This paper presents a theoretical framework and enabling specification for a low-inhibition associative cognitive workspace, designated the Dream Galaxy, implemented within decentralized, peer-to-peer sovereign data nodes constituting a federated personal cognitive universe. The framework introduces four interlocking technical contributions. First, a privacy-preserving multi-modal feature projection layer that maps heterogeneous episodic and physiological inputs, including sub-threshold physiological tokenization of continuous EEG, EMG, and EOG streams, into a unified semantic embedding space without cross-node data exposure, using horizontal federated learning with differential privacy at the gradient layer. Second, an asynchronous local topological optimization engine that applies incremental GPU-accelerated persistent homology over a Vietoris-Rips filtration to the projected feature space, maintaining a continuously valid persistence barcode without centralized computation or synchronous coordination across nodes. Third, a four-zone epistemological classifier that assigns each projected feature node a zone-resident probability distribution across Terra Firma (Zone 1), Incognita (Zone 2), Hic Sunt Dracones (Zone 3), and Whisp (Zone 4), where Zone 4 nodes represent pre-cognitive unknown unknowns validated against a local density criterion distinguishing genuine structural absence from data sparsity. Fourth, the Latent Yield metric: a dimension-weighted rate of topological feature birth in Zones 3 and 4, formalized with explicit pseudocode in the Technical Appendix, quantifying cognitive generative activity below the threshold of conscious expression. The paper further specifies an Asynchronous Convergence Protocol by which topologically equivalent planet structures in independent sovereign nodes are detected under differential privacy without raw data exchange, constituting a knowledge event of elevated epistemic weight requiring bilateral consent before any bridge formation. As a Perspectives and Theoretical Frameworks contribution, this paper provides sufficient enabling detail, including algorithmic pseudocode, parameter specifications, and system architecture, for a person having ordinary skill in machine learning or computational neuroscience to implement the described system. The framework is protected by ten United States provisional patent applications (Nos. 64/074,530; 64/074,608; 64/074,779; 64/075,396; 64/079,009; 64/081,935; 64/084,355; 64/086,036; 64/089,881; 64/089,883), the last two filed June 13, 2026.
Eric Hoppe (Sat,) studied this question.