Mathematical models of cognitive processes

The research activity carried out during the PhD course was focused on the development of mathematical models of some cognitive processes and their validation by means of data present in literature, with a double aim: i) to achieve a better interpretation and explanation of the great amount of data obtained on these processes from different methodologies (electrophysiological recordings on animals, neuropsychological, psychophysical and neuroimaging studies in humans), ii) to exploit model predictions and results to guide future research and experiments. In particular, the research activity has been focused on two different projects: 1) the first one concerns the development of neural oscillators networks, in order to investigate the mechanisms of synchronization of the neural oscillatory activity during cognitive processes, such as object recognition, memory, language, attention; 2) the second one concerns the mathematical modelling of multisensory integration processes (e.g. visual-acoustic), which occur in several cortical and subcortical regions (in particular in a subcortical structure named Superior Colliculus (SC)), and which are fundamental for orienting motor and attentive responses to external world stimuli. This activity has been realized in collaboration with the Center for Studies and Researches in Cognitive Neuroscience of the University of Bologna (in Cesena) and the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA). PART 1. Objects representation in a number of cognitive functions, like perception and recognition, foresees distribute processes in different cortical areas. One of the main neurophysiological question concerns how the correlation between these disparate areas is realized, in order to succeed in grouping together the characteristics of the same object (binding problem) and in maintaining segregated the properties belonging to different objects simultaneously present (segmentation problem). Different theories have been proposed to address these questions (Barlow, 1972). One of the most influential theory is the so called “assembly coding”, postulated by Singer (2003), according to which 1) an object is well described by a few fundamental properties, processing in different and distributed cortical areas; 2) the recognition of the object would be realized by means of the simultaneously activation of the cortical areas representing its different features; 3) groups of properties belonging to different objects would be kept separated in the time domain. In Chapter 1.1 and in Chapter 1.2 we present two neural network models for object recognition, based on the “assembly coding” hypothesis. These models are networks of Wilson-Cowan oscillators which exploit: i) two high-level “Gestalt Rules” (the similarity and previous knowledge rules), to realize the functional link between elements of different cortical areas representing properties of the same object (binding problem); 2) the synchronization of the neural oscillatory activity in the γ-band (30-100Hz), to segregate in time the representations of different objects simultaneously present (segmentation problem). These models are able to recognize and reconstruct multiple simultaneous external objects, even in difficult case (some wrong or lacking features, shared features, superimposed noise). In Chapter 1.3 the previous models are extended to realize a semantic memory, in which sensory-motor representations of objects are linked with words. To this aim, the network, previously developed, devoted to the representation of objects as a collection of sensory-motor features, is reciprocally linked with a second network devoted to the representation of words (lexical network) Synapses linking the two networks are trained via a time-dependent Hebbian rule, during a training period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from linguistic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with some shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits). PART 2. The ability of the brain to integrate information from different sensory channels is fundamental to perception of the external world (Stein et al, 1993). It is well documented that a number of extraprimary areas have neurons capable of such a task; one of the best known of these is the superior colliculus (SC). This midbrain structure receives auditory, visual and somatosensory inputs from different subcortical and cortical areas, and is involved in the control of orientation to external events (Wallace et al, 1993). SC neurons respond to each of these sensory inputs separately, but is also capable of integrating them (Stein et al, 1993) so that the response to the combined multisensory stimuli is greater than that to the individual component stimuli (enhancement). This enhancement is proportionately greater if the modality-specific paired stimuli are weaker (the principle of inverse effectiveness). Several studies have shown that the capability of SC neurons to engage in multisensory integration requires inputs from cortex; primarily the anterior ectosylvian sulcus (AES), but also the rostral lateral suprasylvian sulcus (rLS). If these cortical inputs are deactivated the response of SC neurons to cross-modal stimulation is no different from that evoked by the most effective of its individual component stimuli (Jiang et al 2001). This phenomenon can be better understood through mathematical models. The use of mathematical models and neural networks can place the mass of data that has been accumulated about this phenomenon and its underlying circuitry into a coherent theoretical structure. In Chapter 2.1 a simple neural network model of this structure is presented; this model is able to reproduce a large number of SC behaviours like multisensory enhancement, multisensory and unisensory depression, inverse effectiveness. In Chapter 2.2 this model was improved by incorporating more neurophysiological knowledge about the neural circuitry underlying SC multisensory integration, in order to suggest possible physiological mechanisms through which it is effected. This endeavour was realized in collaboration with Professor B.E. Stein and Doctor B. Rowland during the 6 months-period spent at the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA), within the Marco Polo Project. The model includes four distinct unisensory areas that are devoted to a topological representation of external stimuli. Two of them represent subregions of the AES (i.e., FAES, an auditory area, and AEV, a visual area) and send descending inputs to the ipsilateral SC; the other two represent subcortical areas (one auditory and one visual) projecting ascending inputs to the same SC. Different competitive mechanisms, realized by means of population of interneurons, are used in the model to reproduce the different behaviour of SC neurons in conditions of cortical activation and deactivation. The model, with a single set of parameters, is able to mimic the behaviour of SC multisensory neurons in response to very different stimulus conditions (multisensory enhancement, inverse effectiveness, within- and cross-modal suppression of spatially disparate stimuli), with cortex functional and cortex deactivated, and with a particular type of membrane receptors (NMDA receptors) active or inhibited. All these results agree with the data reported in Jiang et al. (2001) and in Binns and Salt (1996). The model suggests that non-linearities in neural responses and synaptic (excitatory and inhibitory) connections can explain the fundamental aspects of multisensory integration, and provides a biologically plausible hypothesis about the underlying circuitry.

Asset management of wastewater networks

Asset Management (AM) is a set of procedures operable at the strategic-tacticaloperational level, for the management of the physical asset’s performance, associated risks and costs within its whole life-cycle. AM combines the engineering, managerial and informatics points of view. In addition to internal drivers, AM is driven by the demands of customers (social pull) and regulators (environmental mandates and economic considerations). AM can follow either a top-down or a bottom-up approach. Considering rehabilitation planning at the bottom-up level, the main issue would be to rehabilitate the right pipe at the right time with the right technique. Finding the right pipe may be possible and practicable, but determining the timeliness of the rehabilitation and the choice of the techniques adopted to rehabilitate is a bit abstruse. It is a truism that rehabilitating an asset too early is unwise, just as doing it late may have entailed extra expenses en route, in addition to the cost of the exercise of rehabilitation per se. One is confronted with a typical ‘Hamlet-isque dilemma’ – ‘to repair or not to repair’; or put in another way, ‘to replace or not to replace’. The decision in this case is governed by three factors, not necessarily interrelated – quality of customer service, costs and budget in the life cycle of the asset in question. The goal of replacement planning is to find the juncture in the asset’s life cycle where the cost of replacement is balanced by the rising maintenance costs and the declining level of service. System maintenance aims at improving performance and maintaining the asset in good working condition for as long as possible. Effective planning is used to target maintenance activities to meet these goals and minimize costly exigencies. The main objective of this dissertation is to develop a process-model for asset replacement planning. The aim of the model is to determine the optimal pipe replacement year by comparing, temporally, the annual operating and maintenance costs of the existing asset and the annuity of the investment in a new equivalent pipe, at the best market price. It is proposed that risk cost provide an appropriate framework to decide the balance between investment for replacing or operational expenditures for maintaining an asset. The model describes a practical approach to estimate when an asset should be replaced. A comprehensive list of criteria to be considered is outlined, the main criteria being a visà- vis between maintenance and replacement expenditures. The costs to maintain the assets should be described by a cost function related to the asset type, the risks to the safety of people and property owing to declining condition of asset, and the predicted frequency of failures. The cost functions reflect the condition of the existing asset at the time the decision to maintain or replace is taken: age, level of deterioration, risk of failure. The process model is applied in the wastewater network of Oslo, the capital city of Norway, and uses available real-world information to forecast life-cycle costs of maintenance and rehabilitation strategies and support infrastructure management decisions. The case study provides an insight into the various definitions of ‘asset lifetime’ – service life, economic life and physical life. The results recommend that one common value for lifetime should not be applied to the all the pipelines in the stock for investment planning in the long-term period; rather it would be wiser to define different values for different cohorts of pipelines to reduce the uncertainties associated with generalisations for simplification. It is envisaged that more criteria the municipality is able to include, to estimate maintenance costs for the existing assets, the more precise will the estimation of the expected service life be. The ability to include social costs enables to compute the asset life, not only based on its physical characterisation, but also on the sensitivity of network areas to social impact of failures. The type of economic analysis is very sensitive to model parameters that are difficult to determine accurately. The main value of this approach is the effort to demonstrate that it is possible to include, in decision-making, factors as the cost of the risk associated with a decline in level of performance, the level of this deterioration and the asset’s depreciation rate, without looking at age as the sole criterion for making decisions regarding replacements.

Creazione e sviluppo di un sistema di controllo in tempo reale per un sistema fuel cell

The control of a proton exchange membrane fuel cell system (PEM FC) for domestic heat and power supply requires extensive control measures to handle the complicated process. Highly dynamic and non linear behavior, increase drastically the difficulties to find the optimal design and control strategies. The objective is to design, implement and commission a controller for the entire fuel cell system. The fuel cell process and the control system are engineered simultaneously; therefore there is no access to the process hardware during the control system development. Therefore the method of choice was a model based design approach, following the rapid control prototyping (RCP) methodology. The fuel cell system is simulated using a fuel cell library which allowed thermodynamic calculations. In the course of the development the process model is continuously adapted to the real system. The controller application is designed and developed in parallel and thereby tested and verified against the process model. Furthermore, after the commissioning of the real system, the process model can be also better identified and parameterized utilizing measurement data to perform optimization procedures. The process model and the controller application are implemented in Simulink using Mathworks` Real Time Workshop (RTW) and the xPC development suite for MiL (model-in-theloop) and HiL (hardware-in-the-loop) testing. It is possible to completely develop, verify and validate the controller application without depending on the real fuel cell system, which is not available for testing during the development process. The fuel cell system can be immediately taken into operation after connecting the controller to the process.

Physiological studies to optimize algal biomass production in phytoremediation processes

Nowadays microalgae are studied, and a number of species already mass-cultivated, for their application in many fields: food and feed, chemicals, pharmaceutical, phytoremediation and renewable energy. Phytoremediation, in particular, can become a valid integrated process in many algae biomass production systems. This thesis is focused on the physiological and biochemical effects of different environmental factors, mainly macronutrients, lights and temperature on microalgae. Microalgal species have been selected on the basis of their potential in biotechnologies, and nitrogen occurs in all chapters due to its importance in physiological and applicative fields. There are 5 chapters, ready or in preparation to be submitted, with different specific matters: (i) to measure the kinetic parameters and the nutrient removal efficiencies for a selected and local strain of microalgae; (ii) to study the biochemical pathways of the microalga D. communis in presence of nitrate and ammonium; (iii) to improve the growth and the removal efficiency of a specific green microalga in mixotrophic conditions; (iv) to optimize the productivity of some microalgae with low growth-rate conditions through phytohormones and other biostimulants; and (v) to apply the phyto-removal of ammonium in an effluent from anaerobic digestion. From the results it is possible to understand how a physiological point of view is necessary to provide and optimize already existing biotechnologies and applications with microalgae.