The Manifold of Neural Responses Informs Physiological Circuits in the Visual System

The rapid development of multi-electrode and imaging techniques is leading to a data explosion in neuroscience, opening the possibility of truly understanding the organization and functionality of our visual systems. Furthermore, the need for more natural visual stimuli greatly increases the complexity of the data. Together, these create a challenge for machine learning. Our goal in this thesis is to develop one such technique. The central pillar of our contribution is designing a manifold of neurons, and providing an algorithmic approach to inferring it. This manifold is functional, in the sense that nearby neurons on the manifold respond similarly (in time) to similar aspects of the stimulus ensemble. By organizing the neurons, our manifold differs from other, standard manifolds as they are used in visual neuroscience which instead organize the stimuli. Our contributions to the machine learning component of the thesis are twofold. First, we develop a tensor representation of the data, adopting a multilinear view of potential circuitry. Tensor factorization then provides an intermediate representation between the neural data and the manifold. We found that the rank of the neural factor matrix can be used to select an appropriate number of tensor factors. Second, to apply manifold learning techniques, a similarity kernel on the data must be defined. Like many others, we employ a Gaussian kernel, but refine it based on a proposed graph sparsification technique—this makes the resulting manifolds less sensitive to the choice of bandwidth parameter. We apply this method to neuroscience data recorded from retina and primary visual cortex in the mouse. For the algorithm to work, however, the underlying circuitry must be exercised to as full an extent as possible. To this end, we develop an ensemble of flow stimuli, which simulate what the mouse would \u27see\u27 running through a field. Applying the algorithm to the retina reveals that neurons form clusters corresponding to known retinal ganglion cell types. In the cortex, a continuous manifold is found, indicating that, from a functional circuit point of view, there may be a continuum of cortical function types. Interestingly, both manifolds share similar global coordinates, which hint at what the key ingredients to vision might be. Lastly, we turn to perhaps the most widely used model for the cortex: deep convolutional networks. Their feedforward architecture leads to manifolds that are even more clustered than the retina, and not at all like that of the cortex. This suggests, perhaps, that they may not suffice as general models for Artificial Intelligence

Ethene dimerization on zeolite-hosted Ni ions : reversible mobilization of the active site

The active site in ethene oligomerization catalyzed by Ni-zeolites is proposed to be a mobile Ni(II) complex, based on density functional theory-based molecular dynamics (DFT-MD) simulations corroborated by continuous-flow experiments on Ni-SSZ-24 zeolite. The results of the simulations at operating conditions show that ethene molecules reversibly mobilize the active site as they exchange with the zeolite as ligands on Ni during reaction. Microkinetic modeling was conducted on the basis of free-energy profiles derived with DFT-MD for oligomerization on these mobile [(ethene)(2)-Ni-alkyl](+) species. The model reproduces the experimentally observed high selectivity to dimerization and indicates that the mechanism is consistent with the observed second-order rate dependence on ethene pressure

Methode zur Rückstandsanalyse von elf Organozinn-Verbindungen in Fischen und Muscheln mit GC-AED

Es wurde ein Analysenverfahren entwickelt, mit dem mehrere OZV nebeneinander in fettarmen und fetthaltigen Fischen und in Muscheln selektiv, empfindlich und zuverlässig bestimmt werden können. Als Detektionssysteme wurden LC-MS/MS und GC-AED erprobt. Für eine Multimethode war die GC-AED am besten geeignet, bei der die OZV mit Natriumtetraethylborat derivatisiert und an einer Kapillarsäule DB-5 getrennt wurden. Untersucht wurden verschiedene Aufschlussverfahren und Extraktions-/Elutionsmittel für die Reinigung durch Säulenchromatographie, zur schnellen und einfachen Extraktion ohne zusätzliche Reinigung außerdem die Festphasen-Mikroextraktion (SPME). Am besten geeignet war die enzymatische Hydrolyse der Matrix mit Lipase/Protease, Extraktion mit Cyclohexan und Reinigung an Florisil unter Eluieren mit Cyclohexan/Toluol (1+1). Das Verfahren wurde statistisch beurteilt und 40 Proben damit untersucht

Raising the COx Methanation Activity of a Ru/γ-Al2O3 Catalyst by Activated Modification of Metal–Support Interactions

Ru/Al2O3 is a highly stable, but less active catalyst for methanation reactions. Herein we report an effective approach to significantly improve its performance in the methanation of CO2/H2 mixtures. Highly active and stable Ru/γ-Al2O3 catalysts were prepared by high-temperature treatment in the reductive reaction gas. Operando/in situ spectroscopy and STEM imaging reveals that the strongly improved activity, by factors of 5 and 14 for CO and CO2 methanation, is accompanied by a flattening of the Ru nanoparticles and the formation of highly basic hydroxylated alumina sites. We propose a modification of the metal–support interactions (MSIs) as the origin of the increased activity, caused by modification of the Al2O3 surface in the reductive atmosphere and an increased thermal mobility of the Ru nanoparticles, allowing their transfer to modified surface sites

Synthesis of C2-Symmetric Diphosphormonoamidites and Their Use as Ligands in Rh-Catalyzed Hydroformylation: Relationships between Activity and Hydrolysis Stability

A series of diphosphoramidites has been synthetized with a piperazine, homopiperazine, and an acyclic 1,2-diamine unit in the backbone. New compounds were tested alongside related N-acyl phosphoramidites as ligands in the Rh-catalyzed hydroformylation of n-octenes to investigate their influence on the activity and regioselectivity. A subsequent study of their hydrolysis stability revealed that the most stable ligands induced the highest activity in the catalytic reaction