In this thesis, I have used a combination of computational models such as mean field and spikingnetwork simulations to study various sub-circuits of basal ganglia. I first studied the striatum(chapter 2), which is the input nucleus of basal ganglia. The two types of Medium SpinyNeurons (MSNs), D1 and D2-MSNs, together constitute 98% of the neurons in striatum. Thecomputational models so far have treated striatum as a homogenous unit and D1 and D2 MSNs asinterchangeable subpopulations. This implied that a bias in a Go/No-Go decision is enforced viaexternal agents to the striatum (eg. cortico-striatal weights), thereby assigning it a passive role.New data shows that there is an inherent asymmetry in striatal circuits. In this work, I showedthat striatum due to its asymmetric connectivity acts as a decision transition threshold devicefor the incoming cortical input. This has significant implications on the function of striatum asan active participant in influencing the bias towards a Go/No-Go decision. The striatal decisiontransition threshold also gives mechanistic explanations for phenomena such as L-Dopa InducedDyskinesia (LID), DBS-induced impulsivity, etc. In chapter 3, I extend the mean field model toinclude all the nuclei of basal ganglia to specifically study the role of two new subpopulationsfound in GPe (Globus Pallidus Externa). Recent work shows that GPe, also earlier consideredto be a homogenous nucleus, has at least two subpopulations which are dichotomous in theiractivity with respect to the cortical Slow Wave (SWA) and beta activity. Since the data for thesesubpopulations are missing, a parameter search was performed for effective connectivities usingGenetic Algorithms (GA) to fit the available experimental data. One major result of this studyis that there are various parameter combinations that meet the criteria and hence the presenceof functional homologs of the basal ganglia network for both pathological (PD) and healthynetworks is a possibility. Classifying all these homologous networks into clusters using somehigh level features of PD shows a large variance, hinting at the variance observed among the PDpatients as well as their response to the therapeutic measures. In chapter 4, I collaborated on aproject to model the role of STN and GPe burstiness for pathological beta oscillations as seenduring PD. During PD, the burstiness in the firing patterns of GPe and STN neurons are shownto increase. We found that in the baseline state, without any bursty neurons in GPe and STN,the GPe-STN network can transition to an oscillatory state through modulating the firing ratesof STN and GPe neurons. Whereas when GPe neurons are systematically replaced by burstyneurons, we found that increase in GPe burstiness enforces oscillations. An optimal % of burstyneurons in STN destroys oscillations in the GPe-STN network. Hence burstiness in STN mayserve as a compensatory mechanism to destroy oscillations. We also propose that bursting inGPe-STN could serve as a mechanism to initiate and kill oscillations on short time scales, asseen in the healthy state. The GPe-STN network however loses the ability to kill oscillations inthe pathological state. / <p>QC 20160509</p>

Online social networks have quickly become the most popular destination on the World Wide Web. These networks are still a fairly new form of online human interaction and have gained wide popularity only recently within the past three to four years. Few models or descriptions of the dynamics of these systems exist. This is largely due to the difficulty in gaining access to the data from these networks which is often viewed as very valuable. In these networks, members maintain list of friends with which they share content with by first uploading it to the social network service provider. The content is then distributed to members by the service provider who generates a feed for each member containing the content shared by all of the member's friends aggregated together. Direct access to dynamic linkage data for these large networks is especially difficult without a special relationship with the service provider. This makes it difficult for researchers to explore and better understand how humans interface with these systems. This dissertation examines an event driven sampling approach to acquire both dynamics link event data and blog content from the site known as LiveJournal. LiveJournal is one of the oldest online social networking sites whose features are very similar to sites such as Facebook and Myspace yet smaller in scale as to be practical for a research setting. The event driven sampling methodology and analysis of the resulting network model provide insights for other researchers interested in acquiring social network dynamics from LiveJournal or insight into what might be expected if an event driven sampling approach was applied to other online social networks. A detailed analysis of both the static structure and network dynamics of the resulting network model was performed. The analysis helped motivated work on a model of link prediction using both topological and content-based metrics. The relationship between topological and content-based metrics was explored. Factored into the link prediction analysis is the open nature of the social network data where new members are constantly joining and current members are leaving. The data used for the analysis spanned approximately two years.

The study of social networks has become well established in social science. As part of this development, the past several decades have seen an increasing interest in adolescent social relations. Some of the relevant research has focused on explaining similarity patterns in friendship with respect to social categories and have found homophily (the tendency to select similar friends) to be an important factor, or mechanism, influencing friendships. Although the study of social networks has also documented the importance of several other factors for the formation/maintenance of friendships, it has paid little attention to how different factors might interact. Surprisingly little attention has also been paid to how culturally constructed desires and beliefs might influence friend selection. Focusing on social categories relating to immigration background and religiosity, this research examines how homophily interacts with, or is affected by, a school’s classroom organization, and whether students’ beliefs and desires influence the formation and maintenance of friendships. Specifically, the four studies that constitute the second part of this work examine (1) whether native/immigrant background homophily varies depending on whether ties are formed/maintained within or across classroom boundaries, (2) whether adolescents tend to select friends with similar preferences for cultural diversity, and whether reporting a stronger preference for cultural diversity is associated with i) having more friends in school and ii) being more inclined to select dissimilar friends with respect to parents’ birth region, (3) whether adolescents tend to select similar friends in terms of religiosity (defined as the importance attributed to religion), and whether adolescents are influenced by the religiosity of their friends, and finally (4) whether selection of friends with similar beliefs brings with it similarity among friends in terms of behaviors such as alcohol consumption and cigarette smoking. All four studies are based on three observations of the complete friendship network of a cohort of adolescents during the first year in upper secondary education (N=115) and statistical models for social network analysis, so-called stochastic actor-oriented models. The results suggest adolescents’ inclination to select similar friends in terms of social categories varies with a school’s classroom structure and (for a smaller number of students) diversity preferences. Diversity preferences are also found to play a role in friend selection processes in other ways. In addition, so is religiously. Friend selection based on similarity in religiosity is found to lead to similarity among friends with respect to drinking behaviors. These findings suggest that considering the interplay between different tie formation mechanisms as well as individual desires and beliefs can be important for better understanding the evolution of social networks. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Submitted. Paper 2: Submitted. Paper 3: Submitted. Paper 4: Submitted.</p><p> </p>

In the past thirty years, the IMP Group's Interaction and Network Approach has gained its increasing popularity in researching economic behaviours among resource-dependent business actors through relational linkages (Håkansson et al., 2004; Turnbull et al., 1996). Within network research, understanding the dynamics in business networks, in which interfirm relationships are regarded as crucial constituents, has been of particular interest (Johnston et al., 2006; Möller and Halinen, 1999). Moreover, technology has been identified as an important component driving the evolution of a business network, where technological change may bring about positive and negative effects on the relationships embedded in this network, and consequently, results in network dynamics (Afuah, 2000; Christensen, 1997; Håkansson and Waluszewski, 2002b; Lundgren, 1995). A perspective of resource interaction (e.g. Håkansson et al., 2009) suggests that technological change needs to be treated as a process rather than a critical event. However the nature of this process as well as how it impacts on the configuration of a technology-based business net and on dynamics of relationships constituting this net remains under-examined.Based on qualitative research methods, a longitudinal single-case study is chosen to conduct an empirical investigation in the optical recording media industry, in order to address the above research problems. To facilitate the data collection, a focal net perspective and an input-process-output model are employed. The focal net under study is characterised as a value-creating and technology-bundled business net. A total of 72 interviews were carried out in three stages and with the focal actor, its customers, suppliers and a complementor. The empirical data allows the research to reconstruct the evolution of the focal business net, which covers a time-span of more than 10 years from 1998 to 2008, and in which major technological change has taken place three times, from CD-R to DVD-/+R, DVD Double Layer and HD/Blu-ray technologies. In the development of the optical recording technology, the focal net has experienced four net reconfigurations in which radical changes of relationships as well as disturbance in resource interaction are observed. Based on the case study result, empirical observations are offered and new insights into the process of the arrival of technological change and net reconfiguration and relationship dynamics affected by this technological arrival are developed. Moreover, theoretical contribution, managerial implications, limitations and future research directions are provided.

Dynamics of social processes in populations, such as the spread of emotions, influence, language, mass movements, and warfare (often referred to individually and collectively as contagions), are increasingly studied because of their social, political, and economic impacts. Discrete dynamical systems (discrete in time and discrete in agent states) are often used to quantify contagion propagation in populations that are cast as graphs, where vertices represent agents and edges represent agent interactions. We refer to such formulations as graph dynamical systems. For social applications, threshold models are used extensively for agent state transition rules (i.e., for vertex functions). In its simplest form, each agent can be in one of two states (state 0 (1) means that an agent does not (does) possess a contagion), and an agent contracts a contagion if at least a threshold number of its distance-1 neighbors already possess it. The transition to state 0 is not permitted. In this study, we extend threshold models in three ways. First, we allow transitions to states 0 and 1, and we study the long-term dynamics of these bithreshold systems, wherein there are two distinct thresholds for each vertex; one governing each of the transitions to states 0 and 1. Second, we extend the model from a binary vertex state set to an arbitrary number r of states, and allow transitions between every pair of states. Third, we analyze a recent hierarchical model from the literature where inputs to vertex functions take into account subgraphs induced on the distance-1 neighbors of a vertex. We state, prove, and analyze conditions characterizing long-term dynamics of all of these models. / Master of Science

No description available.

Evidence of networks in the resting-brain reflecting the spontaneous brain activity is perhaps the most significant discovery to understand intrinsic brain functionality. Moreover, subsequent detection of dynamics in these networks can be milestone in differentiating the normal and disordered brain functions. However, capturing the correct dynamics is a challenging task since no ground truths' are present for comparison of the results. The change points of these networks can be different for different subjects even during normal brain functions. Even for the same subject and session, dynamics can be different at the start and end of the session based on the fatigue level of the subject scanned. Despite the absence of ground truths, studies have analyzed these dynamics using the existing methods and some of them have developed new algorithms too. One of the most commonly used method for this purpose is sliding window correlation. However, the result of the sliding window correlation is dependent on many parameters and without the ground truth there is no way of validating the results. In addition, most of the new algorithms are complicated, computationally expensive, and/or focus on just one aspect on these dynamics. This study applies the algorithms and concepts from signal processing, image processing, video processing, information theory, and machine learning to analyze the results of the sliding window correlation and develops a novel algorithm to detect change points of these networks adaptively. The findings in this study are divided into three parts: 1) Analyzing the extent of variability in well-defined networks of rodents and humans with sliding window correlation applying concepts from information theory and machine learning domains. 2) Analyzing the performance of sliding window correlation using simulated networks as ground truths for best parameters’ selection, and exploring its dependence on multiple frequency components of the correlating signals by processing the signals in time and Fourier domains. 3) Development of a novel algorithm based on image similarity measures from image and video processing that maybe employed to identify change points of these networks adaptively.

Many processes and systems in nature and society can be characterized as large numbers of discrete elements that are (usually non-uniformly) interrelated. These networks were long thought to be random, but in the late 1990s, Barabási and Albert found that an underlying structure did in fact exist in many natural and technological networks that are now referred to as scale free. Since then, researchers have gained a much deeper understanding of this particular form of complexity, largely by combining graph theory, statistical physics, and advances in computing technology. This dissertation focuses on out-of-equilibrium dynamic processes as they unfold on these complex networks. Diffusion in networks of non-interacting nodes is shown to be temporally complex, while equilibrium is represented by a stable state with Poissonian fluctuations. Scale free networks achieve equilibrium very quickly compared to regular networks, and the most efficient are those with the lowest inverse power law exponent. Temporally complex diffusion also occurs in networks with interacting nodes under a cooperative decision-making model. At a critical value of the cooperation parameter, the most efficient scale free network achieves consensus almost as quickly as the equivalent all-to-all network. This finding suggests that the ubiquity of scale free networks in nature is due to Zipf's principle of least effort. It also suggests that an efficient scale free network structure may be optimal for real networks that require high connectivity but are hampered by high link costs.

The gene regulatory network (GRN) controls the expression of genes providing phenotypic traits in living organisms. In particular, transcriptional regulation is essential to life, as it governs all levels of gene products that enable cell survival and numerous cellular functions. However, there is still poor understanding of how shifts in gene regulation alter the underlying evolutionary dynamics and consequently generate evolutionary innovations. By employing Wagner's GRN model, this dissertation investigates how the interplay of simple evolutionary forces (mutation and recombination) with natural selection acting on gene regulatory dynamics can generate major evolutionary innovations. In this dissertation, firstly, I review all currently available research papers using Wagner's GRN model, which is also employed as the computational model used extensively in the remaining chapters. I then describe how Wagner's GRN model and its variants are implemented. Finally, network properties such as stability, robustness and path length in initial populations are investigated. In the first study, I explore the characteristics of compensatory mutation in the context of genetic networks. Specifically, I find that 1) compensatory mutations are relatively insensitive to the size and connectivity of the network, 2) compensatory mutations are more likely to occur in genes at or adjacent to the site of a previous deleterious mutation and 3) compensatory mutations are more likely to be driven by mutations with a relatively large regulatory impact. In the second study, I further investigate the evolutionary consequences of the properties of compensatory mutation discovered previously. Specifically, I find that 1) compensatory mutations can occur regardless of patterns of selection, 2) networks with compensatory mutations exhibit proportionately higher robustness when compensatory mutations interact closely with deleterious mutations or have large effects on gene regulation, and 3) regulatory complexity can arise as a consequence of the propensity for co-localised and large-effect compensatory mutations. In the third study, I provide a mechanistic understanding of how recombination benefits sexual lineages. Specifically, I find that 1) recombination together with selection for developmental stability can drive populations towards the optimum, 2) recombination does not frequently disrupt well-adapted lineages as conventionally expected, and 3) recombination facilitates finding good genetic combinations which are robust to disruption, although it also rapidly purges weaker configurations. In the final study, I show that the selection pressure acting on rewiring gene regulation is critical to increasing benefits for sexual lineages whilst mitigating costs of sex and recombination. Specifically, I find that 1) strong selection strength can greatly benefit low-fitness sexual lineages, especially at the early stage, 2) recombination is initially costly, but it can rapidly evolve to compensate for costs of sex and recombination, and 3) sexual lineages with low levels of sex and recombination can outcompete strictly asexual populations under higher selection pressure and lower mutation rates. The results presented for all of the studies are important for mechanistically understanding evolutionary innovations through altering transcriptional regulatory dynamics. These innovations include 1) facilitating alternative pathway evolution, 2) driving regulatory complexity, 3) benefiting sexual reproduction, and 4) resisting invasion against asexual lineages.

What changes in our brain when we learn? This is perhaps the most intriguing question of science in this century. In an attempt to learn more about the inner workings of neural circuitry, I studied cultured 2-dimensional networks of neurons on multi-electrode arrays (MEAs). MEAs are ideal tools for studying long-term neural ensemble activity because many individual cells can be studied continuously for months, through electrical stimulation and recording. One of the most prominent patterns of activity observed in these cultures is network-wide spontaneous bursting, during which most of the active electrodes in the culture show elevated firing rates. We view the persistence of spontaneous bursting in vitro as a sign of arrested development due to deafferentation. Substituting distributed electrical stimulation for afferent input transformed the activity in dissociated cultures from bursting to more dispersed spiking, reminiscent of activity in the adult brain. Burst suppression reduced the variability in neural responses making it easier to induce and detect functional plasticity caused by tetanic stimulation. This suggests that spontaneous bursts interfere with the effects of external stimulation and that a burst-free environment leads to more stable connections and predictable effects of tetanization. Moreover, our culture models continuously receive input stimulation in the form of background electrical stimulation, and so better resemble the intact brain than isolated (non-continuously stimulated) cultures. The proportion of GABAergic neurons in the cultures was significantly increased (p<1e-2, paired t-test) after burst-quieting for 2 days, suggesting that burst suppression operated through the homeostatic control of inhibitory neurotransmitter levels. We also studied the role of spontaneous bursts as potential carriers of information in the network by clustering these spatiotemporally diverse bursts. Spontaneous burst clusters were stable over hours and tetanic stimulation significantly reorganized the distribution of the clusters. In summary, this body of work explores the rules of network-level functional plasticity and provides the input (electrical stimulation) output (spatiotemporal patterns) mappings for behavioral studies in embodied hybrid systems. The results of this study may also have clinical implications in the development of sensory prostheses and treatment of diseases of aberrant network activity such as epilepsy.