Generative Predictive Codes by Multiplexed Hippocampal Neuronal Tuplets.

Rapid internal representations are continuously formed based on single experiential episodes in space and time, but the neuronal ensemble mechanisms enabling rapid encoding without constraining the capacity for multiple distinct representations are unknown. We developed a probabilistic statistical model of hippocampal spontaneous sequential activity and revealed existence of an internal model of generative predictive codes for the regularities of multiple future novel spatial sequences. During navigation, the inferred difference between external stimuli and the internal model was encoded by emergence of intrinsic-unlikely, novel functional connections, which updated the model by preferentially potentiating post-experience. This internal model and these predictive codes depended on neuronal organization into inferred modules of short, high-repeat sequential neuronal "tuplets" operating as "neuro-codons." We propose that flexible multiplexing of neuronal tuplets into repertoires of extended sequences vastly expands the capacity of hippocampal predictive codes, which could initiate top-down hierarchical cortical loops for spatial and mental navigation and rapid learning.