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Beyond Scaling: How Brains Reorganize to Support Higher Intelligence
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DOI：10.3389/fnsys.2026.1847194.png)
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Abstract
En 中文
Systems neuroscience—from Lashley's distributed engrams through Pribram's field-based pro-cessing to Freeman's oscillatory dynamics—has long argued that intelligence is a whole-brain property requiring feedback-driven computation. We formalize this tradition using Reinforcement Learning and Approximate Dynamic Programming (RLADP) and propose that vertebrate intelligence falls into four qualitatively distinct levels—rodent; primate; human; cetacean—each defined by a different architecture for generating and propagating backprop-agated feedback signals. The transition between levels is not parametric but architectural; and each architecture demands a different energy strategy. A conserved allometric rule for cortical ion channels holds across nine of ten mammalian species; fixing the biophysical cost of computation per unit volume; human neurons uniquely violate this rule; reducing channel density to redirect energy toward long-range white matter connectivity. We show that white matter is an active communication system whose superlinear scaling creates a geometric cost trap; that the corticothalamic loop provides master timing for forward-backward cortical processing cycles; and that timing degradation causes qualitative intelligence failure. The biological strategies cataloged here—from selective connectivity reduction to cellular energy reallocation to cortical reorganization—have parallels with the communication-energy wall now constraining artificial intelligence.
Keywords:
artificial intelligence
Comparative Neuroanatomy
Cortical energy model
corticothalamic timing
white matter
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