Dr Taylor on Brain Decoding and Machine Learning

IV. Neuroscience and Perception (1900–2000) 8. Quote (1932) “The body has an internal sense—interoception—that informs the brain of its state.” — Charles Sherrington Reference: Studies on proprioception and interoception. Case Study 8: Experiments showed that internal signals (heartbeat, respiration) influence emotional and cognitive states—supporting “interoceptives are exteroceptives.” ⸻ 9. Quote (1953) “The structure of DNA carries the blueprint of life.” — James Watson and Francis Crick Reference: DNA double helix discovery. Case Study 9: Genetic research confirmed that humans are “made in an image”—a reproducible biological code across generations. ⸻ V. Systems Biology and Environmental Integration (1950–2000) 10. Quote (1968) “The environment and organism form a continuous system.” — Ludwig von Bertalanffy Reference: General Systems Theory. Case Study 10: Ecosystem studies showed that human physiology responds directly to environmental variables (temperature, humidity, pressure), mirroring elemental interaction. ⸻ 11. Quote (1970s) “The Earth functions as a self-regulating system.” — James Lovelock Reference: Gaia Hypothesis. Case Study 11: Climate models demonstrated that Earth behaves like a living organism—supporting the poetic concept of Earth as a “temple.” ⸻ VI. Consciousness and Embodiment (2000–2020) 12. Quote (2002) “The feeling of what happens is the basis of consciousness.” — Antonio Damasio Reference: The Feeling of What Happens Case Study 12: Neuroscience studies showed that bodily states shape perception, reinforcing unity of inner and outer experience. ⸻ 13. Quote (2010) “Interoception is central to emotional awareness and decision-making.” — Modern neuroscience consensus Reference: Research in cognitive neuroscience journals. Case Study 13: MRI studies revealed that insular cortex activity links internal bodily signals to external perception—validating sensory unity.

🧠 Neuroscience Insight: How We Learn Faster (Inspired by the TEDx Talk by Chris Lonsdale) ~ Watch the talk here: https://lnkd.in/gq9fdUrB Language learning is often seen as a slow, difficult process, but from a neuroscience perspective, the brain is actually optimized for rapid learning when the right conditions are met. In this talk, the focus is on how we learn rather than what we learn, and this aligns strongly with principles from Neuroplasticity, the brain’s remarkable ability to adapt and reorganise itself over time. ~ What neuroscience tells us: • The brain rewires itself through repetition, relevance, and emotional engagement • Learning accelerates when input is meaningful and context-based, not memorized in isolation • Active use of information strengthens neural pathways more than passive exposure • The brain prioritises patterns, not rules especially in language acquisition This explains why immersion, listening first, and real-world interaction often outperform traditional rote learning methods. ~ A simple neuroscience framework to remember: Meaning → Relevance → Attention → Memory These four elements are tightly connected in the brain: • Meaning makes information emotionally and cognitively important • Relevance signals the brain that the information matters • Attention allows the brain to focus resources on that information • Memory forms when meaningful and relevant information is repeatedly attended to In short: The brain remembers what feels meaningful and useful. ~Key takeaway: The brain is not slow; it is selective. When learning is aligned with how neural circuits naturally adapt, progress becomes faster and more sustainable. Understanding the brain changes how we approach learning from effort-heavy to strategy-driven. #Neuroscience #LearningScience #Neuroplasticity #BrainBasedLearning #Memory #Attention #LanguageLearning #CognitiveScience #StudySmart #Education #ScienceCommunication #CognitiveNeuroScience #StudySmart #Education #SkillDevelopment #LinkedInLearning #Neurosciencefacts #ChrisLonsdale #TEDxTalk #Neuroscientist #Neurosciencesociety #Fens

Happy Brain Awareness Week! We’re excited to share a new four-video series introducing ARNI’s work and the emerging field of NeuroAI, created in collaboration with Columbia Engineering Communications. The four short videos introduce the mission of the NSF AI Institute for Artificial and Natural Intelligence (ARNI) and explore a core question driving our research: What are the principles of intelligence shared by brains and machines? The videos explore how connecting advances in neuroscience and cognitive science with AI can help uncover the principles of intelligence, across both biological and engineered systems. Watch the ARNI video series: https://lnkd.in/e_ehzC-V Learn more about the project: https://lnkd.in/eTKN5Awj hashtag #NeuroAI hashtag #AIResearch hashtag #MachineLearning hashtag #Neuroscience hashtag #FutureOfAI

What are the principles of intelligence shared by brains and machines? NSF AI Institute for Artificial and Natural Intelligence (ARNI) explores this question and more ... #AI #ML #NSFfunded

Happy Brain Awareness Week! We’re excited to share a new four-video series introducing ARNI’s work and the emerging field of NeuroAI, created in collaboration with Columbia Engineering Communications. The four short videos introduce the mission of the NSF AI Institute for Artificial and Natural Intelligence (ARNI) and explore a core question driving our research: What are the principles of intelligence shared by brains and machines? The videos explore how connecting advances in neuroscience and cognitive science with AI can help uncover the principles of intelligence, across both biological and engineered systems. Watch the  ARNI video series: https://lnkd.in/e_ehzC-V Learn more about the project: https://lnkd.in/eTKN5Awj #NeuroAI #AIResearch #MachineLearning #Neuroscience #FutureOfAI

Happy Brain Awareness Week! 🧠 This week we’re celebrating the research at Columbia Engineering that advances brain science, like the NSF AI Institute for Artificial and Natural Intelligence (ARNI). ARNI bridges neuroscience, cognitive science, and artificial intelligence to build smarter AI algorithms grounded in principles of brain and cognition. The center also uses AI to accelerate discovery in neuroscience. ARNI director Richard Zemel and fellow researchers explain ARNI’s mission in a new playlist of short videos. Explore the playlist: https://bit.ly/3PdDpCA

Mark Brain Awareness Week 🧠 by exploring Columbia University research that advances brain science, like the NSF AI Institute for Artificial and Natural Intelligence (ARNI). ARNI bridges neuroscience, cognitive science, and artificial intelligence to build smarter AI algorithms grounded in principles of brain and cognition. The center also uses AI to accelerate discovery in neuroscience. ARNI director & DSI member Richard Zemel and fellow researchers explain ARNI’s mission in a new playlist of short videos from Columbia Engineering: https://bit.ly/3PdDpCA

How can machines achieve the kind of flexible learning mastered by our brains? That’s what our own Kimberly Stachenfeld is trying to find out! Learn more about NSF AI Institute for Artificial and Natural Intelligence (ARNI)’s work on natural and artificial intelligence. #BrainAwarenessWeek

One of the highlights of my week was seeing "Functional hierarchy of the human neocortex across the lifespan" by Pew-Thian Yap and colleagues published in Nature Magazine. The work describes how the brain’s major functional gradients (cortical hierarchies that underlie distinct modes of information processing) shift from infancy to age 100. In early life, the brain is anchored by its sensory systems; as children move into adolescence, these simple foundations give way to richer, more differentiated hierarchies that support reasoning, attention, and self‑directed cognition. By adulthood, the architecture stabilizes into the familiar patterns we recognize in healthy young brains. With age, these distinctions begin to fade. The boundaries between networks blur, and the brain’s once‑sharp hierarchy gradually relaxes—mirroring changes in flexibility, memory, and cognition. What makes this work stand out is that these gradients don’t just describe the brain—they help predict cognitive performance, relate to how structure and function align, and reflect underlying genetic influences that are strongest early in life. I'm so glad to see this new reference for understanding how the brain develops, matures, and ages in our pages! #Neuroscience #BrainDevelopment #AgingResearch #fMRI #Cognition https://lnkd.in/eyessMRH

Facebook just built a model that can predict how your brain responds to videos, audio, and text. TRIBE v2 is a deep multimodal brain encoding foundation model. It predicts fMRI responses to naturalistic stimuli across three modalities. The system integrates pretrained feature extractors: LLaMA 3.2 for text, V-JEPA2 for video, and Wav2Vec-BERT for audio. A Transformer architecture maps these representations to cortical surfaces. This enables zero-shot predictions across subjects, languages, and tasks. The model was trained on over 500 hours of fMRI data from 700 individuals. That's massive scale for neuroscience research. What makes TRIBE v2 groundbreaking: → Simulates neuroscience experiments in silico → Denoises noisy fMRI data to produce canonical brain patterns → Supports brain visualization and ROI analysis → Built with PyTorch Lightning and PyVista tools → Available on Hugging Face with 89 likes showing community interest This isn't just academic research. It's a foundation model for in-silico neuroscience. Researchers can now run virtual brain experiments without new fMRI scans. The implications for understanding cognition are profound. 📷 Image credit: Gemini Nano Banana #TheAIConsultant #AI #Neuroscience #MachineLearning #BrainEncoding #FMRI #DeepLearning