How vision stabilizes after birth: new research

Recent research has identified how early brain structure primes itself for efficient learning. Findings reveal that, even before visual experience, the brain organizes neurons into modules, setting the stage for reliable and rapid interpretation of sensory information. As visual experience accumulates, these modules become better aligned with incoming information, enhancing reliability and adaptability. This developmental process may extend beyond vision, offering a broader framework for understanding how the brain achieves fast, flexible learning. Insights from this work could inform future approaches in neuroscience and artificial intelligence by highlighting mechanisms underlying the brain’s learning efficiency.

🚨 Big step forward for neuroscience. For the first time, scientists have mapped the activity of single neurons across the entire brain during decision-making. That means recording from 600,000+ neurons in 279 brain areas — covering about 95% of the mouse brain volume. An incredible scale that just a few years ago would have sounded impossible. This achievement gives us a first real glimpse of how distributed brain circuits work together to guide behaviour. Exciting times ahead for neuroscience, network science, and computational modeling! https://lnkd.in/dh3McUwu

Researchers investigate how mice process illusions, highlighting the neural circuitry involved in seeing and perception >>> https://lnkd.in/e3V3FYcG Hyeyoung Shin, Hillel Adesnik & Jerome Lecoq University of California, Berkeley & Allen Institute 

🧠🌎 How does the brain build internal models of the world - sometimes without us even realizing it? Very happy to finally share this latest study from my previous academic work, now published in PNAS, that explores the phenomenon of neural reactivation („replay“) in humans and its role in implicit statistical learning. ✨ 🔬 Using fMRI and behavioral modeling, we found that during brief 10-second pauses in an ongoing task, the visual cortex replays implicitly learned sequences. Interestingly, this neural replay was unrelated to explicit awareness of the sequences. 🔍 Key findings: - Replay occurs outside the hippocampus, in visual cortical areas - Replay supports implicit learning of multistep transitions - Replay strength aligns with internal predictive models (successor representation), but not with explicit conscious knowledge These results shed light on how the human brain forms predictive internal maps of the environment, even when we’re unaware of the patterns we're learning. 💡 📄 Read the full paper here: https://lnkd.in/ePGpfBef #Neuroscience #fMRI #CognitiveScience #NeuralReplay #StatisticalLearning #ImplicitLearning #VisualCortex #Memory #PNAS

Delighted to share our work on replay and successor representations led by the one and only Dr. Lennart de Vries, out now in PNAS! We find replay during very short task pauses in human visual cortex that is linked to learning SRs & happens when learning is implicit. It was very interesting to see that replay in visual cortex happens during such short 10s pauses between trials, and that it does not show any obvious relation to explicit knowledge, but seems to support implicit learning.

It's a mind-boggling fact – the human brain generates enough electrical energy to power a small light bulb! This incredible statistic highlights the immense electrical activity occurring within our neural networks every second. But what does this mean for neuroscience research? By leveraging advanced EEG

Researchers have created a novel computational method to decipher the complex communication patterns between neurons. By analyzing their irregular electrical "spikes," the technique accurately identifies which neurons influence others, a key step in understanding brain function and neurological disorders.

TL;DR - educational neuroscience is fascinating and important, but at the moment there's still a big gap between analyses of the brain's physical structure and how cognition functions. Be wary of overly simplistic "the brain lights up during this cognitive process, so we should teach this way" recommendations and explanations. https://lnkd.in/eMuHnwei

Precision Neuroscience, started by ex-Neuralink folks, has developed an ultra-thin brain implant (Layer 7 Cortical Interface) that just got FDA clearance for 30-day use. What’s wild is how minimally invasive it is — instead of drilling deep, it slides in through a tiny incision in the skull and sits on the surface of the brain. The goal? To pick up neural signals and translate thoughts into digital commands. Imagine a paralyzed patient controlling a computer or device just by thinking. As someone who’s super curious about the intersection of AI, neuroscience, and human-machine interfaces, this feels like a glimpse into the future — one where tech is literally bridging biology and digital systems. It also makes me wonder: How far are we from making such devices long-term safe and reliable? Could this open doors not just for medical use, but also for everyday human-AI collaboration? And what role could students like us play in shaping this future?

🧠 The Brain's 80ms Secret: Why Lag Makes Us Conscious The human brain intentionally delays sensory inputs by about 80 milliseconds. This isn't a flaw; it's a fundamental mechanism. This "lag" is crucial for fusing vision, sound, and touch into a single, coherent conscious experience. How does understanding our brain's temporal processing impact future tech design? 1. Optimizing UX: Design interfaces aligning with natural human perception delays. 2. Advancing AI: Informing models for more integrated and context-aware sensory processing. 3. Neurotech Innovation: Guiding development of brain-computer interfaces for seamless interaction. It's fascinating how biology often reveals counter-intuitive 'features' that are actually vital for complex functions. This makes me rethink efficiency in design. https://lnkd.in/eFP8t2gZ How might we leverage this biological insight in AI or human-computer interaction? #Neuroscience #Consciousness #BrainScience #Perception #CognitiveScience #NeuralDelay #TechInnovation