Store Working Memory Networks for Storage and Recall of Arbitrary Temporal Sequences

Neural network models of working memory, called Sustained Temporal Order REcurrent (STORE) models, are described. They encode the invariant temporal order of sequential events in short term memory (STM) in a way that mimics cognitive data about working memory, including primacy, recency, and bowed order and error gradients. As new items are presented, the pattern of previously stored items is invariant in the sense that, relative activations remain constant through time. This invariant temporal order code enables all possible groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such a competence is needed to design self-organizing temporal recognition and planning systems in which any subsequence of events may need to be categorized in order to to control and predict future behavior or external events. STORE models show how arbitrary event sequences may be invariantly stored, including repeated events. A preprocessor interacts with the working memory to represent event repeats in spatially separate locations. It is shown why at least two processing levels are needed to invariantly store events presented with variable durations and interstimulus intervals. It is also shown how network parameters control the type and shape of primacy, recency, or bowed temporal order gradients that will be stored.

Characterization of TRAF6 mediated ubiquitination of presenilins and γ-secretase substrates

Post-translational modification of the γ-secretase protease complexes and their substrates has an important role in controlling receptor-initiated signalling events, which are critically important in the pathogenesis of cancer, inflammatory and Alzheimer’s disease. Our lab has previously characterised an interaction between TRAF6 and presenilin-1, which lead to the identification of interleukin-1 (IL-1) receptor type 1 (IL-1R1) and Toll-like receptor-4 (TLR4) as novel γ-secretase substrates. Subsequently our group showed that TRAF6 promoted ubiquitination and γ-secretase cleavage of IL-1R1. The aim of this project is to study the association between TRAF6 and the presenilins, the critical γ-secretase complex components, and to determine the functional importance of TRAF6-mediated ubiquitination of γ-secretase substrates. Firstly, we show that the full-length presenilins are novel substrates of TRAF6-mediated Lysine-63-linked polyubiquitination. Secondly, we show that co-expression of TRAF6 and the presenilins increases the stability and alters the turnover of the presenilins. Thirdly, we reveal that TRAF6-mediated ubiquitination of presenilin does not affect γ-secretase enzyme activity, but may regulate the full-length presenilin functions such as ER Ca2+ signalling. Previously, we have reported IL-1R1 as a novel substrate of TRAF6-mediated ubiquitination. In this study, we identified five lysine residues in the IL-1R1 intracellular domain targeted by TRAF6-mediated polyubiquitination. Furthermore, mutagenesis of these five lysine residues led to decreased IL-1R1 cell surface expression, precluded the ectodomain shedding and attenuated the responsiveness to IL-1β stimulation, demonstrating the critical role of TRAF6 in IL-1R1 trafficking.