Relevant academic work, hopefully, will be interesting to someone wrt. NAH. From Fields et al. (2023):
We show in this paper that control flow in such systems can always be formally described as a tensor network, a factorization of some overall tensor (i.e., high-dimensional matrix) operator into multiple component tensor operators that are pairwise contracted on shared degrees of freedom [48]. In particular, we show that the factorization conditions that allow the construction of a TN are exactly the same as those that allow the identification of distinct, mutually conditionally independent (in quantum terms, decoherent), sets of data on the MB, and hence allow the identification of distinct “objects” or “features” in the environment. This equivalence allows the topological structures of TNs – many of which have been well-characterized in applications of the TN formalism to other domains [48] – to be employed as a classification of control structures in active inference systems; including cells, organisms, and multi-organism communities. It allows, in particular, a principled approach to the question of whether, and to what extent, a cognitive system can impose a decompositional or mereological (i.e., part-whole) structure on its environment. Such structures naturally invoke a notion of locality, and hence of geometry. The geometry of spacetime itself has been described as a particular TN – a multiscale entanglement renormalization ansatz (MERA) [49, 50, 51] – suggesting a deep link between control flow in systems capable of observing spacetime (i.e., capable of implementing internal representations of spacetime) and the deep structure of spacetime as a physical construct.
Relevant academic work, hopefully, will be interesting to someone wrt. NAH. From Fields et al. (2023):