connections between neurons ultimately dictate how information is processed, transmitted, stored, and retrieved, thus forming the basis of our cognitive functions. via News Medical Device / Technology News Feed

Scientists discovered that the heart has over 40,000 neurons, creating its own neural network that communicates directly with the brain. In fact, around 80% of vagus nerve signals go from the heart to the brain, influencing thoughts, emotions, and decisions. This field, known as neurocardiology, shows the heart is more than just a pump—it’s an intelligent organ shaping human choices for millions of years. Source: https://lnkd.in/dwvqZeSj

Cracking the Wave–Particle Code of the Brain For over 100 years, neuroscience has been constrained by the “spike model”—reducing action potentials to digital blips. The ECEPJ Model redefines this: neurons are not switches. They are capacitor lattices, storing and releasing energy across 3D dielectric layers of myelin. • Waves = field patterns, analog, infinite, orchestrating thought like harmonics. • Particles = neurotransmitters, discrete messengers that translate the waves. • Code = the infinite combinations where energy configurations map to memory, emotion, and awareness. This is where wave–particle duality and quantum coherence converge. Why this matters: • PBM (light) restores mitochondrial energy → stabilizes field codes. • TMS (magnetic fields) modulates synchrony → strengthens coherence. • TUM (ultrasound) penetrates deep tissue → fine-tunes oscillations. Together, they demonstrate: the brain is an energy-driven code system, not a wiring diagram. The cure for neurological disease will not be found in plaques or tangles. It will be found in decoding and restoring the energy codes. The ECEPJ framework is the path toward a neural codebook—a map not only for treating Alzheimer’s and Parkinson’s, but for building AI that learns like the brain itself.

This raises an important question: can isometric training help rewire similar brain areas? While no study has tested this directly in surfers, evidence shows that sustained isometric exertion reorganizes brain networks for greater efficiency. EEG studies demonstrate that higher levels of isometric force increase clustering, modularity, and global efficiency of brain activity—meaning the brain communicates more effectively under load. That makes it plausible that training with Isophit could cultivate the same type of neural precision big wave surfers display when managing threat at the highest level. Continue Reading: https://lnkd.in/gkHdf_nw

One Neuron → 10,000 Synapses ⚡🧠 The ECEPJ Model Reveals the Brain’s Hidden Energy Code Your brain isn’t wired like a computer. It’s an energy lattice — billions of neurons acting as biological capacitors, storing and synchronizing energy into field-driven codes. In the ECEPJ model, a single neuron reaches 10,000+ synapses by aligning energy fields in 3D space, not by firing endless spikes. 🔹 Series Capacitors (X-axis): Energy flows node-to-node at lightning speed. 🔹 Parallel Capacitors (Y-axis): Neighboring neurons resonate, synchronizing instantly. 🔹 Layered Fields (Z-axis): Cortical columns lock together vertically, uniting deep and surface layers. This creates field-driven energy codes — the brain’s true language of memory, attention, and thought. ⸻ Why It Matters When this system breaks — as in Alzheimer’s disease — • Myelin fails → energy leaks • Timing collapses → memory codes dissolve The ECEPJ approach fights back: ✨ PBM → Recharges the neuron’s capacitors ✨ 40 Hz Gamma Entrainment → Restores synchronization ✨ NeuroGuardian → Monitors and protects field coherence ⸻ This is more than neuroscience. It’s a new path for AI, Alzheimer’s therapy, and the future of intelligence itself. 📖 Dive deeper in the Energy Brain Series: ECEPJ Model: Capacitor-Based Neuron — A New Theory for Medicine, AI, and Beyond 📌 Get the book on Amazon #ECEPJ #EnergyBrainSeries #NeuroGuardian #NeuroByte #BrainEnergy #AI #AlzheimersResearch #NeuroscienceInnovation

【From U-Net to Swin-Unet Transformers: The Next-Generation Advances in Brain Tumor Segmentation with Deep Learning】 Full article: https://lnkd.in/g3nusjmZ (Authored by Mushtaq Mahyoob Saleh and Bharat B. Biswal, from University of Electronic Science and Technology of China, China.) #Brain_tumor_segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, advancements in machine learning (ML) and #deep_learning (DL) have revolutionized segmentation accuracy. This review paper comprehensively surveys the evolution of brain tumor segmentation techniques, emphasizing the transition from conventional U-Net models to cutting-edge Swin UNET transformer architectures, discusses the impact of novel activation functions on improving gradient stability and segmentation accuracy, addresses ongoing challenges such as data heterogeneity, real-time clinical applicability, and integration barriers, and proposes future directions for developing robust, interpretable, and scalable brain tumor segmentation systems. #Vision_Transformers 

🧠 𝗠𝗮𝗿𝗸𝗼𝘃 𝗕𝗹𝗮𝗻𝗸𝗲𝘁𝘀 𝗮𝗻𝗱 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗗𝘆𝘀𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝗺𝗧𝗕𝗜: Insights from Simulation Models 𝗠𝗶𝗹𝗱 𝘁𝗿𝗮𝘂𝗺𝗮𝘁𝗶𝗰 𝗯𝗿𝗮𝗶𝗻 𝗶𝗻𝗷𝘂𝗿𝘆 (𝗺𝗧𝗕𝗜) affects millions annually. Though often considered “transient,” many patients suffer persistent cognitive dysfunction—deficits in attention, memory, executive control, and emotional regulation—revealing the inadequacy of current diagnostic frameworks. 🔬 The article introduces the 𝗠𝗮𝗿𝗸𝗼𝘃 𝗯𝗹𝗮𝗻𝗸𝗲𝘁 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸, conceptualizing the fragile boundary between internal neural states and external inputs. Yet, mTBI pathophysiology—ionic flux, glutamate excitotoxicity, calcium accumulation, mitochondrial failure, axonal shearing, and neuroinflammation—systematically dismantles this balance, collapsing conditional independence and leaving cognition profoundly unstable. 📉 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 (stochastic differential equations) reveal how noise and weakened neural coupling generate instability. fMRI evidence supports this: Posterior network disruptions → memory impairment Frontal hyperconnectivity → depression & fatigue These findings converge on a sobering insight: mTBI destabilizes the brain’s predictive architecture, with ripple effects across cognition, emotion, and behavior. ⚠️ Within 𝗮𝗰𝘁𝗶𝘃𝗲 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 and 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 frameworks, mTBI represents a failure of free energy minimization. The breakdown of Markov blankets prevents accurate model updating, creating maladaptive compensatory dynamics. Persistent post-concussive symptoms emerge not as incidental, but as the system’s futile attempt to regain equilibrium. 🧩 Extending the metaphor to memory systems, the study shows how short-term and long-term memory both deteriorate under disrupted Markov blankets. Impaired synaptic plasticity compounds this decline, further eroding adaptability. 👉 𝗧𝗵𝗲 𝗰𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻: even “mild” brain injuries precipitate enduring dysfunction by disrupting the brain’s most fundamental organizational principle. Recovery is not linear nor guaranteed—instead shadowed by persistent vulnerability rooted in the architecture of neural computation.

Scientists discovered that the heart has over 40,000 neurons, creating its own neural network that communicates directly with the brain. In fact, around 80% of vagus nerve signals go from the heart to the brain, influencing thoughts, emotions, and decisions. This field, known as neurocardiology, shows the heart is more than just a pump—it’s an intelligent organ shaping human choices for millions of years.