THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.

Trust and Social Networks in a Changing Europe.

This project set out to investigate the effects of the recent massive social transitions in Eastern Europe on the everyday social lives of the inhabitants of three very different nations: Georgia, Russia and Hungary. It focused in particular on the availability and nature of the support networks available to three different segments of each of the societies (manual workers, students and entrepreneurs) and the impact of network participation on psychological and physical well-being. The group set four specific questions to investigate: the part played by individual psychological beliefs in the formation and maintenance of social networks and the consequent formation of trusting relations; the implication of the size and quality of these networks for mental health; the nature of the social groups inhabited by the respondents and the implication of their work schedule and daily routines on the maintenance of a social and family life; and an analysis of how cultures vary in their social networks and intimacy. Three different methods were used to examine social support and its implications: structured questionnaires, semi-structured short interviews and a media analysis of newspaper materials. The questionnaires were administered to 150 participants in each country, equally divided between students studying full time, manual workers employed in factories, and business people (small kiosk owners, whose work and life style differs considerably from that of the manual workers). The questionnaires investigated various predictors of social support including the locus of control, relationship beliefs, individualism-collectivism and egalitarianism, demographic variables (age, gender and occupation), social support, both in general and in relation to significant events that have occurred since the transition from communism. Those with an internal locus of control were more likely to report a higher level of social support, as were collectivists, while age too was a significant predictor, with younger respondents enjoying higher levels of support, regardless of the measures of support employed. Respondents across the cultures referred to a decline of social support and the group also found a direct correlation between social support and mental health outcomes. All 450 respondents were interviewed on their general responses to changes in their lives since the fall of communism and the effects of their work lives on their social lives and the home environment. The interviews revealed considerable variations in the way in which work-life offered opportunities for a broader social life and also provided a hindrance to the development of fulfilling relationships. Many of the work experiences discussed were culture specific, with work having a particularly negative impact on the social life of Russian entrepreneurs but being seen much more positively in Georgia. This may reflect the nature of support offered in a society as overall support levels were lowest in Russia, meaning that social support may be of particular importance there. The way in cultural values and norms about personal relationships are transmitted in a culture is a critical issue for social psychologists and the group examined newspaper articles in those newspapers read by the respondents in each of the three countries. These revealed a number of different themes. The concept of a divided society and its implications for personal relationships was clearest in Russian and Hungary, where widely-read newspapers dwelt on the contrast between "new Russians/Hungarians" and the older, poorer ones and extended considerable sympathy to those suffering from neglect in institutions. Magyar Nemzet, a paper widely read by Hungarian students reflects the generally more pessimistic tone about personal relationships in Russia and Hungary and gave a particularly detailed analysis of the implications this holds for human relations in a modern society. In Georgia, however, the tone of the newspapers is more positive, stressing greater social cohesion. Part of this cohesion is framed in the context of religion, with the church appealing to a broader egalitarianism, whereas in less egalitarian Hungary appeals by the Church are centred more on the nuclear family and its need for expansion in both size and influence. The division between the sexes was another prominent issue in Hungary and Russia, while the theme of generational conflict also emerged in Hungarian and Georgian papers, although with some understanding of "young people today". The team's original expectation that the different newspapers read by the different groups of respondents would present differing images of personal relationships was not fulfilled, as despite variations in style, they found little clear "ideological targeting" of any particular readership. They conclude that the vast majority of respondents recognised that the social transition from communism has had a significant impact on the well-being of social relationships and that this is a pertinent issue for all segments of society. While the group see the data collected as a source to be worked on for some time in the future, their initial impressions include the following. Social support is clearly an important concern across all three countries. All respondents (including the students) lament the time taken up by their heavy work schedules and value their social networks and family ties in particular. The level of social support differs across the countries investigated, with Georgian apparently enjoying significantly higher levels of social support. The analysis produced an image of a relatively cohesive and egalitarian society in which even the group most often seen as distant from the general population, business people, is supported by a strong social network. In contrast, the support networks available to the Russian respondents seem particularly weak and reflect a general sense of division and alienation within the culture as a whole. The implications of low levels of social support may vary across countries. While Russians reported the lowest level of mental health problems, the link between social support and mental health may be strongest in that country. In contrast, in Hungary it is the link between fatalism and mental health problems which is particularly strong, while in Georgia the strongest correlation was between mental health and marital quality, emphasising the significance of the marital relationship in that country.

Understanding "Just Enough" Computer Users: Motivation Style and Proficiency

Among daily computer users who are proficient, some are flexible at accomplishing unfamiliar tasks on their own and others have difficulty. Software designers and evaluators involved with Human Computer Interaction (HCI) should account for any group of proficient daily users that are shown to stumble over unfamiliar tasks. We define "Just Enough" (JE) users as proficient daily computer users with predominantly extrinsic motivation style who know just enough to get what they want or need from the computer. We hypothesize that JE users have difficulty with unfamiliar computer tasks and skill transfer, whereas intrinsically motivated daily users accomplish unfamiliar tasks readily. Intrinsic motivation can be characterized by interest, enjoyment, and choice and extrinsic motivation is externally regulated. In our study we identified users by motivation style and then did ethnographic observations. Our results confirm that JE users do have difficulty accomplishing unfamiliar tasks on their own but had fewer problems with near skill transfer. In contrast, intrinsically motivated users had no trouble with unfamiliar tasks nor with near skill transfer. This supports our assertion that JE users know enough to get routine tasks done and can transfer that knowledge, but become unproductive when faced with unfamiliar tasks. This study combines quantitative and qualitative methods. We identified 66 daily users by motivation style using an inventory adapted from Deci and Ryan (Ryan and Deci 2000) and from Guay, Vallerand, and Blanchard (Guay et al. 2000). We used qualitative ethnographic methods with a think aloud protocol to observe nine extrinsic users and seven intrinsic users. Observation sessions had three customized phases where the researcher directed the participant to: 1) confirm the participant's proficiency; 2) test the participant accomplishing unfamiliar tasks; and 3) test transfer of existing skills to unfamiliar software.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.

Modeling Calcium Concentration and Biochemical Reactions

Almost all regions of the brain receive one or more neuromodulatory inputs, and disrupting these inputs produces deficits in neuronal function. Neuromodulators act through intracellular second messenger pathways to influence the electrical properties of neurons, integration of synaptic inputs, spatio-temporal firing dynamics of neuronal networks, and, ultimately, systems behavior. Second messengers pathways consist of series of bimolecular reactions, enzymatic reactions, and diffusion. Calcium is the second messenger molecule with the most effectors, and thus is highly regulated by buffers, pumps and intracellular stores. Computational modeling provides an innovative, yet practical method to evaluate the spatial extent, time course and interaction among second messenger pathways, and the interaction of second messengers with neuron electrical properties. These processes occur both in compartments where the number of molecules are large enough to describe reactions deterministically (e.g. cell body), and in compartments where the number of molecules is small enough that reactions occur stochastically (e.g. spines). – In this tutorial, I explain how to develop models of second messenger pathways and calcium dynamics. The first part of the tutorial explains the equations used to model bimolecular reactions, enzyme reactions, calcium release channels, calcium pumps and diffusion. The second part explains some of the GENESIS, Kinetikit and Chemesis objects that implement the appropriate equations. In depth explanation of calcium and second messenger models is provided by reviewing code, both in XPP, Chemesis and Kinetikit, that implements simple models of calcium dynamics and second messenger cascades.

Scoping Electronic Communication Privacy Rules: Data, Services and Values

We use electronic communication networks for more than simply traditional telecommunications: we access the news, buy goods online, file our taxes, contribute to public debate, and more. As a result, a wider array of privacy interests is implicated for users of electronic communications networks and services. . This development calls into question the scope of electronic communications privacy rules. This paper analyses the scope of these rules, taking into account the rationale and the historic background of the European electronic communications privacy framework. We develop a framework for analysing the scope of electronic communications privacy rules using three approaches: (i) a service-centric approach, (ii) a data-centric approach, and (iii) a value-centric approach. We discuss the strengths and weaknesses of each approach. The current e-Privacy Directive contains a complex blend of the three approaches, which does not seem to be based on a thorough analysis of their strengths and weaknesses. The upcoming review of the directive announced by the European Commission provides an opportunity to improve the scoping of the rules.

Performance Analysis and Comparison between Legacy-PSM and U-APSD

This paper evaluates the performance of the most popular power saving mechanisms defined in the IEEE 802.11 standard, namely the Power Save Mode (Legacy-PSM) and the Unscheduled Automatic Power Save Delivery (U-APSD). The assessment comprises a detailed study concerning energy efficiency and capability to guarantee the required Quality of Service (QoS) for a certain application. The results, obtained in the OMNeT++ simulator, showed that U-APSD is more energy efficient than Legacy-PSM without compromising the end-to- end delay. Both U-APSD and Legacy-PSM revealed capability to guarantee the application QoS requirements in all the studied scenarios. However, unlike U-APSD, when Legacy-PSM is used in the presence of QoS demanding applications, all the stations connected to the network through the same access point will consume noticeable additional energy.

Repeatability analysis of global and local metrics of brain structural networks

Computational network analysis provides new methods to analyze the human connectome. Brain structural networks can be characterized by global and local metrics that recently gave promising insights for diagnosis and further understanding of neurological, psychiatric and neurodegenerative disorders. In order to ensure the validity of results in clinical settings the precision and repeatability of the networks and the associated metrics must be evaluated. In the present study, nineteen healthy subjects underwent two consecutive measurements enabling us to test reproducibility of the brain network and its global and local metrics. As it is known that the network topology depends on the network density, the effects of setting a common density threshold for all networks were also assessed. Results showed good to excellent repeatability for global metrics, while for local metrics it was more variable and some metrics were found to have locally poor repeatability. Moreover, between subjects differences were slightly inflated when the density was not fixed. At the global level, these findings confirm previous results on the validity of global network metrics as clinical biomarkers. However, the new results in our work indicate that the remaining variability at the local level as well as the effect of methodological characteristics on the network topology should be considered in the analysis of brain structural networks and especially in networks comparisons.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.