Semantics to system architecture

Figure 1


The book How to build a brain from Oxford University Press was released in early 2013. It exploits the Neural Engineering Framework to develop the Semantic Pointer Architecture (SPA) for cognitive modelling. The book uses Nengo to explain and demonstrate many of the central concepts for these frameworks. This section of the website supports the book by providing links to models, demos, videos, and tutorials mentioned in the book, which are on this website. Submenus at left will take you to content related to specific chapters. The remainder of this page introduces the SPA.

Briefly, the semantic pointer hypothesis states:

Figure 2Higher-level cognitive functions in biological systems are made possible by semantic pointers. Semantic pointers are neural representations that carry partial semantic content and are composable into the representational structures necessary to support complex cognition.

The term 'semantic pointer' was chosen because the representations in the architecture are like 'pointers' in computer science (insofar as they can be 'dereferenced' to access large amounts of information which they do not directly carry). However, they are 'semantic' (unlike pointers in computer science) because these representations capture relations in a semantic vector space in virtue of their distances to one another, as typically envisaged by connectionists.

The four main topics in the book that are used to describe the architecture are semantics, syntax, control, and learning and memory. The discussion of semantics considers how semantic pointers are generated from information that impinges on the senses, reproducing details of the spiking tuning curves in the visual system. Syntax is captured by demonstrating how very large structures can be encoded, by binding semantic pointers to one another. The section on control considers the role of the basal ganglia and other structures in routing information throughout the brain to control cognitive tasks. The section on learning memory describes how the SPA includes adaptability (despite this not being a focus of the NEF) showing how detailed spiking timing during learning can be incorporated into the basal ganglia model using a biologically plausible STDP-like rule.

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