Catalytic Transformations of Alkynes via Ruthenium Vinylidene and Allenylidene Intermediates

NOTICE: This is the peer reviewed version of the following book chapter: Varela J. A., González-Rodríguez C., Saá C. (2014). Catalytic Transformations of Alkynes via Ruthenium Vinylidene and Allenylidene Intermediates. In: Dixneuf P., Bruneau C. (eds) Ruthenium in Catalysis. Topics in Organometallic Chemistry, vol 48, pp. 237-287. Springer, Cham. [doi: 10.1007/3418_2014_81]. This article may be used for non-commercial purposes in accordance with Springer Verlag Terms and Conditions for self-archiving.Vinylidenes are high-energy tautomers of terminal alkynes and they can be stabilized by coordination with transition metals. The resulting metal-vinylidene species have interesting chemical properties that make their reactivity different to that of the free and metal π-coordinated alkynes: the carbon α to the metal is electrophilic whereas the β carbon is nucleophilic. Ruthenium is one of the most commonly used transition metals to stabilize vinylidenes and the resulting species can undergo a range of useful transformations. The most remarkable transformations are the regioselective anti-Markovnikov addition of different nucleophiles to catalytic ruthenium vinylidenes and the participation of the π system of catalytic ruthenium vinylidenes in pericyclic reactions. Ruthenium vinylidenes have also been employed as precatalysts in ring closing metathesis (RCM) or ring opening metathesis polymerization (ROMP). Allenylidenes could be considered as divalent radicals derived from allenes. In a similar way to vinylidenes, allenylidenes can be stabilized by coordination with transition metals and again ruthenium is one of the most widely used metals. Metalallenylidene complexes can be easily obtained from terminal propargylic alcohols by dehydration of the initially formed metal-hydroxyvinylidenes, in which the reactivity of these metal complexes is based on the electrophilic nature of Cα and Cγ, while Cβ is nucleophilic. Catalytic processes based on nucleophilic additions and pericyclic reactions involving the π system of ruthenium allenylidenes afford interesting new structures with high selectivity and atom economy

Charles Bonnet Syndrome:Evidence for a Generative Model in the Cortex?

Several theories propose that the cortex implements an internal model to explain, predict, and learn about sensory data, but the nature of this model is unclear. One condition that could be highly informative here is Charles Bonnet syndrome (CBS), where loss of vision leads to complex, vivid visual hallucinations of objects, people, and whole scenes. CBS could be taken as indication that there is a generative model in the brain, specifically one that can synthesise rich, consistent visual representations even in the absence of actual visual input. The processes that lead to CBS are poorly understood. Here, we argue that a model recently introduced in machine learning, the deep Boltzmann machine (DBM), could capture the relevant aspects of (hypothetical) generative processing in the cortex. The DBM carries both the semantics of a probabilistic generative model and of a neural network. The latter allows us to model a concrete neural mechanism that could underlie CBS, namely, homeostatic regulation of neuronal activity. We show that homeostatic plasticity could serve to make the learnt internal model robust against e.g. degradation of sensory input, but overcompensate in the case of CBS, leading to hallucinations. We demonstrate how a wide range of features of CBS can be explained in the model and suggest a potential role for the neuromodulator acetylcholine. This work constitutes the first concrete computational model of CBS and the first application of the DBM as a model in computational neuroscience. Our results lend further credence to the hypothesis of a generative model in the brain