Biology doesn’t compute the way our current AI systems assume. Neurons aren’t static units. They operate across multiple scales, electrical, mechanical, structural, and quantum-timing, all interacting in real time. Recent neuroscience points to: • fractal dendritic integration • phase-dependent computation in local microdomains • microtubule-scale vibrational timing structures • multi-scale electromechanical coupling inside neurons • nested oscillatory hierarchies coordinating local and global processing These are not captured by today’s neural networks. If AI is going to move beyond statistical pattern matching toward true natural-intelligence behaviors, we need models that reflect: multi-scale geometry, recursive timing structure, and intrinsic dynamical complexity, not just more layers and parameters. This isn’t about copying biology literally; it’s about understanding the computational principles biology actually uses. The next step is to model the right dynamics. @AllenInstitute @mitbrainandcog @stanfordneuro @NeuroCellPress @NatureNeuro @HumanBrainProj @CIFARnews @MetaAI @DeepMind @googledeepmind @drmichaellevin @MITNeuro @MurrayShanahan @KordingLab