Statistical Parametric Mapping (SPM, homepage) represents a pivotal development in the history of active inference and computational neuroscience. Created by Karl Friston at the MRC Cyclotron Unit in the late 1980s, SPM began as a statistical technique for analyzing brain imaging data, particularly fMRI, PET, and EEG data (Wikipedia and History).
The development of SPM marked a crucial shift from simple region-of-interest analyses to whole-brain statistical approaches. Originally written in MATLAB, SPM91 (also known as SPMclassic) became the community standard for analyzing neuroimaging studies within a few years of its release. The software's success stemmed from its rigorous approach to making valid statistical inferences about brain responses without prior knowledge of where those responses would occur.
SPM's theoretical framework evolved to incorporate increasingly sophisticated statistical methods, including the general linear model (GLM) and Gaussian field theory. This evolution paralleled and supported the development of active inference theory, as many of the mathematical principles underlying SPM - particularly those involving free energy minimization and Bayesian inference - became foundational to active inference. Today, while dedicated Implementations of Active Inference toolboxes exist in various programming languages (like PyMDP in Python, RxInfer.jl in Julia), SPM remains significant as both a historical cornerstone and practical tool in the field.