  The "SP theory of intelligence", with its realisation in the "SP computer
model", aims to simplify and integrate observations and concepts across
AI-related fields, with information compression as a unifying theme. This paper
describes how abstract structures and processes in the theory may be realised
in terms of neurons, their interconnections, and the transmission of signals
between neurons. This part of the SP theory -- "SP-neural" -- is a tentative
and partial model for the representation and processing of knowledge in the
brain. In the SP theory (apart from SP-neural), all kinds of knowledge are
represented with "patterns", where a pattern is an array of atomic symbols in
one or two dimensions. In SP-neural, the concept of a "pattern" is realised as
an array of neurons called a "pattern assembly", similar to Hebb's concept of a
"cell assembly" but with important differences. Central to the processing of
information in the SP system is the powerful concept of "multiple alignment",
borrowed and adapted from bioinformatics. Processes such as pattern
recognition, reasoning and problem solving are achieved via the building of
multiple alignments, while unsupervised learning -- significantly different
from the "Hebbian" kinds of learning -- is achieved by creating patterns from
sensory information and also by creating patterns from multiple alignments in
which there is a partial match between one pattern and another. Short-lived
neural structures equivalent to multiple alignments will be created via an
inter-play of excitatory and inhibitory neural signals. The paper discusses
several associated issues, with relevant empirical evidence.
