GigaIO的动态

Breaking New Ground in Neuroscience with FlyWire AI ???? FlyWire AI has achieved a feat in mapping the fruit fly brain, at the level of individual cells, using AI. This high-resolution 3D map with neural circuits and pathways is now allowing researchers to explore connectivity inside the brain in ways previously unimaginable. By studying this model, we gain insights into very substantial transformations in our understanding of cognition and pathways to advancement both in AI and in the research of the human brain. This work of FlyWire AI constitutes a quantum leap in neural mapping and neuroscience! #Neuroscience #FlyWireAI #ArtificialIntelligence #NeuralMapping #Research https://flywire.ai

Groundbreaking research from the Integrative Cognition and Neuroscience Lab and the National Institute of Neurological Disorders and Stroke (NINDS) reveals a fascinating new dimension of how our brains encode information. Using Blackrock Neurotech's Utah Array, Neuroport System, and CerePlex SI technology, researchers discovered that neurons communicate not just through firing rates, but also through precise sequences of activation within population bursts. Published in Nature Portfolio, this landmark study shows how the temporal ordering of neural activity in the human anterior temporal lobe carries unique information about both broad categories and specific examples of visual stimuli. This sequential code works alongside traditional rate-based encoding to enhance the brain's information processing capabilities. The findings suggest that the brain may use this efficient coding strategy to represent complex information using relatively sparse neural activity. The research team found that these neuronal sequences represent a distinct coding mechanism rather than just a byproduct of response timing. Their innovative approach to analyzing neural data revealed patterns that had never been observed before in human cortex. As we continue to advance our neural recording capabilities, these insights could have profound implications for our understanding of human cognition and the development of brain-computer interfaces. Congratulations to Weizhen Xie, John H. Wittig Jr, Julio Chapeton, Mostafa El-Kalliny, Samantha Jackson, Sara Inati, and Kareem A. Zaghloul on this pivotal discovery that advances our understanding of neural information processing! Read the full paper: https://lnkd.in/ei4CgF-s #Neuroscience #Innovation #Neurotechnology #BCI

Researchers have developed a device that can simultaneously measure six markers of brain health. The sensor can pull off this feat thanks to an artificial intelligence (AI) system that pieces apart the six signals in real time.

Pushing the frontiers of data-intensive neuroscience: New research from Harvard University and Google Research unveils the largest and most intricate reconstruction of human brain tissue to date. Involving over 1.4 million gigabytes of imaging data, this is nothing short miraculous. Learn more in our latest article! ?? https://lnkd.in/eve5J9K3 Sign up to hear our upcoming interview with the neuroscientist Jeff Lichtman, one of the great minds behind this incredible project. In our interview he delves into the collaboration with google, talks about this work, what is to come, and even whether he thinks its possible to upload a human brain to a computer... Link in comments! #Connectome #AIInMedicine #AIInnovation #AIResearch #Neuroscience

I remember during my PhD, we thought a lot about this intriguing question in our lab: Is there unique information encoded in the sequence of neuronal firing, beyond what is captured by their firing rates? At first glance, it seems straightforward. But when you dive deeper, you quickly realize the complexities and confounds, making it very challenging to design an experiment that provides a definitive answer. A recent study from the NIH tackles this very question, dissecting the problem and presenting evidence that neural sequences indeed carry unique information. So, how does this relate to Brain Computer Interfaces? In BCI papers published in recent years, LSTMs (long short-term memory networks) have been shown to often outperform other machine learning models that ignore temporal sequences. This study offers a possible explanation for why that might be—highlighting the critical role of temporal information in decoding neural activity. Exciting insights like these deepen our understanding of both neuroscience and BCI development. What are your thoughts?

Groundbreaking Signal Detected in Human Brains for the First Time Scientists have identified a never-before-seen signal in human brains, marking a significant milestone in neuroscience. This discovery sheds light on previously unrecognized neural activities, which could revolutionize our understanding of brain function. The detected signal involves unique patterns of neural communication, potentially linked to higher cognitive processes or subconscious brain functions. Researchers speculate that it could play a crucial role in memory formation, decision-making, or emotional regulation. Advanced imaging techniques and electrophysiological tools enabled the detection of this signal, highlighting the growing sophistication of brain research. Understanding its origin and implications could pave the way for innovative approaches to treating neurological disorders, enhancing brain-machine interfaces, and unlocking the mysteries of human consciousness. This finding underscores the complexity of the human brain and the untapped potential for further discoveries in neuroscience. https://lnkd.in/dnGV6CNA

Scientists have created an incredibly detailed digital map of a tiny piece of human brain tissue, about half the size of a grain of rice. This cubic millimeter of brain contains an astonishing 1.4 million gigabytes of data, including approximately 57,000 cells, 230 millimeters of blood vessels, and 150 million synapses. The tissue sample, sourced from the left anterior temporal lobe of an epilepsy patient during surgery, was meticulously imaged using an electron microscope over 11 months. AI algorithms then reconstructed the cells and their connections in 3D. Key findings include: 1, Neurons connected by over 50 synapses, potentially representing well-practiced behaviors requiring minimal conscious effort. 2, Axons forming knots, a previously unseen phenomenon. Led by Jeff Lichtman from Harvard University and Viren Jain from Google, the team has made the entire dataset freely available online, complete with tools for analysis and proofreading. This resource is poised to offer unparalleled insights into the human brain's complexity and will be invaluable for neuroscientists worldwide. This groundbreaking research underscores the incredible potential of AI in processing and analyzing vast amounts of complex biological data. The use of AI algorithms in reconstructing the brain's intricate neural connections highlights its crucial role in advancing neuroscience. This technology not only aids in medical research but also paves the way for future innovations in AI-driven diagnostics and brain-machine interfaces. As we continue to explore the depths of neural structures, AI will be instrumental in unlocking new frontiers in brain science and beyond. Follow 15minAi for more AI topics! #Neuroscience #BrainMapping #ArtificialIntelligence #MedicalInnovation #AI #NeuralNetworks #Research #HealthcareInnovation

??Scientific paper: Improvement in Alzheimer's Disease MRI Images Analysis by Convolutional Neural Networks Via Topological Optimization Abstract: This research underscores the efficacy of Fourier topological optimization in refining MRI imagery, thereby bolstering the classification precision of Alzheimer's Disease through convolutional neural networks. Recognizing that MRI scans are indispensable for neurological assessments, but frequently grapple with issues like blurriness and contrast irregularities, the deployment of Fourier topological optimization offered enhanced delineation of brain structures, ameliorated noise, and superior contrast. The applied techniques prioritized boundary enhancement, contrast and brightness adjustments, and overall image lucidity. Employing CNN architectures VGG16, ResNet50, InceptionV3, and Xception, the post-optimization analysis revealed a marked elevation in performance. Conclusively, the amalgamation of Fourier topological optimization with CNNs delineates a promising trajectory for the nuanced classification of Alzheimer's Disease, portending a transformative impact on its diagnostic paradigms. Continued on ES/IODE ?? https://etcse.fr/tJci ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you. #alzheimer #science #health

??Scientific paper: Improvement in Alzheimer's Disease MRI Images Analysis by Convolutional Neural Networks Via Topological Optimization Abstract: This research underscores the efficacy of Fourier topological optimization in refining MRI imagery, thereby bolstering the classification precision of Alzheimer's Disease through convolutional neural networks. Recognizing that MRI scans are indispensable for neurological assessments, but frequently grapple with issues like blurriness and contrast irregularities, the deployment of Fourier topological optimization offered enhanced delineation of brain structures, ameliorated noise, and superior contrast. The applied techniques prioritized boundary enhancement, contrast and brightness adjustments, and overall image lucidity. Employing CNN architectures VGG16, ResNet50, InceptionV3, and Xception, the post-optimization analysis revealed a marked elevation in performance. Conclusively, the amalgamation of Fourier topological optimization with CNNs delineates a promising trajectory for the nuanced classification of Alzheimer's Disease, portending a transformative impact on its diagnostic paradigms. Continued on ES/IODE ?? https://etcse.fr/tJci ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you. #alzheimer #science #health

Precision Neuroscience, co-founded by neurosurgeon Benjamin Rapoport and CEO Michael Mager, has secured $102 million in Series C funding to advance its minimally invasive brain-computer interface technology. The company’s Layer 7 Cortical Interface aims to help patients control digital devices with their thoughts—without penetrating the brain—setting it apart from Neuralink, co-founded by Rapoport and Elon Musk. As the competition intensifies, Precision’s physician-led approach focuses on safety, scalability, and solving real-world medical challenges today. Read the complete article here: https://lnkd.in/gNZsYyDq