MINES: Mutual Information Neuro-Evolutionary System

Mutual information neuro-evolutionary system (MINES) presents a novel self-governing approach to determine the optimal quantity and connectivity of the hidden layer of a three layer feed-forward neural network founded on theoretical and practical basis. The system is a combination of a feed-forward neural network, back-propagation algorithm, genetic algorithm, mutual information and clustering. Back-propagation is used for parameter learning to reduce the system’s error; while mutual information aides back-propagation to follow an effective path in the weight space. A genetic algorithm changes the incoming synaptic connections of the hidden nodes, based on the fitness provided by the mutual information from the error space to the hidden layer, to perform structural learning. Mutual information determines the appropriate synapses, connecting the hidden nodes to the input layer; however, in effect it also links the back-propagation to the genetic algorithm. Weight clustering is applied to reduce hidden nodes having similar functionality; i.e. those possessing same connectivity patterns and close Euclidean angle in the weight space. Finally, the performance of the system is assessed on two theoretical and one empirical problems. A nonlinear polynomial regression problem and the well known two-spiral classification task are used to evaluate the theoretical performance of the system. Forecasting daily crude oil prices are conducted to observe the performance of MINES on a real world application

Interactive rhythms across species: The evolutionary biology of animal chorusing and turn-taking

The study of human language is progressively moving toward comparative and interactive frameworks, extending the concept of turn‐taking to animal communication. While such an endeavor will help us understand the interactive origins of language, any theoretical account for cross‐species turn‐taking should consider three key points. First, animal turn‐taking must incorporate biological studies on animal chorusing, namely how different species coordinate their signals over time. Second, while concepts employed in human communication and turn‐taking, such as intentionality, are still debated in animal behavior, lower level mechanisms with clear neurobiological bases can explain much of animal interactive behavior. Third, social behavior, interactivity, and cooperation can be orthogonal, and the alternation of animal signals need not be cooperative. Considering turn‐taking a subset of chorusing in the rhythmic dimension may avoid overinterpretation and enhance the comparability of future empirical work

The cultural psychology of obesity: diffusion of pathological norms from Western to East Asian societies

We examine the accelerating worldwide obesity epidemic using a mathematical model relating a cognitive hypothalamic-pituitary-adrenal axis tuned by embedding cultural context to a signal of chronic, structured, psychosocial threat. The obesity epidemic emerges as a distorted physiological image of ratcheting social pathology involving massive, policy-driven, economic and social 'structural adjustment' causing increasing individual, family, and community insecurity. The resulting, broadly developmental, disorder, while stratified by expected divisions of class, ethnicity, and culture, is nonetheless relentlessly engulfing even affluent majority populations across the globe. The progression of analogous epidemics in affluent Western and East Asian socieities is particularly noteworthy since these enjoy markedly different cultural structures known to influence even such fundamental psychophysical phenomena as change blindness. Indeed, until recently population patterns of obesity were quite different for these cultures. We attribute the entrainment of East Asian societies into the obesity epidemic to the diffusion of Western socioeconomic practices whose imposed resource uncertainties and exacerbation of social and economic divisions constitute powerful threat signals. We find that individual-oriented 'therapeutic' interventions will be largely ineffective since the therapeutic process itself (e.g. relinace on drug treatments) embodies the very threats causing the epidemic

Rock-burst occurrence prediction based on optimized naïve bayes models

Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE

A statistical mechanical problem?

The problem of deriving the processes of perception and cognition or the modes of behaviour from states of the brain appears to be unsolvablein view of the huge numbers of units involved. However, neural activities are not random, but, rather, form spatio-temporal patterns, and thank to these restrictions, which in turn are due to connections among neurons, the problem can at least be approached.The situation is similar to what happens in large physical ensembles, whereglobal behaviour is derived by microscopic properties. Despite the obvious differences between neural and physical systems a statistical mechanics approach is almost inescapable, since dynamics of the brain as a whole are clearly determined by the outputs of single neurons. In this paper it will be shown how, starting from very simple systems, connectivity engenders levels of increasing complexity in thefunctions of the brain depending on specific constraints.Correspondingly levels of explanations must take into account the fundamental role of constraints and assign at each level proper model structures and variables, that, on one hand, emerge from outputs of the lower levels, and yet are specific, in that they ignore irrelevant details

Social zebrafish: Danio rerio as an emerging model in social neuroendocrinology

The fitness benefits of social life depend on the ability of animals to affiliate with others and form groups, on dominance hierarchies within groups that determine resource distribution, and on cognitive capacities for recognition, learning and information transfer. The evolution of these phenotypes is coupled with that of neuroendocrine mechanisms, but the causal link between the two remains underexplored. Growing evidence from our research group and others demonstrates that the tools available in zebrafish, Danio rerio, can markedly facilitate progress in this field. Here, we review this evidence and provide a synthesis of the state-of-the-art in this model system. We discuss the involvement of generalized motivation and cognitive components, neuroplasticity and functional connectivity across social decision-making brain areas, and how these are modulated chiefly by the oxytocin-vasopressin neuroendocrine system, but also by reward-pathway monoamine signaling and the effects of sex-hormones and stress physiology.Fundação para a Ciência e Tecnologia - FCTinfo:eu-repo/semantics/publishedVersio