You’re talking about a pure hierarchy — “passing their conclusions onto next layers to decide.” This “thousand brains” concept refers instead to a peerwise mesh

You’re talking about a pure hierarchy — “passing their conclusions onto next layers to decide.” This “thousand brains” concept refers instead to a peerwise mesh, where “workers” at each layer receive both lower-layer inputs, and also their peers’ outputs, and use both to derive an output that evolves over time, as outputs are passed between peers, until reaching a layerwise equilibrium-state (a “consensus.”) The (again, many) models in the layer above receive this evolving consensus as the output from subsets of nodes in the layer below — potentially with overlap in which upper-layer models receive outputs of which lower-layer models; and each of these models themselves evolve to a consensus. By analogy: DCNN is like representative democracy, where each layer is a representative with the layer below as its “constituents”, forming a belief based on the multitude of lower-layer beliefs it hears, and then acting on its own formed belief when reporting to the layer above. This “thousand brains” model is more like grouping constituents into juries and running them through the Delphi-model iterative consensus; and then having representatives who act on continuous-feed statistical aggregations of the Delphi-model iteration-step outputs of multiple juries.