Simulating event related potentials in tasks involving conflict resolution: a neural network

In this thesis, the main Executive Control theories are exposed. Methods typical of Cognitive and Computational Neuroscience are introduced and the role of behavioural tasks involving conflict resolution in the response elaboration, after the presentation of a stimulus to the subject, are highlighted. In particular, the Eriksen Flanker Task and its variants are discussed. Behavioural data, from scientific literature, are illustrated in terms of response times and error rates. During experimental behavioural tasks, EEG is registered simultaneously. Thanks to this, event related potential, related with the current task, can be studied. Different theories regarding relevant event related potential in this field - such as N2, fERN (feedback Error Related Negativity) and ERN (Error Related Negativity) – are introduced. The aim of this thesis is to understand and simulate processes regarding Executive Control, including performance improvement, error detection mechanisms, post error adjustments and the role of selective attention, with the help of an original neural network model. The network described here has been built with the purpose to simulate behavioural results of a four choice Eriksen Flanker Task. Model results show that the neural network can simulate response times, error rates and event related potentials quite well. Finally, results are compared with behavioural data and discussed in light of the mentioned Executive Control theories. Future perspective for this new model are outlined.

Selective photo-oxidation of glucose

Biomass transformation into high-value chemicals has attracted attention according to the “green chemistry” principles. Low price and high availability make biomass one of the most interesting renewable resources as it provides the means to create sustainable alternatives to the oil-derived building blocks of the chemical industry In recent year, the need for alternative environmentally friendly routes to drive chemical reactions has in photocatalytic processes an interesting way to obtain valuable chemicals from various sources using the solar light as energy source. The purpose of this work was to use supported noble metal nanoparticles in the selective photo-oxidation of glucose through using visible light. Glucose was chosen as model molecule because it is the cheapest and the most common monosaccharide. Few studies about glucose photo oxidation have been conducted so far, and reaction mechanism is still not totally explained. The aim of this work was to systematically analyze and assess the impact of several parameters (eg. catalyst/substrate ratio, reaction time, effect of the solvent and light source) on the reaction pathway and to monitor the product distribution in order to draw a general reaction scheme for the photo oxidation of glucose under visible light. This study regards the reaction mechanism and the influence of several parameters, such as solvent, light power and substrate concentration. Furthermore, the work focuses on the influence of gold and silver nanoparticles and on the influence of metal loading. The glucose oxidation was monitored through the mass balance and the products selectivity. Reactions were evaluated in terms of glucose conversion, mass balance and selectivities towards arabinose and gluconic acid. In conclusion, this study is able to demonstrate that the photo oxidation of glucose under visible light is feasible; the full identification of the main products allows, for the first time, a comprehensive reaction mechanism scheme.