Abstract

The brain generates cognition and behavior through firing changes of its neurons, yet, with enormous firing variability, the organizing principle underlying real-time neural code remains unclear. Here, we test the Neural Self-Information Theory that neural code is constructed via the self-information principle under which each inter-spike-interval (ISI) is inherently self-tagged with discrete information based on its relation to ISI variability-probability distribution - higher-probability ISIs, which reflect the balanced excitation-inhibition ground state, convey minimal information, whereas lower-probability ISIs, which signify statistical surprisals, carry more information. Moreover, temporally coordinated ISI surprisals across neural cliques intrinsically give rise to real-time cell-assembly neural code. As a result, this self-information-based neural coding is uniquely intrinsic to the neurons themselves, with no need for outside observers to set any reference point to manually mark external or internal inputs. Applying this neural self-information concept, we devised an unbiased general decoding strategy and successfully uncovered 15 distinct cell-assembly patterns from multiple cortical and hippocampal circuits associated with different sleep cycles, earthquake, elevator-drop, foot-shock experiences, navigation or various actions in five-choice visual-discrimination operant-conditioning tasks. Detailed analyses of all 15 cell assemblies revealed that ~20% of the skewed ISI distribution tails were responsible for the emergence of robust cell-assembly codes, conforming to the Pareto Principle. These findings support the notion that neural coding is organized via the self-information principle to generate real-time information across brain regions, cognitive modalities, and behaviors.