Since Robinson and Gregory demonstrated the need to administer analgesia to preterm infants and the safety of such anaesthestic techniques in this specific patient population, pain in neonates and adequate analgesia have drawn more and more attention. Thanks to the outstanding work by Anand et al, it became increasingly clear that premature infants experience stress during invasive procedures and that as a consequence long-term neurodevelopmental status may be affected. Fetuses also demonstrate a stress response. Fetal analgesia can be administered efficiently, eliminating the fetal stress response. However, it remains unclear whether this results in improved neurodevelopment and improved long term outcome.

Various issues concerning the neural blackboard architectures for combinatorial structures are discussed and clarified. They range from issues related to neural dynamics, the structure of the architectures for language and vision, and alternative architectures, to linguistic issues concerning the language architecture. Particular attention is given to the nature of true combinatorial structures and the way in which information can be retrieved from them in a productive and systematic manner.

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables, and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural “blackboard” architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. Perspectives and potential developments of the architectures are discussed.

We discuss a visual blackboard architecture that could be involved in imagery. In this architecture, networks that process identity information interact with networks that process location information, in a manner that produces structural (compositional) forms of representation. Architectures of this kind can be identified in the visual cortex, but perhaps also in prefrontal cortex areas related with working memory.