Synchronization Analysis of Winner-Take-All Neuronal Networks

With the physical limitations of current CMOS technology, it becomes necessary to design and develop new methods to perform simple and complex computations. Nature is efficient, so many in the scientific community attempt to mimic it when optimizing or creating new systems and devices. The human brain is looked to as an efficient computing device, inspiring strong interest in developing powerful computer systems that resemble its architecture and behavior such as neural networks. There is much research focusing on both circuit designs that behave like neurons and arrangement of these electromechanical neurons to compute complex operations. It has been shown previously that the synchronization characteristics of neural oscillators can be used not only for primitive computation functions such as convolution but for complex non-Boolean computations. With strong interest in the research community to develop biologically representative neural networks, this dissertation analyzes and simulates biologically plausible networks, the four-dimensional Hodgkin-Huxley and the simpler two-dimensional Fitzhugh-Nagumo neural models, fashioned in winner-take-all neuronal networks. The synchronization behavior of these neurons coupled together is studied in detail. Different neural network topologies are considered including lateral inhibition and inhibition via a global interneuron. Then, this dissertation analyzes the winner-take-all behaviors, in terms of both firing rates and phases, of neuronal networks with different topologies. A technique based on phase response curve is suggested for the analysis of synchronization phase characteristics of winner-take-all networks. Simulations are performed to validate the analytical results. This study promotes the understanding of winner-take-all operations in biological neuronal networks and provides a fundamental basis for applications of winner-take-all networks in modern computing systems

Dynamic social learning under graph constraints

We introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively $\alpha$-homogeneous rewards. We show that its empirical process, which can be written as a stochastic approximation recursion with Markov noise, has the same probability law as a certain vertex reinforced random walk. We use this equivalence to show that for $\alpha > 0$, the asymptotic outcome concentrates around the optimum in a certain limiting sense when `annealed' by letting $\alpha\uparrow\infty$ slowly

A hydrogen-dependent geochemical analogue of primordial carbon and energy metabolism

Hydrogen gas, H2, is generated by alkaline hydrothermal vents through an ancient geochemical process called serpentinization in which water reacts with iron containing minerals deep within the Earth's crust. H2 is the electron donor for the most ancient and the only energy releasing route of biological CO2 fixation, the acetyl-CoA pathway. At the origin of metabolism, CO2 fixation by hydrothermal H2 within serpentinizing systems could have preceded and patterned biotic pathways. Here we show that three hydrothermal minerals—greigite (Fe3S4), magnetite (Fe3O4) and awaruite (Ni3Fe)—catalyse the fixation of CO2 with H2 at 100°C under alkaline aqueous conditions. The product spectrum includes formate (up to 200 mM), acetate (up to 100 µM), pyruvate (up to 10 µM), methanol (up to 100 µM), and methane. The results shed light on both the geochemical origin of microbial metabolism and on the nature of abiotic formate and methane synthesis in modern hydrothermal vents

Biologically inspired learning system

Learning Systems used on robots require either a-priori knowledge in the form of models, rules of thumb or databases or require that robot to physically execute multitudes of trial solutions. The first requirement limits the robot’s ability to operate in unstructured changing environments, and the second limits the robot’s service life and resources. In this research a generalized approach to learning was developed through a series of algorithms that can be used for construction of behaviors that are able to cope with unstructured environments through adaptation of both internal parameters and system structure as a result of a goal based supervisory mechanism. Four main learning algorithms have been developed, along with a goal directed random exploration routine. These algorithms all use the concept of learning from a recent memory in order to save the robot/agent from having to exhaustively execute all trial solutions. The first algorithm is a reactive online learning algorithm that uses a supervised learning to find the sensor/action combinations that promote realization of a preprogrammed goal. It produces a feed forward neural network controller that is used to control the robot. The second algorithm is similar to first in that it uses a supervised learning strategy, but it produces a neural network that considers past values, thus providing a non-reactive solution. The third algorithm is a departure from the first two in that uses a non-supervised learning technique to learn the best actions for each situation the robot encounters. The last algorithm builds a graph of the situations encountered by agent/robot in order to learn to associate the best actions with sensor inputs. It uses an unsupervised learning approach based on shortest paths to a goal situation in the graph in order to generate a non-reactive feed forward neural network. Test results were good, the first and third algorithms were tested in a formation maneuvering task in both simulation and onboard mobile robots, while the second and fourth were tested simulation

Statistical modelling by neural networks

In this thesis the two disciplines of Statistics and Artificial Neural Networks are combined into an integrated study of a data set of a weather modification Experiment. An extensive literature study on artificial neural network methodology has revealed the strongly interdisciplinary nature of the research and the applications in this field. An artificial neural networks are becoming increasingly popular with data analysts, statisticians are becoming more involved in the field. A recursive algoritlun is developed to optimize the number of hidden nodes in a feedforward artificial neural network to demonstrate how existing statistical techniques such as nonlinear regression and the likelihood-ratio test can be applied in innovative ways to develop and refine neural network methodology. This pruning algorithm is an original contribution to the field of artificial neural network methodology that simplifies the process of architecture selection, thereby reducing the number of training sessions that is needed to find a model that fits the data adequately. [n addition, a statistical model to classify weather modification data is developed using both a feedforward multilayer perceptron artificial neural network and a discriminant analysis. The two models are compared and the effectiveness of applying an artificial neural network model to a relatively small data set assessed. The formulation of the problem, the approach that has been followed to solve it and the novel modelling application all combine to make an original contribution to the interdisciplinary fields of Statistics and Artificial Neural Networks as well as to the discipline of meteorology.Mathematical SciencesD. Phil. (Statistics

Pressurized CO2 Electrochemical Conversion to Formic Acid: From Theoretical Model to Experimental Results

To curb the severely rising levels of carbon dioxide in the atmosphere, new approaches to capture and utilize this greenhouse gas are currently being investigated. In the last few years, many researches have focused on the electrochemical conversion of CO2 to added-value products in aqueous electrolyte solutions. In this backdrop, the pressurized electroreduction of CO2 can be assumed an up-and-coming alternative process for the production of valuable organic chemicals [1-3]. In this work, the process was studied in an undivided cell with tin cathode in order to produce formic acid and develop a theoretical model, predicting the effect of several operative parameters. The model is based on the cathodic conversion of pressurized CO2 to HCOOH and it also accounts for its anodic oxidation. In particular, the electrochemical reduction of CO2 to formic acid was performed in pressurized filter press cell with a continuous recirculation of electrolytic solution (0.9 L) at a tin cathode (9 cm2) for a long time (charge passed 67’000 C). It was shown that it is possible to scale-up the process by maintaining good results in terms of faradaic efficiency and generating significantly high concentrations of HCOOH (about 0.4 M) [4]. It was also demonstrated that, for pressurized systems, the process is under the mixed kinetic control of mass transfer of CO2 and the reduction of adsorbed CO2 (described by the Langmuir equation), following our proposed reaction mechanism [5]. Moreover, the theoretical model is in good agreement with the experimental results collected and well describes the effect of several operating parameters, including current density, pressure, and the type of reactor used. 1. Ma, S., & Kenis, P. J. (2013). Electrochemical conversion of CO2 to useful chemicals: current status, remaining challenges, and future opportunities. Current Opinion in Chemical Engineering, 2(2), 191-199. 2. Endrődi, B., Bencsik, G., Darvas, F., Jones, R., Rajeshwar, K., & Janáky, C. (2017). Continuous-flow electroreduction of carbon dioxide. Progress in Energy and Combustion Science, 62, 133-154. 3. Dufek, E. J., Lister, T. E., Stone, S. G., & McIlwain, M. E. (2012). Operation of a pressurized system for continuous reduction of CO2. Journal of The Electrochemical Society, 159(9), F514-F517. 4. Proietto, F., Schiavo, B., Galia, A., & Scialdone, O. (2018). Electrochemical conversion of CO2 to HCOOH at tin cathode in a pressurized undivided filter-press cell. Electrochimica Acta, 277, 30-40. 5. Proietto, F., Galia, A., & Scialdone, O. (2019) Electrochemical conversion of CO2 to HCOOH at tin cathode: development of a theoretical model and comparison with experimental results. ChemElectroChem, 6, 162-172

Effect of the air pressure on electro-Fenton process

Electro-Fenton process is considered a very promising tool for the treatment of waste waters contaminated by organic pollutants refractant or toxic for microorganisms used in biological processes [1-6]. In these processes H2O2 is continuously supplied to an acidic aqueous solution contained in an electrolytic cell from the two-electron reduction of oxygen gas, directly injected as pure gas or bubbled air. Due to the poor solubility of O2 in aqueous solutions, two dimensional cheap graphite or carbon felt electrodes give quite slow generation of H2O2, thus resulting in a slow abatement of organics. In this context, we report here a series of studies [7-9] on the effect of air pressure on the electro-generation of H2O2 and the abatement of organic pollutants in water by electro-Fenton process. The effect of air pressure, current density, mixing and nature of the organic pollutant was evaluated. [1] E. Brillas, I. Sirés, M.A. Oturan, Chem. Rev., 109 (2009) 6570-6631. [2] C.A. Martínez-Huitle, M.A. Rodrigo, I. Sirés, O. Scialdone, Chem. Rev. 115 (2015) 13362–13407. [3] M. Panizza, G. Cerisola, Chem. Rev. 109 (2009) 6541–6569. [4] I. Sirés, E. Brillas, M.A. Oturan, M.A. Rodrigo, M. Panizza, Environ. Sci. Pollut. Res. 21 (2014) 8336–8367. [5] C.A. Martínez-Huitle, S. Ferro, Chem. Soc. Rev. 35 (2006) 1324–1340. [6] B.P.P. Chaplin, Environ. Sci. Process. Impacts. 16 (2014) 1182–1203. [7] O. Scialdone, A. Galia, C. Gattuso, S. Sabatino, B. Schiavo, Electrochim. Acta, 182 (2015) 775-780. [8] J.F. Pérez, A. Galia, M.A. Rodrigo, J. Llanos, S. Sabatino, C. Sáez, B. Schiavo, O. Scialdone, Electrochim. Acta, 248 (2017) 169-177. [9] A.H. Ltaïef, S. Sabatino, F. Proietto, A. Galia, O. Scialdone, O. 2018, Chemosphere, 202, 111-118