Imagine the cortex as a bustling dance floor, where each neuron is a dancer following the rhythm of synaptic selection. As the music plays, more structured inputs join the dance, prompting an intricate choreography of self-organization at local cortical sites. This culminates in the emergence of ultra-small world structures that can represent sequences of spatiotemporal images. Like couples shifting partners, presynapses switch from excitatory to inhibitory cells, creating spatial eigenmodes and developing Markov blankets that minimize prediction errors. As the party gets livelier with inputs from different cortical areas, cognitive structures are selected through merging units and eliminating redundant connections. The brain does this clever trick by minimizing variational free energy and getting rid of unnecessary degrees of freedom. This process is shaped by interactions with sensorimotor, limbic, and brainstem mechanisms, paving the way for unlimited associative learning and creative breakthroughs.

Capacity for generativity and unlimited association is the defining characteristic of sentience, and this capacity somehow arises from neuronal self-organization in the cortex. We have previously argued that, consistent with the free energy principle, cortical development is driven by synaptic and cellular selection maximizing synchrony, with effects manifesting in a wide range of features of mesoscopic cortical anatomy. Here, we further argue that in the postnatal stage, as more structured inputs reach the cortex, the same principles of self-organization continue to operate at multitudes of local cortical sites. The unitary ultra-small world structures that emerged antenatally can represent sequences of spatiotemporal images. Local shifts of presynapses from excitatory to inhibitory cells result in the local coupling of spatial eigenmodes and the development of Markov blankets, minimizing prediction errors in each unit’s interactions with surrounding neurons. In response to the superposition of inputs exchanged between cortical areas, more complicated, potentially cognitive structures are competitively selected by the merging of units and the elimination of redundant connections that result from the minimization of variational free energy and the elimination of redundant degrees of freedom. The trajectory along which free energy is minimized is shaped by interaction with sensorimotor, limbic, and brainstem mechanisms, providing a basis for creative and unlimited associative learning.

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