Geometric representations for minimalist grammars

We reformulate minimalist grammars as partial functions on term algebras for strings and trees. Using filler/role bindings and tensor product representations, we construct homomorphisms for these data structures into geometric vector spaces. We prove that the structure-building functions as well as simple processors for minimalist languages can be realized by piecewise linear operators in representation space. We also propose harmony, i.e. the distance of an intermediate processing step from the final well-formed state in representation space, as a measure of processing complexity. Finally, we illustrate our findings by means of two particular arithmetic and fractal representations.Comment: 43 pages, 4 figure

Dynamic searching in the brain

Cognitive functions rely on the extensive use of information stored in the brain, and the searching for the relevant information for solving some problem is a very complex task. Human cognition largely uses biological search engines, and we assume that to study cognitive function we need to understand the way these brain search engines work. The approach we favor is to study multi-modular network models, able to solve particular problems that involve searching for information. The building blocks of these multimodular networks are the context dependent memory models we have been using for almost 20 years. These models work by associating an output to the Kronecker product of an input and a context. Input, context and output are vectors that represent cognitive variables. Our models constitute a natural extension of the traditional linear associator. We show that coding the information in vectors that are processed through association matrices, allows for a direct contact between these memory models and some procedures that are now classical in the Information Retrieval field. One essential feature of context-dependent models is that they are based on the thematic packing of information, whereby each context points to a particular set of related concepts. The thematic packing can be extended to multimodular networks involving input-output contexts, in order to accomplish more complex tasks. Contexts act as passwords that elicit the appropriate memory to deal with a query. We also show toy versions of several ‘neuromimetic’ devices that solve cognitive tasks as diverse as decision making or word sense disambiguation. The functioning of these multimodular networks can be described as dynamical systems at the level of cognitive variables

Langerhans cells down-regulate inflammation-driven alveolar bone loss

Excessive bone resorption is frequently associated with chronic infections and inflammatory diseases. Whereas T cells were demonstrated to facilitate osteoclastogenesis in such diseases, the role of dendritic cells, the most potent activators of naive T cells, remains unclear. Using a model involving inflammation-driven alveolar bone loss attributable to infection, we showed that in vivo ablation of Langerhans cells (LCs) resulted in enhanced bone loss. An increased infiltration of B and T lymphocytes into the tissue surrounding the bone was observed in LC-ablated mice, including receptor activator of NF-kappa B ligand (RANKL)-expressing CD4(+) T cells with known capabilities of altering bone homeostasis. In addition, the absence of LCs significantly reduced the numbers of CD4(+)Foxp3(+) T-regulatory cells in the tissue. Further investigation revealed that LCs were not directly involved in presenting antigens to T cells. Nevertheless, despite their low numbers in the tissue, the absence of LCs resulted in an elevated activation of CD4(+) but not CD8(+) T cells. This activation involved elevated production of IFN-gamma but not IL-17 or IL-10 cytokines. Our data, thus, reveal a protective immunoregulatory role for LCs in inflammation-induced alveolar bone resorption, by inhibiting IFN-gamma secretion and excessive activation of RANKL(+)CD4(+) T cells with a capability of promoting osteoclastogenesis