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The "SP theory of intelligence", with its realisation in the "SP computermodel", aims to simplify and integrate observations and concepts acrossAI-related fields, with information compression as a unifying theme. This paperdescribes how abstract structures and processes in the theory may be realisedin terms of neurons, their interconnections, and the transmission of signalsbetween neurons. This part of the SP theory -- "SP-neural" -- is a tentativeand partial model for the representation and processing of knowledge in thebrain. In the SP theory (apart from SP-neural), all kinds of knowledge arerepresented with "patterns", where a pattern is an array of atomic symbols inone or two dimensions. In SP-neural, the concept of a "pattern" is realised asan array of neurons called a "pattern assembly", similar to Hebb's concept of a"cell assembly" but with important differences. Central to the processing ofinformation in the SP system is the powerful concept of "multiple alignment",borrowed and adapted from bioinformatics. Processes such as patternrecognition, reasoning and problem solving are achieved via the building ofmultiple alignments, while unsupervised learning -- significantly differentfrom the "Hebbian" kinds of learning -- is achieved by creating patterns fromsensory information and also by creating patterns from multiple alignments inwhich there is a partial match between one pattern and another. Short-livedneural structures equivalent to multiple alignments will be created via aninter-play of excitatory and inhibitory neural signals. The paper discussesseveral associated issues, with relevant empirical evidence.

The SP theory of intelligence and the representation and processing of knowledge in the brain