Prólogo

Homenaje a Georges Laplace, realizado en Vitoria-Gasteiz el 13,14 y 15 de noviembre de 2012. Edición a cargo de Aitor Calvo, Aitor Sánchez, Maite García-Rojas y Mónica Alonso-Eguíluz.

Solvent-Free Enantioselective Organocatalyzed Aldol Reactions

The use of proline as catalyst for the aldol process has given a boost to the development of organocatalysis as a research area. Since then, a plethora of organocatalysts of diverse structures have been developed for this and other organic transformations under different reaction conditions. The use of an organic molecule as catalyst to promote a reaction meets several principles of Green Chemistry. The implementation of solvent-free methodologies to carry out the aldol reaction was soon envisaged. These solvent-free processes can be performed using conventional magnetic stirring or applying ball milling techniques and are even compatible with the use of supported organocatalysts as promoters, which allows the recovery and reuse of the organocatalysts. In addition, other advantages such as the reduction of the required amount of nucleophile and the acceleration of the reaction are accomplished by using solvent-free conditions leading to a “greener” and more sustainable process.

Sensitivity and Bias within the Binary Signal Detection Theory, BSDT

Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared.

ROC Curves within the Framework of Neural Network Assembly Memory Model: Some Analytic Results

On the basis of convolutional (Hamming) version of recent Neural Network Assembly Memory Model (NNAMM) for intact two-layer autoassociative Hopfield network optimal receiver operating characteristics (ROCs) have been derived analytically. A method of taking into account explicitly a priori probabilities of alternative hypotheses on the structure of information initiating memory trace retrieval and modified ROCs (mROCs, a posteriori probabilities of correct recall vs. false alarm probability) are introduced. The comparison of empirical and calculated ROCs (or mROCs) demonstrates that they coincide quantitatively and in this way intensities of cues used in appropriate experiments may be estimated. It has been found that basic ROC properties which are one of experimental findings underpinning dual-process models of recognition memory can be explained within our one-factor NNAMM.