A Neurogenetic Algorithm Based on Rational Agents

Lately, a lot of research has been conducted on the automatic design of artificial neural networks (ADANNs) using evolutionary algorithms, in the so-called neuro-evolutive algorithms (NEAs). Many of the presented proposals are not biologically inspired and are not able to generate modular, hierarchical and recurrent neural structures, such as those often found in living beings capable of solving intricate survival problems. Bearing in mind the idea that a nervous system's design and organization is a constructive process carried out by genetic information encoded in DNA, this paper proposes a biologically inspired NEA that evolves ANNs using these ideas as computational design techniques. In order to do this, we propose a Lindenmayer System with memory that implements the principles of organization, modularity, repetition (multiple use of the same sub-structure), hierarchy (recursive composition of sub-structures), minimizing the scalability problem of other methods. In our method, the basic neural codification is integrated to a genetic algorithm (GA) that implements the constructive approach found in the evolutionary process, making it closest to biological processes. Thus, the proposed method is a decision-making (DM) process, the fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. In other words, the penalty approach implemented through the fitness function automatically rewards the economical ANNs with stronger generalization and extrapolation capacities. Our method was initially tested on a simple, but non-trivial, XOR problem. We also submit our method to two other problems of increasing complexity: time series prediction that represents consumer price index and prediction of the effect of a new drug on breast cancer. In most cases, our NEA outperformed the other methods, delivering the most accurate classification. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decision-making process. The result is an optimized neural network architecture for solving classification problems

Cultural Neuroeconomics of Intertemporal Choice

According to theories of cultural neuroscience, Westerners and Easterners may have distinct styles of cognition (e.g., different allocation of attention). Previous research has shown that Westerners and Easterners tend to utilize analytical and holistic cognitive styles, respectively. On the other hand, little is known regarding the cultural differences in neuroeconomic behavior. For instance, economic decisions may be affected by cultural differences in neurocomputational processing underlying attention; however, this area of neuroeconomics has been largely understudied. In the present paper, we attempt to bridge this gap by considering the links between the theory of cultural neuroscience and neuroeconomic theory\ud of the role of attention in intertemporal choice. We predict that (i) Westerners are more impulsive and inconsistent in intertemporal choice in comparison to Easterners, and (ii) Westerners more steeply discount delayed monetary losses than Easterners. We examine these predictions by utilizing a novel temporal discounting model based on Tsallis' statistics (i.e. a q-exponential model). Our preliminary analysis of temporal discounting of gains and losses by Americans and Japanese confirmed the predictions from the cultural neuroeconomic theory. Future study directions, employing computational modeling via neural networks, are briefly outlined and discussed

Disordered Gambling: Etiology, Trajectory and Clinical Considerations

Gambling-related research has advanced rapidly during the past 20 years. As a result of expanding interest toward pathological gambling (PG), stakeholders (e.g., clinicians, regulators, and policy makers) have a better understanding of excessive gambling, including its etiology (e.g., neurobiological/neurogenetic, psychological, and sociological factors) and trajectory (e.g., initiation, course, and adaptation to gambling exposure). In this article, we will examine these advances in PG-related research and then consider some of the clinical implications of these advances. We will consider the DSM-V Impulse Control Work Group’s recently proposed changes to the DSM criteria for PG. We also will review how clinicians can more accurately and efficiently diagnose clients seeking help for gambling-related problems by utilizing brief screens. Finally, we consider the importance of future research that can identify behavioral markers for PG. We suggest that identifying these markers will allow clinicians to make earlier diagnoses, suggest targeted treatments, and advance secondary prevention efforts. Original version available at http://www.annualreviews.org/toc/clinpsy/7/

Automated Trading Systems Statistical and Machine Learning Methods and Hardware Implementation: A Survey

Automated trading, which is also known as algorithmic trading, is a method of using a predesigned computer program to submit a large number of trading orders to an exchange. It is substantially a real-time decision-making system which is under the scope of Enterprise Information System (EIS). With the rapid development of telecommunication and computer technology, the mechanisms underlying automated trading systems have become increasingly diversified. Considerable effort has been exerted by both academia and trading firms towards mining potential factors that may generate significantly higher profits. In this paper, we review studies on trading systems built using various methods and empirically evaluate the methods by grouping them into three types: technical analyses, textual analyses and high-frequency trading. Then, we evaluate the advantages and disadvantages of each method and assess their future prospects

Beyond dichotomies in reinforcement learning

Reinforcement learning (RL) is a framework of particular importance to psychology, neuroscience and machine learning. Interactions between these fields, as promoted through the common hub of RL, has facilitated paradigm shifts that relate multiple levels of analysis in a singular framework (for example, relating dopamine function to a computationally defined RL signal). Recently, more sophisticated RL algorithms have been proposed to better account for human learning, and in particular its oft-documented reliance on two separable systems: a model-based (MB) system and a model-free (MF) system. However, along with many benefits, this dichotomous lens can distort questions, and may contribute to an unnecessarily narrow perspective on learning and decision-making. Here, we outline some of the consequences that come from overconfidently mapping algorithms, such as MB versus MF RL, with putative cognitive processes. We argue that the field is well positioned to move beyond simplistic dichotomies, and we propose a means of refocusing research questions towards the rich and complex components that comprise learning and decision-making

STOCK MARKET FORECASTING BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGY

This culminating experience project used artificial intelligence (AI) technology to forecast and analyze the stock market and construct complex nonlinear relationships between the input data and the output data. This project used a radial basis function neural network to forecast and analyze the stock market data. Compared the radial basis function neural network performance with the feed-forward neural network and clearly showed the superiority of the radial basis function neural network over the feed-forward neural network in the data processing. The results showed that AI technology could effectively predict stock market performance. Based on the results, the conclusion is that the prediction performance of the RBF neural network is better than that of the multilayer feed-forward neural network. Areas for future research are to explore the use of other AI and other Neural Network Algorithms such as Back Propagation, Convolutional, Kohonen Self Organizing, and Modular to predict stock market performance

Isolation of anticancer and anti-trypanosome secondary metabolites from the endophytic fungus Aspergillus flocculus via bioactivity guided isolation and MS based metabolomics

This study aims to identify bioactive anticancer and anti-trypanosome secondary metabolites from the fermentation culture of Aspergillus flocculus endophyte assisted by modern metabolomics technologies. The endophyte was isolated from the stem of the medicinal plant Markhamia platycalyx and identified using phylogenetics. Principle component analysis was employed to screen for the optimum growth endophyte culturing conditions and revealing that the 30-days rice culture (RC-30d) provided the highest levels of the bioactive agents. To pinpoint for active chemicals in endophyte crude extracts and successive fractions, a new application of molecular interaction network is implemented to correlate the chemical and biological profiles of the anti-trypanosome active fractions to highlight the metabolites mediating for bioactivity prior to purification trials. Multivariate data analysis (MVDA), with the aid of dereplication studies, efficiently annotated the putatively active anticancer molecules. The small-scale RC-30d fungal culture was purified using high-throughput chromatographic techniques to yield compound 1, a novel polyketide molecule though inactive. Whereas, active fractions revealed from the bioactivity guided fractionation of medium scale RC-30d culture were further purified to yield 7 metabolites, 5 of which namely cis-4-hydroxymellein, 5-hydroxymellein, diorcinol, botryoisocoumarin A and mellein, inhibited the growth of chronic myelogenous leukemia cell line K562 at 30 μM. 3-Hydroxymellein and diorcinol exhibited a respective inhibition of 56% and 97% to the sleeping sickness causing parasite Trypanosoma brucei brucei. More interestingly, the anti-trypanosomal activity of A. flocculus extract appeared to be mediated by the synergistic effect of the active steroidal compounds i.e. ergosterol peroxide, ergosterol and campesterol. The isolated structures were elucidated by using 1D, 2D NMR and HR-ESIMS

Multiscale computation and dynamic attention in biological and artificial intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks—with fixed scalings—to attention, transformers, dynamic convolutions, and consciousness priors—which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence