A UNIVERSAL META-ALGORITHM EXPLAINING CEREBELLAR FUNCTIONAL DIVERSITY THROUGH MULTI-LEVEL CONFIGURATION

The cerebellum presents a fundamental paradox. Its remarkably uniform microcircuit architecture supports a vast diversity of functions, ranging from motor control to cognition. Inspired by the structural uniformity and functional versatility of the Transformer architecture, we propose the C.O.R.E. framework to resolve this dilemma. We define cerebellar computation as a universal meta-algorithm comprising four cyclic stages: Contextualized expansion of inputs, Output computation integrating cortical and nuclear pathways, Reconfiguration via multidimensional instruction signals, and Engramming through synaptic plasticity. We argue that functional diversity emerges not from distinct hardware, but through a "multi-level configuration" of this single meta-algorithm. Specifically, the system generates context-specific sub-algorithms by gating inputs, balancing complex versus simple output pathways, and utilizing diverse climbing fiber signals that dictate processing modes. Consequently, this framework harmonizes the "Universal Cerebellar Transform" theory with the "Multiple Functionality" perspective. Ultimately, C.O.R.E. offers a unified explanation for cerebellar disorders as configuration failures and provides a biological blueprint for next-generation meta-learning artificial intelligence.