This document discusses sparse distributed representations (SDRs), which are theorized to be the common data structure used in the cortex. SDRs have several key properties, including extremely high storage capacity, robustness to noise and random deletions, ability to represent multiple patterns in a single structure, and enabling highly efficient computations. Analysis of SDRs can provide a foundation for understanding cortical computing and functions like perception, planning, and attention. The document outlines fundamental attributes and analysis of error bounds for SDR matching, unions, and other operations.