An Overview on Evaluation of E-Learning/Training Response Time Considering Artificial Neural Networks Modeling

The objective of this piece of research is to interpret and investigate systematically an observed brain functional phenomenon which associated with proceeding of e-learning processes. More specifically, this work addresses an interesting and challenging educational issue concerned with dynamical evaluation of e-learning performance considering convergence (response) time. That's based on an interdisciplinary recent approach named as Artificial Neural Networks (ANNs) modeling. Which incorporate Nero-physiology, educational psychology, cognitive, and learning sciences. Herein, adopted application of neural modeling results in realistic dynamical measurements of e-learners' response time performance parameter. Initially, it considers time evolution of learners' experienced acquired intelligence level during proceeding of learning / training process. In the context of neurobiological details, the state of synaptic connectivity pattern (weight vector) inside e-learner's brain-at any time instant-supposed to be presented as timely varying dependent parameter. The varying modified synaptic state expected to lead to obtain stored experience spontaneously as learner's output (answer). Obviously, obtained responsive learner's output is a resulting action to any arbitrary external input stimulus (question). So, as the initial brain state of synaptic connectivity pattern (vector) considered as pre-intelligence level measured parameter. Actually, obtained e-learner’s answer is compatibly consistent with modified state of internal / stored experienced level of intelligence. In other words, dynamical changes of brain synaptic pattern (weight vector) modify adaptively convergence time of learning processes, so as to reach desired answer. Additionally, introduced research work is motivated by some obtained results for performance evaluation of some neural system models concerned with convergence time of learning process. Moreover, this paper considers interpretation of interrelations among some other interesting results obtained by a set of previously published educational models. The interpretational evaluation and analysis for introduced models results in some applicable studies at educational field as well as medically promising treatment of learning disabilities. Finally, an interesting comparative analogy between performances of ANNs modeling versus Ant Colony System (ACS) optimization presented at the end of this paper

Information driven self-organization of complex robotic behaviors

Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well.Comment: 29 pages, 12 figure

Dynamics of embodied dissociated cortical cultures for the control of hybrid biological robots.

The thesis presents a new paradigm for studying the importance of interactions between an organism and its environment using a combination of biology and technology: embodying cultured cortical neurons via robotics. From this platform, explanations of the emergent neural network properties leading to cognition are sought through detailed electrical observation of neural activity. By growing the networks of neurons and glia over multi-electrode arrays (MEA), which can be used to both stimulate and record the activity of multiple neurons in parallel over months, a long-term real-time 2-way communication with the neural network becomes possible. A better understanding of the processes leading to biological cognition can, in turn, facilitate progress in understanding neural pathologies, designing neural prosthetics, and creating fundamentally different types of artificial cognition. Here, methods were first developed to reliably induce and detect neural plasticity using MEAs. This knowledge was then applied to construct sensory-motor mappings and training algorithms that produced adaptive goal-directed behavior. To paraphrase the results, most any stimulation could induce neural plasticity, while the inclusion of temporal and/or spatial information about neural activity was needed to identify plasticity. Interestingly, the plasticity of action potential propagation in axons was observed. This is a notion counter to the dominant theories of neural plasticity that focus on synaptic efficacies and is suggestive of a vast and novel computational mechanism for learning and memory in the brain. Adaptive goal-directed behavior was achieved by using patterned training stimuli, contingent on behavioral performance, to sculpt the network into behaviorally appropriate functional states: network plasticity was not only induced, but could be customized. Clinically, understanding the relationships between electrical stimulation, neural activity, and the functional expression of neural plasticity could assist neuro-rehabilitation and the design of neuroprosthetics. In a broader context, the networks were also embodied with a robotic drawing machine exhibited in galleries throughout the world. This provided a forum to educate the public and critically discuss neuroscience, robotics, neural interfaces, cybernetics, bio-art, and the ethics of biotechnology.Ph.D.Committee Chair: Steve M. Potter; Committee Member: Eric Schumacher; Committee Member: Robert J. Butera; Committee Member: Stephan P. DeWeerth; Committee Member: Thomas D. DeMars

Repetitive Transcranial Magnetic Stimulation by Theta Burst

Transcranial magnetic stimulation (TMS) is a non-invasive diagnostic and therapeutic technique used to stimulate the brain in several neurological and psychiatric diseases, even though the main bases underlying its action are not fully understood. Theta Burst Stimulation (TBS), a patterned form of repetitive TMS, has been assuming particular importance due to its faster application. Research of TBS effects on some higher cortical functions such as cognition after stimulation of the prefrontal cortex (PFC), or its possible influence in some less studied cortical regions (as the temporal cortex) has been limited and revealed inconsistent results. One of the problems assessing the cognitive TBS after-effects relates to the use of multiple evaluation methods, with different sensitivities. In this matter, the use of neurophysiology studies such as the auditory P300, a cognitive evoked potential, may be of particular importance. To date, studies addressing the association between auditory P300 and TBS are scarce, and some contradictory results were found. The study of other higher cognitive domains such as creativity is even rarer, but it may be relevant given that part of the neural networks involved in creative processing are associated with the PFC. The effect of TMS over the PFC, studying the modulation of functions mediated by the autonomic nervous system has also been reported, but there is still a significant disagreement between the rare studies performed. So far, the extent of the modulatory effects associated with TBS at the sensory level is still poorly known, and research with TBS over the auditory cortex, despite showing some positive results, remains inconclusive, with some reports of sound hypersensitivity after sessions with higher intensity stimulation. It should also be noted that a significant part of the knowledge about the effects of TBS derives from studies in patients, with dysfunctional neuronal networks or hemispheric lesions, which add challenges to the search for scientific evidence in healthy individuals. Given the uncertainties that remain regarding the extent of the neuromodulatory effects of TBS, the primary objective of this thesis focused on increasing the scientific knowledge related to the use of TBS in the healthy brain. Therefore, we intended to study the neurophysiological responses (such as auditory P300), the functional responses (such as auditory thresholds), and the physiological responses (such as cerebral oximetry and blood pressure) associated with the application of TBS in the prefrontal and temporal cortices. All studies used a target population of healthy young adults, with an average age of approximately 23 years, and similar education. TBS was performed accordingly to the 600-pulse paradigm described by Huang et al. (continuous and intermittent). Sham-controlled, double-blind intervention protocols were used, with random distribution by the respective groups. The main objective of the study in chapter III was to evaluate the effect of TBS on the dorsolateral prefrontal cortex (DLPFC) of both cerebral hemispheres in cognitive processing. The objective was to assess if the auditory P300 would be influenced by the stimulation type. Results revealed that the mean P300 peak latency after TBS decreased only after leftward iTBS. A significant delay in P300 latency was originated from both right and left cTBS. Amplitude response did not change significantly. The results covered in chapter IV derived from the use of TBS on the left DLPFC, studying the possibility of a relationship between the post-TBS auditory P300 and the post-TBS neuropsychological tests: Trail Making Test (TMT) and the Stroop Test of Words and Colours. Results revealed that cTBS led to a delay of the P300, also significantly influencing the expected performance on Stroop C and Stroop Interference when compared to the groups submitted to iTBS and sham stimulation. No significant results were found in the TMT tests for any type of TBS stimulation. In Chapter V, we studied the cerebral oximetry using Near Infra-Red Spectroscopy, blood pressure, and heart rate, after applying TBS to the right and left DLPFC. We found a significant reduction in oximetry in the left frontal region after ipsilateral cTBS and a significant decrease in systolic blood pressure after cTBS to the right DLPFC. Chapter VI covered the evaluation of the effects of TBS over the left temporal cortex, specifically studying the auditory thresholds in the ear closest to the coil. Results showed no major side effects after iTBS, cTBS, or sham stimulation. It was also found that iTBS led to lower hearing thresholds, especially when comparing the iTBS and sham groups at 500Hz and between the iTBS and cTBS groups at 4000Hz. Chapter VII addresses a patent concerning the technique and possible use of iTBS as a method to influence creative processing. After iTBS over the right DLPFC, results of an adapted selection of the Torrance Tests of Creative Thinking suggest that divergent thinking, originality and fluency improved significantly compared to the sham group. An integrative analysis of the results shows that TBS seems to effectively influence the underlying cortical neurons and cortico-subcortical networks. The findings thus support the existence of a trans-synaptic effect advocated initially for the classic repetitive TMS, which after the publication of our research can continue to be extended with greater confidence to TBS protocols. Our results also support the most consensual theory about the modulatory effects of the two main forms of TBS – intermittent (excitatory) and continuous (inhibitory) – particularly on the prefrontal and temporal cortices. The effects of TBS seem to be intrinsically correlated with the hemispheric lateralization and this may be related to the specific functions or dominance of each hemisphere and the specific stimulated cortical regions. The combined results of this investigation also seem to suggest that the inhibition induced by cTBS seems more effective when compared to the excitatory effect of iTBS, which seemed stronger in the left hemisphere. After all our research with TBS in more than one cortical region, we can infer that this is a safe technique, with rare and incipient side effects. The encouraging results after using iTBS in the auditory cortex opens new perspectives regarding future implementations of the technique and should be replicated in patients, particularly with mild sensorineural hearing loss, in order to assess whether this stimulation protocol can be a valid therapeutic technique in these cases. We also conclude that the techniques used to study TBS-related effects, as the P300 or the NIRS, can be very useful in the future, as an attempt to identify the effectiveness of the therapeutic use of TBS protocols, possibly allowing to adapt and modify the idealized interventions, leading to a personalized patient intervention. Our findings provide relevant information, necessary to increase the technical and scientific credibility required for achieving a more comprehensive and reliable clinical use of TBS. This is crucial at a time when transcranial magnetic stimulation use as an off-label therapy for numerous neurological and psychiatric diseases grows unregulated, and the patient best interests must be defended.A estimulação magnética transcraniana (EMT) é uma técnica de diagnóstico e terapêutica não invasiva, que tem vindo a evoluir nos últimos 35 anos. A aplicação terapêutica da forma repetitiva da EMT (EMTr), tem vindo a demonstrar a sua utilidade científica e clínica, com aplicação em várias doenças neurológicas e psiquiátricas como a depressão major, a perturbação obsessivo-compulsiva, dor e reabilitação em doentes com acidentes vasculares cerebrais, ainda que as principais bases subjacentes à sua acção não sejam totalmente compreendidas. A EMT baseia-se no princípio da indução magnética e na sua capacidade de induzir correntes elétricas no tecido cortical. Esses campos magnéticos (pulsos) originados por uma bobina adjacente ao couro cabeludo originam um fluxo iónico intracraniano que irá provocar a despolarização da membrana neuronal, desencadeando assim um potencial de ação. Embora a EMT exerça os seus efeitos predominantemente na área cortical adjacente à bobina, os potenciais de ação induzidos espalham-se trans-sinapticamente, originando a propagação da ativação para regiões corticais e subcorticais vizinhas pertencentes à rede neuronal em questão. Parece ocorrer ainda a aparente capacidade de influenciar a função do hemisfério contralateral à estimulação possivelmente por mediação calossal. Os efeitos da EMTr ao nível da modulação da excitabilidade neuronal estão intrinsecamente dependentes das características da estimulação, nomeadamente ao nível da frequência e padronização dos estímulos. A aplicação de frequências inferiores ou iguais a 1 Hz (EMTr de baixa frequência) são associadas à indução de um efeito inibitório neuronal, enquanto que a aplicação de frequências acima de 1 Hz, normalmente acima dos 5 Hz (EMTr de alta frequência), podem induzir um efeito excitatório. Em 2005 surgiu uma forma padronizada de aplicação dos pulsos magnéticos, denominada Theta Burst Stimulation (TBS), na qual grupos de 3 pulsos com alta frequência (bursts de 50Hz) são enviados a cada 200 milissegundos (5 Hz – frequência teta), implicando normalmente a aplicação de 600 pulsos por cada sessão de estimulação. Este é um protocolo que assume particular importância pela sua rápida aplicação, levando menos de 3 minutos a executar, sendo significativamente mais célere do que os protocolos clássicos de EMTr (que podem exceder 30 minutos). Efeitos neuromodulatórios opostos podem ser igualmente induzidos com TBS, sendo que a aplicação ininterrupta da estimulação durante 40 segundos – TBS contínua (cTBS) – parece originar uma diminuição na excitabilidade cortical com uma duração de até 50 minutos pós-estimulação, enquanto que a aplicação de apenas 2 segundos de TBS intervalada por 8 segundos de pausa – TBS intermitente (iTBS) – durante 190 segundos, terá a capacidade de induzir aumento na excitabilidade cortical até cerca de 60 minutos pós-estimulação. Apesar do volume significativo de investigação acumulada na estimulação com EMTr e TBS, demonstrando a sua capacidade modulatória e a sua aplicabilidade na prática clínica, a investigação dos seus efeitos sobre algumas funções corticais superiores como a cognição ou os efeitos da aplicação em algumas regiões corticais menos estudadas como a região temporal tem sido mais limitada (principalmente com a TBS) e apresentado alguns resultados contraditórios. O córtex pré-frontal assume particular importância associado à aplicação da EMTr/TBS dada a extensa rede de conexões com outras regiões corticais (como o córtex motor, o córtex sensitivo, a amígdala, o tálamo e o hipocampo), importantes em doenças como a depressão (desequilíbrio inter-hemisférico pré-frontal verificado por neuroimagem), e ainda pela sua aparente capacidade de influenciar funções autonómicas e cardiovasculares. Meta-análises como a de Lowe et al. 2018, avaliando os efeitos da TBS sobre o córtex pré-frontal, revelam que parece existir um efeito negativo no desempenho das tarefas de função executiva após estimulação com cTBS e um efeito positivo mas em menor grau após estimulação com iTBS. No entanto, o efeito mais definido da estimulação sobre as várias dimensões cognitivas permanece envolto em alguma dúvida, dado que por um lado têm surgido alguns resultados negativos e por outro lado a maioria dos estudos tem usado populações relativamente pequenas, com infrequente recurso a grupos sham. Um dos principais problemas na avaliação dos possíveis efeitos da estimulação magnética repetitiva prende-se com o uso de diversos métodos de avaliação, com diferentes sensibilidades para o estudo das várias dimensões cognitivas, ou ainda com técnicas com menor resolução temporal (como os estudos de imagem cerebral funcional) comparativamente a técnicas neurofisiológicas. Neste ponto, a utilização de estudos no âmbito da neurofisiologia, como os potenciais de longa latência, pode assumir particular importância. O P300 auditivo, é um potencial evocado cognitivo, dependente da atenção e capacidade de discriminação do sujeito, traduzindo estadios mais superiores ou avançados de processamento associado a uma tarefa. As origens neuronais do P300 são múltiplas e bi-hemisféricas, associando-se a regiões como o hipocampo, o córtex pré-frontal ventrolateral e o córtex cingulado posterior. Até à data, são raros os estudos que abordaram a associação entre o P300 auditivo e a EMTr e ainda mais raros combinando a estimulação com TBS e o P300. A avaliação dos resultados prévios sugere que a estimulação magnética pode ser capaz de influenciar o processamento cognitivo e que as alterações podem ser monitorizadas pelo P300, mas são encontrados alguns resultados contraditórios, existindo significativas discrepâncias na metodologia usada. […

A system-theoretic approach to global and local regulation in neuron morphologies

Synaptic plasticity is a crucial neuronal mechanism for learning and memory. It allows synapses to change their strength over time. This dissertation focuses on a particular form of synaptic plasticity called synaptic scaling, a homeostatic mechanism that preserves relative synaptic strengths in an activity-dependent manner. Synaptic scaling is fundamental for neuronal stability, regulating other plasticity mechanisms like Hebbian plasticity or long-term potentiation (LTP). The aims of this dissertation are to explore the implications of synaptic scaling (and other forms of plasticity, such as structural plasticity) on the overall behavior of neurons. This is done using system-theoretic tools and feedback control. We first formulate a biophysical closed loop model of synaptic scaling. We then study how synaptic scaling affect neurons’ behavior in both abstract and reconstructed morphologies. This study reveals important tradeoffs between robustness, convergence rate, and accuracy of scaling. We first look at synaptic scaling as a “global control action” whose main role is to guarantee a steady level of neural activity. We then consider activity-dependent degradation as a “local control action” whose role is to assist the neuron in fine-tuning different desirable spatial concentration profiles. We show that, in extreme scenarios, it can promote a level of competition between synapses that has a destabilizing effect on the overall behavior. At the methodological level, we use compartmental modeling and we focus on the in- teraction between feedback and transport, in linear and nonlinear settings. Using classical system-theoretic tools like Bode and Nyquist analysis and singular perturbation arguments, and more recent tools like contraction and dominance theory, we derive parameter ranges under which synaptic scaling is stable and well-behaved (slow regulation), stable and oscilla- tory (aggressive regulation), and unstable (pathological regulation). We also study the system robustness against static and dynamics uncertainties. Finally, to understand how different plasticity mechanisms simultaneously affect the neuron behavior, we study synaptic scaling in the presence of activity-dependent growth (mimicking a structural plasticity mechanism). This is a third layer of control action shaping the neuron morphology. We find that activity-dependent growth improves the neuron’s performance when synaptic scaling is insufficient

Emergence of Spatio-Temporal Pattern Formation and Information Processing in the Brain.

The spatio-temporal patterns of neuronal activity are thought to underlie cognitive functions, such as our thoughts, perceptions, and emotions. Neurons and glial cells, specifically astrocytes, are interconnected in complex networks, where large-scale dynamical patterns emerge from local chemical and electrical signaling between individual network components. How these emergent patterns form and encode for information is the focus of this dissertation. I investigate how various mechanisms that can coordinate collections of neurons in their patterns of activity can potentially cause the interactions across spatial and temporal scales, which are necessary for emergent macroscopic phenomena to arise. My work explores the coordination of network dynamics through pattern formation and synchrony in both experiments and simulations. I concentrate on two potential mechanisms: astrocyte signaling and neuronal resonance properties. Due to their ability to modulate neurons, we investigate the role of astrocytic networks as a potential source for coordinating neuronal assemblies. In cultured networks, I image patterns of calcium signaling between astrocytes, and reproduce observed properties of the network calcium patterning and perturbations with a simple model that incorporates the mechanisms of astrocyte communication. Understanding the modes of communication in astrocyte networks and how they form spatial temporal patterns of their calcium dynamics is important to understanding their interaction with neuronal networks. We investigate this interaction between networks and how glial cells modulate neuronal dynamics through microelectrode array measurements of neuronal network dynamics. We quantify the spontaneous electrical activity patterns of neurons and show the effect of glia on the neuronal dynamics and synchrony. Through a computational approach I investigate an entirely different theoretical mechanism for coordinating ensembles of neurons. I show in a computational model how biophysical resonance shifts in individual neurons can interact with the network topology to influence pattern formation and separation. I show that sub-threshold neuronal depolarization, potentially from astrocytic modulation among other sources, can shift neurons into and out of resonance with specific bands of existing extracellular oscillations. This can act as a dynamic readout mechanism during information storage and retrieval. Exploring these mechanisms that facilitate emergence are necessary for understanding information processing in the brain.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111493/1/lshtrah_1.pd

Interpreting EEG alpha activity

Exploring EEG alpha oscillations has generated considerable interest, in particular with regards to the role they play in cognitive, psychomotor, psycho-emotional and physiological aspects of human life. However, there is no clearly agreed upon definition of what constitutes ‘alpha activity’ or which of the many indices should be used to characterize it. To address these issues this review attempts to delineate EEG alpha-activity, its physical, molecular and morphological nature, and examine the following indices: (1) the individual alpha peak frequency; (2) activation magnitude, as measured by alpha amplitude suppression across the individual alpha bandwidth in response to eyes opening, and (3) alpha “auto-rhythmicity” indices: which include intra-spindle amplitude variability, spindle length and steepness. Throughout, the article offers a number of suggestions regarding the mechanism(s) of alpha activity related to inter and intra-individual variability. In addition, it provides some insights into the various psychophysiological indices of alpha activity and highlights their role in optimal functioning and behavior

A walk in the statistical mechanical formulation of neural networks

Neural networks are nowadays both powerful operational tools (e.g., for pattern recognition, data mining, error correction codes) and complex theoretical models on the focus of scientific investigation. As for the research branch, neural networks are handled and studied by psychologists, neurobiologists, engineers, mathematicians and theoretical physicists. In particular, in theoretical physics, the key instrument for the quantitative analysis of neural networks is statistical mechanics. From this perspective, here, we first review attractor networks: starting from ferromagnets and spin-glass models, we discuss the underlying philosophy and we recover the strand paved by Hopfield, Amit-Gutfreund-Sompolinky. One step forward, we highlight the structural equivalence between Hopfield networks (modeling retrieval) and Boltzmann machines (modeling learning), hence realizing a deep bridge linking two inseparable aspects of biological and robotic spontaneous cognition. As a sideline, in this walk we derive two alternative (with respect to the original Hebb proposal) ways to recover the Hebbian paradigm, stemming from ferromagnets and from spin-glasses, respectively. Further, as these notes are thought of for an Engineering audience, we highlight also the mappings between ferromagnets and operational amplifiers and between antiferromagnets and flip-flops (as neural networks -built by op-amp and flip-flops- are particular spin-glasses and the latter are indeed combinations of ferromagnets and antiferromagnets), hoping that such a bridge plays as a concrete prescription to capture the beauty of robotics from the statistical mechanical perspective.Comment: Contribute to the proceeding of the conference: NCTA 2014. Contains 12 pages,7 figure

Interaction dynamics and autonomy in cognitive systems

The concept of autonomy is of crucial importance for understanding life and cognition. Whereas cellular and organismic autonomy is based in the self-production of the material infrastructure sustaining the existence of living beings as such, we are interested in how biological autonomy can be expanded into forms of autonomous agency, where autonomy as a form of organization is extended into the behaviour of an agent in interaction with its environment (and not its material self-production). In this thesis, we focus on the development of operational models of sensorimotor agency, exploring the construction of a domain of interactions creating a dynamical interface between agent and environment. We present two main contributions to the study of autonomous agency: First, we contribute to the development of a modelling route for testing, comparing and validating hypotheses about neurocognitive autonomy. Through the design and analysis of specific neurodynamical models embedded in robotic agents, we explore how an agent is constituted in a sensorimotor space as an autonomous entity able to adaptively sustain its own organization. Using two simulation models and different dynamical analysis and measurement of complex patterns in their behaviour, we are able to tackle some theoretical obstacles preventing the understanding of sensorimotor autonomy, and to generate new predictions about the nature of autonomous agency in the neurocognitive domain. Second, we explore the extension of sensorimotor forms of autonomy into the social realm. We analyse two cases from an experimental perspective: the constitution of a collective subject in a sensorimotor social interactive task, and the emergence of an autonomous social identity in a large-scale technologically-mediated social system. Through the analysis of coordination mechanisms and emergent complex patterns, we are able to gather experimental evidence indicating that in some cases social autonomy might emerge based on mechanisms of coordinated sensorimotor activity and interaction, constituting forms of collective autonomous agency

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot