“For the entire nervous system is nothing but a system of paths between a sensory terminus a quo and a muscular, glandular, or other terminus ad quem.” William James (Principles of Psychology, 1890, p. 108, italics by James)

This chapter provides a larger perspective and background on the neural blackboard architectures and the underlying theory that have been developed over the last decades. The aim of these is to model compositional ‘symbolic’ processing, e.g. as found in language, in a neural manner. Neural blackboard architectures achieve this with a form of ‘logistics of access’ that is different from symbolic architectures. In particular, conceptual representations remain ‘in situ’ and hence content addressable in any compositional structure of which they are a part. ‘Symbolic’ processing then consists of the creation and control of temporal connection paths in neural blackboards that possess a ‘small world’ connection structure. In language, a connection path provides the intrinsic structure of a sentence. In this way, arbitrary sentence structures can be created and processed, and simulations can reproduce and predict brain activity observed in sentence processing. Next to presenting an overview, the chapter will discuss theoretical and modeling foundations and compare them with forms of symbolic processing as found in other AI architectures.