Rule based systems for big data a machine learning approach

CERN Document Server

Liu, Han; Cocea, Mihaela

2016-01-01

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.

Instruction of pattern recognition by MATLAB practice 1

International Nuclear Information System (INIS)

1999-06-01

This book describes the pattern recognition by MATLAB practice. It includes possibility and limit of AI, introduction of pattern recognition a vector and matrix, basic status and a probability theory, a random variable and probability distribution, statistical decision theory, data-mining, gaussian mixture model, a nerve cell modeling such as Hebb's learning rule, LMS learning rule, genetic algorithm, dynamic programming and DTW, HMN on Markov model and HMM's three problems and solution, introduction of SVM with KKT condition and margin optimum, kernel trick and MATLAB practice.

Students’ Learning Obstacles and Alternative Solution in Counting Rules Learning Levels Senior High School

Directory of Open Access Journals (Sweden)

M A Jatmiko

2017-12-01

Full Text Available The counting rules is a topic in mathematics senior high school. In the learning process, teachers often find students who have difficulties in learning this topic. Knowing the characteristics of students' learning difficulties and analyzing the causes is important for the teacher, as an effort in trying to reflect the learning process and as a reference in constructing alternative learning solutions which appropriate to anticipate students’ learning obstacles. This study uses qualitative methods and involves 70 students of class XII as research subjects. The data collection techniques used in this study is diagnostic test instrument about learning difficulties in counting rules, observation, and interview. The data used to know the learning difficulties experienced by students, the causes of learning difficulties, and to develop alternative learning solutions. From the results of data analysis, the results of diagnostic tests researcher found some obstacles faced by students, such as students get confused in describing the definition, students difficulties in understanding the procedure of solving multiplication rules. Based on those problems, researcher analyzed the causes of these difficulties and make hypothetical learning trajectory as an alternative solution in counting rules learning.

The involvement of long-term serial-order memory in reading development: A longitudinal study.

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Bogaerts, Louisa; Szmalec, Arnaud; De Maeyer, Marjolijn; Page, Mike P A; Duyck, Wouter

2016-05-01

Recent findings suggest that Hebb repetition learning-a paradigmatic example of long-term serial-order learning-is impaired in adults with dyslexia. The current study further investigated the link between serial-order learning and reading using a longitudinal developmental design. With this aim, verbal and visual Hebb repetition learning performance and reading skills were assessed in 96 Dutch-speaking children who we followed from first through second grade of primary school. We observed a positive association between order learning capacities and reading ability as well as weaker Hebb learning performance in early readers with poor reading skills even at the onset of reading instruction. Hebb learning further predicted individual differences in later (nonword) reading skills. Finally, Hebb learning was shown to explain a significant part of the variance in reading performance above and beyond phonological awareness. These findings highlight the role of serial-order memory in reading ability. Copyright © 2015 Elsevier Inc. All rights reserved.

Memory in Neural Networks and Glasses

NARCIS (Netherlands)

Heerema, M.

2000-01-01

The thesis tries and models a neural network in a way which, at essential points, is biologically realistic. In a biological context, the changes of the synapses of the neural network are most often described by what is called `Hebb's learning rule'. On careful analysis it is, in fact, nothing but a

Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

Science.gov (United States)

Mihai, Gabroveanu

2015-09-01

Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

Concurrence of rule- and similarity-based mechanisms in artificial grammar learning.

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Opitz, Bertram; Hofmann, Juliane

2015-03-01

A current theoretical debate regards whether rule-based or similarity-based learning prevails during artificial grammar learning (AGL). Although the majority of findings are consistent with a similarity-based account of AGL it has been argued that these results were obtained only after limited exposure to study exemplars, and performance on subsequent grammaticality judgment tests has often been barely above chance level. In three experiments the conditions were investigated under which rule- and similarity-based learning could be applied. Participants were exposed to exemplars of an artificial grammar under different (implicit and explicit) learning instructions. The analysis of receiver operating characteristics (ROC) during a final grammaticality judgment test revealed that explicit but not implicit learning led to rule knowledge. It also demonstrated that this knowledge base is built up gradually while similarity knowledge governed the initial state of learning. Together these results indicate that rule- and similarity-based mechanisms concur during AGL. Moreover, it could be speculated that two different rule processes might operate in parallel; bottom-up learning via gradual rule extraction and top-down learning via rule testing. Crucially, the latter is facilitated by performance feedback that encourages explicit hypothesis testing. Copyright © 2015 Elsevier Inc. All rights reserved.

Learning general phonological rules from distributional information: a computational model.

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Calamaro, Shira; Jarosz, Gaja

2015-04-01

Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony (Peperkamp, Le Calvez, Nadal, & Dupoux, 2006). This paper extends the model to account for learning of a broader set of phonological alternations and the formalization of these alternations as general rules. In Experiment 1, we apply the original model to new data in Dutch and demonstrate its limitations in learning nonallophonic rules. In Experiment 2, we extend the model to allow it to learn general rules for alternations that apply to a class of segments. In Experiment 3, the model is further extended to allow for generalization by context; we argue that this generalization must be constrained by linguistic principles. Copyright © 2014 Cognitive Science Society, Inc.

Rule learning in autism: the role of reward type and social context.

Science.gov (United States)

Jones, E J H; Webb, S J; Estes, A; Dawson, G

2013-01-01

Learning abstract rules is central to social and cognitive development. Across two experiments, we used Delayed Non-Matching to Sample tasks to characterize the longitudinal development and nature of rule-learning impairments in children with Autism Spectrum Disorder (ASD). Results showed that children with ASD consistently experienced more difficulty learning an abstract rule from a discrete physical reward than children with DD. Rule learning was facilitated by the provision of more concrete reinforcement, suggesting an underlying difficulty in forming conceptual connections. Learning abstract rules about social stimuli remained challenging through late childhood, indicating the importance of testing executive functions in both social and non-social contexts.

Rule Learning in Autism: The Role of Reward Type and Social Context

OpenAIRE

Jones, E. J. H.; Webb, S. J.; Estes, A.; Dawson, G.

2013-01-01

Learning abstract rules is central to social and cognitive development. Across two experiments, we used Delayed Non-Matching to Sample tasks to characterize the longitudinal development and nature of rule-learning impairments in children with Autism Spectrum Disorder (ASD). Results showed that children with ASD consistently experienced more difficulty learning an abstract rule from a discrete physical reward than children with DD. Rule learning was facilitated by the provision of more concret...

A self-learning rule base for command following in dynamical systems

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Tsai, Wei K.; Lee, Hon-Mun; Parlos, Alexander

1992-01-01

In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.

Moral empiricism and the bias for act-based rules.

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Ayars, Alisabeth; Nichols, Shaun

2017-10-01

Previous studies on rule learning show a bias in favor of act-based rules, which prohibit intentionally producing an outcome but not merely allowing the outcome. Nichols, Kumar, Lopez, Ayars, and Chan (2016) found that exposure to a single sample violation in which an agent intentionally causes the outcome was sufficient for participants to infer that the rule was act-based. One explanation is that people have an innate bias to think rules are act-based. We suggest an alternative empiricist account: since most rules that people learn are act-based, people form an overhypothesis (Goodman, 1955) that rules are typically act-based. We report three studies that indicate that people can use information about violations to form overhypotheses about rules. In study 1, participants learned either three "consequence-based" rules that prohibited allowing an outcome or three "act-based" rules that prohibiting producing the outcome; in a subsequent learning task, we found that participants who had learned three consequence-based rules were more likely to think that the new rule prohibited allowing an outcome. In study 2, we presented participants with either 1 consequence-based rule or 3 consequence-based rules, and we found that those exposed to 3 such rules were more likely to think that a new rule was also consequence based. Thus, in both studies, it seems that learning 3 consequence-based rules generates an overhypothesis to expect new rules to be consequence-based. In a final study, we used a more subtle manipulation. We exposed participants to examples act-based or accident-based (strict liability) laws and then had them learn a novel rule. We found that participants who were exposed to the accident-based laws were more likely to think a new rule was accident-based. The fact that participants' bias for act-based rules can be shaped by evidence from other rules supports the idea that the bias for act-based rules might be acquired as an overhypothesis from the

The spatial distribution of errors made by rats in Hebb-Williams type mazes in relation to the spatial properties of the blind alleys

NARCIS (Netherlands)

Boer, S. de; Bohus, B.

The various configurations in series of Hebb-Williams type of mazes, which are used to measure problem solving behaviour in rats, differ markedly in structure. The relationship between error behaviour and spatial maze structure in control rats tested in a number of pharmacological experiments is

A rule-learning program in high energy physics event classification

International Nuclear Information System (INIS)

Clearwater, S.H.; Stern, E.G.

1991-01-01

We have applied a rule-learning program to the problem of event classification in high energy physics. The program searches for event classifications, i.e. rules, and effectively allows an exploration of many more possible classifications than is practical by a physicist. The program, RL4, is particularly useful because it can easily explore multi-dimensional rules as well as rules that may seem non-intuitive at first to the physicist. RL4 is also contrasted with other learning programs. (orig.)

Online learning algorithm for ensemble of decision rules

KAUST Repository

Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2011-01-01

We describe an online learning algorithm that builds a system of decision rules for a classification problem. Rules are constructed according to the minimum description length principle by a greedy algorithm or using the dynamic programming approach

Multi-Valued Associative Memory Neural Network

Institute of Scientific and Technical Information of China (English)

修春波; 刘向东; 张宇河

2003-01-01

A novel learning method for multi-valued associative memory network is introduced, which is based on Hebb rule, but utilizes more information. According to the current probe vector, the connection weights matrix could be chosen dynamically. Double-valued and multi-valued associative memory are all realized in our simulation experiment. The experimental results show that the method could enhance the associative success rate.

Online learning algorithm for ensemble of decision rules

KAUST Repository

Chikalov, Igor

2011-01-01

We describe an online learning algorithm that builds a system of decision rules for a classification problem. Rules are constructed according to the minimum description length principle by a greedy algorithm or using the dynamic programming approach. © 2011 Springer-Verlag.

Criterion learning in rule-based categorization: simulation of neural mechanism and new data.

Science.gov (United States)

Helie, Sebastien; Ell, Shawn W; Filoteo, J Vincent; Maddox, W Todd

2015-04-01

In perceptual categorization, rule selection consists of selecting one or several stimulus-dimensions to be used to categorize the stimuli (e.g., categorize lines according to their length). Once a rule has been selected, criterion learning consists of defining how stimuli will be grouped using the selected dimension(s) (e.g., if the selected rule is line length, define 'long' and 'short'). Very little is known about the neuroscience of criterion learning, and most existing computational models do not provide a biological mechanism for this process. In this article, we introduce a new model of rule learning called Heterosynaptic Inhibitory Criterion Learning (HICL). HICL includes a biologically-based explanation of criterion learning, and we use new category-learning data to test key aspects of the model. In HICL, rule selective cells in prefrontal cortex modulate stimulus-response associations using pre-synaptic inhibition. Criterion learning is implemented by a new type of heterosynaptic error-driven Hebbian learning at inhibitory synapses that uses feedback to drive cell activation above/below thresholds representing ionic gating mechanisms. The model is used to account for new human categorization data from two experiments showing that: (1) changing rule criterion on a given dimension is easier if irrelevant dimensions are also changing (Experiment 1), and (2) showing that changing the relevant rule dimension and learning a new criterion is more difficult, but also facilitated by a change in the irrelevant dimension (Experiment 2). We conclude with a discussion of some of HICL's implications for future research on rule learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Dynamically stable associative learning: a neurobiologically based ANN and its applications

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Vogl, Thomas P.; Blackwell, Kim L.; Barbour, Garth; Alkon, Daniel L.

1992-07-01

Most currently popular artificial neural networks (ANN) are based on conceptions of neuronal properties that date back to the 1940s and 50s, i.e., to the ideas of McCullough, Pitts, and Hebb. Dystal is an ANN based on current knowledge of neurobiology at the cellular and subcellular level. Networks based on these neurobiological insights exhibit the following advantageous properties: (1) A theoretical storage capacity of bN non-orthogonal memories, where N is the number of output neurons sharing common inputs and b is the number of distinguishable (gray shade) levels. (2) The ability to learn, store, and recall associations among noisy, arbitrary patterns. (3) A local synaptic learning rule (learning depends neither on the output of the post-synaptic neuron nor on a global error term), some of whose consequences are: (4) Feed-forward, lateral, and feed-back connections (as well as time-sensitive connections) are possible without alteration of the learning algorithm; (5) Storage allocation (patch creation) proceeds dynamically as associations are learned (self- organizing); (6) The number of training set presentations required for learning is small (different expressions and/or corrupted by noise, and on reading hand-written digits (98% accuracy) and hand-printed Japanese Kanji (90% accuracy) is demonstrated.

Rule induction performance in amnestic mild cognitive impairment and Alzheimer's dementia: examining the role of simple and biconditional rule learning processes.

Science.gov (United States)

Oosterman, Joukje M; Heringa, Sophie M; Kessels, Roy P C; Biessels, Geert Jan; Koek, Huiberdina L; Maes, Joseph H R; van den Berg, Esther

2017-04-01

Rule induction tests such as the Wisconsin Card Sorting Test require executive control processes, but also the learning and memorization of simple stimulus-response rules. In this study, we examined the contribution of diminished learning and memorization of simple rules to complex rule induction test performance in patients with amnestic mild cognitive impairment (aMCI) or Alzheimer's dementia (AD). Twenty-six aMCI patients, 39 AD patients, and 32 control participants were included. A task was used in which the memory load and the complexity of the rules were independently manipulated. This task consisted of three conditions: a simple two-rule learning condition (Condition 1), a simple four-rule learning condition (inducing an increase in memory load, Condition 2), and a complex biconditional four-rule learning condition-inducing an increase in complexity and, hence, executive control load (Condition 3). Performance of AD patients declined disproportionately when the number of simple rules that had to be memorized increased (from Condition 1 to 2). An additional increment in complexity (from Condition 2 to 3) did not, however, disproportionately affect performance of the patients. Performance of the aMCI patients did not differ from that of the control participants. In the patient group, correlation analysis showed that memory performance correlated with Condition 1 performance, whereas executive task performance correlated with Condition 2 performance. These results indicate that the reduced learning and memorization of underlying task rules explains a significant part of the diminished complex rule induction performance commonly reported in AD, although results from the correlation analysis suggest involvement of executive control functions as well. Taken together, these findings suggest that care is needed when interpreting rule induction task performance in terms of executive function deficits in these patients.

Learning a New Selection Rule in Visual and Frontal Cortex.

Science.gov (United States)

van der Togt, Chris; Stănişor, Liviu; Pooresmaeili, Arezoo; Albantakis, Larissa; Deco, Gustavo; Roelfsema, Pieter R

2016-08-01

How do you make a decision if you do not know the rules of the game? Models of sensory decision-making suggest that choices are slow if evidence is weak, but they may only apply if the subject knows the task rules. Here, we asked how the learning of a new rule influences neuronal activity in the visual (area V1) and frontal cortex (area FEF) of monkeys. We devised a new icon-selection task. On each day, the monkeys saw 2 new icons (small pictures) and learned which one was relevant. We rewarded eye movements to a saccade target connected to the relevant icon with a curve. Neurons in visual and frontal cortex coded the monkey's choice, because the representation of the selected curve was enhanced. Learning delayed the neuronal selection signals and we uncovered the cause of this delay in V1, where learning to select the relevant icon caused an early suppression of surrounding image elements. These results demonstrate that the learning of a new rule causes a transition from fast and random decisions to a more considerate strategy that takes additional time and they reveal the contribution of visual and frontal cortex to the learning process. © The Author 2016. Published by Oxford University Press.

Ontology-based concept map learning path reasoning system using SWRL rules

Energy Technology Data Exchange (ETDEWEB)

Chu, K.-K.; Lee, C.-I. [National Univ. of Tainan, Taiwan (China). Dept. of Computer Science and Information Learning Technology

2010-08-13

Concept maps are graphical representations of knowledge. Concept mapping may reduce students' cognitive load and extend simple memory function. The purpose of this study was on the diagnosis of students' concept map learning abilities and the provision of personally constructive advice dependant on their learning path and progress. Ontology is a useful method with which to represent and store concept map information. Semantic web rule language (SWRL) rules are easy to understand and to use as specific reasoning services. This paper discussed the selection of grade 7 lakes and rivers curriculum for which to devise a concept map learning path reasoning service. The paper defined a concept map e-learning ontology and two SWRL semantic rules, and collected users' concept map learning path data to infer implicit knowledge and to recommend the next learning path for users. It was concluded that the designs devised in this study were feasible and advanced and the ontology kept the domain knowledge preserved. SWRL rules identified an abstraction model for inferred properties. Since they were separate systems, they did not interfere with each other, while ontology or SWRL rules were maintained, ensuring persistent system extensibility and robustness. 15 refs., 1 tab., 8 figs.

An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules

Directory of Open Access Journals (Sweden)

Antonio

2012-04-01

Full Text Available Fuzzy modelling research has traditionally focused on certain types of fuzzy rules. However, the use of alternative rule models could improve the ability of fuzzy systems to represent a specific problem. In this proposal, an extended fuzzy rule model, that can include relations between variables in the antecedent of rules is presented. Furthermore, a learning algorithm based on the iterative genetic approach which is able to represent the knowledge using this model is proposed as well. On the other hand, potential relations among initial variables imply an exponential growth in the feasible rule search space. Consequently, two filters for detecting relevant potential relations are added to the learning algorithm. These filters allows to decrease the search space complexity and increase the algorithm efficiency. Finally, we also present an experimental study to demonstrate the benefits of using fuzzy relational rules.

Incremental Learning of Context Free Grammars by Parsing-Based Rule Generation and Rule Set Search

Science.gov (United States)

Nakamura, Katsuhiko; Hoshina, Akemi

This paper discusses recent improvements and extensions in Synapse system for inductive inference of context free grammars (CFGs) from sample strings. Synapse uses incremental learning, rule generation based on bottom-up parsing, and the search for rule sets. The form of production rules in the previous system is extended from Revised Chomsky Normal Form A→βγ to Extended Chomsky Normal Form, which also includes A→B, where each of β and γ is either a terminal or nonterminal symbol. From the result of bottom-up parsing, a rule generation mechanism synthesizes minimum production rules required for parsing positive samples. Instead of inductive CYK algorithm in the previous version of Synapse, the improved version uses a novel rule generation method, called ``bridging,'' which bridges the lacked part of the derivation tree for the positive string. The improved version also employs a novel search strategy, called serial search in addition to minimum rule set search. The synthesis of grammars by the serial search is faster than the minimum set search in most cases. On the other hand, the size of the generated CFGs is generally larger than that by the minimum set search, and the system can find no appropriate grammar for some CFL by the serial search. The paper shows experimental results of incremental learning of several fundamental CFGs and compares the methods of rule generation and search strategies.

Code-specific learning rules improve action selection by populations of spiking neurons.

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Friedrich, Johannes; Urbanczik, Robert; Senn, Walter

2014-08-01

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

The evolution of social learning rules: payoff-biased and frequency-dependent biased transmission.

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Kendal, Jeremy; Giraldeau, Luc-Alain; Laland, Kevin

2009-09-21

Humans and other animals do not use social learning indiscriminately, rather, natural selection has favoured the evolution of social learning rules that make selective use of social learning to acquire relevant information in a changing environment. We present a gene-culture coevolutionary analysis of a small selection of such rules (unbiased social learning, payoff-biased social learning and frequency-dependent biased social learning, including conformism and anti-conformism) in a population of asocial learners where the environment is subject to a constant probability of change to a novel state. We define conditions under which each rule evolves to a genetically polymorphic equilibrium. We find that payoff-biased social learning may evolve under high levels of environmental variation if the fitness benefit associated with the acquired behaviour is either high or low but not of intermediate value. In contrast, both conformist and anti-conformist biases can become fixed when environment variation is low, whereupon the mean fitness in the population is higher than for a population of asocial learners. Our examination of the population dynamics reveals stable limit cycles under conformist and anti-conformist biases and some highly complex dynamics including chaos. Anti-conformists can out-compete conformists when conditions favour a low equilibrium frequency of the learned behaviour. We conclude that evolution, punctuated by the repeated successful invasion of different social learning rules, should continuously favour a reduction in the equilibrium frequency of asocial learning, and propose that, among competing social learning rules, the dominant rule will be the one that can persist with the lowest frequency of asocial learning.

Thermodynamic efficiency of learning a rule in neural networks

Science.gov (United States)

Goldt, Sebastian; Seifert, Udo

2017-11-01

Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

Science.gov (United States)

van Ginneken, Bram

2017-03-01

Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.

On-line learning of non-monotonic rules by simple perceptron

OpenAIRE

Inoue, Jun-ichi; Nishimori, Hidetoshi; Kabashima, Yoshiyuki

1997-01-01

We study the generalization ability of a simple perceptron which learns unlearnable rules. The rules are presented by a teacher perceptron with a non-monotonic transfer function. The student is trained in the on-line mode. The asymptotic behaviour of the generalization error is estimated under various conditions. Several learning strategies are proposed and improved to obtain the theoretical lower bound of the generalization error.

Symbol manipulation and rule learning in spiking neuronal networks.

Science.gov (United States)

Fernando, Chrisantha

2011-04-21

It has been claimed that the productivity, systematicity and compositionality of human language and thought necessitate the existence of a physical symbol system (PSS) in the brain. Recent discoveries about temporal coding suggest a novel type of neuronal implementation of a physical symbol system. Furthermore, learning classifier systems provide a plausible algorithmic basis by which symbol re-write rules could be trained to undertake behaviors exhibiting systematicity and compositionality, using a kind of natural selection of re-write rules in the brain, We show how the core operation of a learning classifier system, namely, the replication with variation of symbol re-write rules, can be implemented using spike-time dependent plasticity based supervised learning. As a whole, the aim of this paper is to integrate an algorithmic and an implementation level description of a neuronal symbol system capable of sustaining systematic and compositional behaviors. Previously proposed neuronal implementations of symbolic representations are compared with this new proposal. Copyright © 2011 Elsevier Ltd. All rights reserved.

Selection rule for Dirac-like points in two-dimensional dielectric photonic crystals

KAUST Repository

Li, Yan

2013-01-01

We developed a selection rule for Dirac-like points in two-dimensional dielectric photonic crystals. The rule is derived from a perturbation theory and states that a non-zero, mode-coupling integral between the degenerate Bloch states guarantees a Dirac-like point, regardless of the type of the degeneracy. In fact, the selection rule can also be determined from the symmetry of the Bloch states even without computing the integral. Thus, the existence of Dirac-like points can be quickly and conclusively predicted for various photonic crystals independent of wave polarization, lattice structure, and composition. © 2013 Optical Society of America.

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

Science.gov (United States)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

The statistical mechanics of learning a rule

International Nuclear Information System (INIS)

Watkin, T.L.H.; Rau, A.; Biehl, M.

1993-01-01

A summary is presented of the statistical mechanical theory of learning a rule with a neural network, a rapidly advancing area which is closely related to other inverse problems frequently encountered by physicists. By emphasizing the relationship between neural networks and strongly interacting physical systems, such as spin glasses, the authors show how learning theory has provided a workshop in which to develop new, exact analytical techniques

Phonological learning in semantic dementia.

Science.gov (United States)

Jefferies, Elizabeth; Bott, Samantha; Ehsan, Sheeba; Lambon Ralph, Matthew A

2011-04-01

Patients with semantic dementia (SD) have anterior temporal lobe (ATL) atrophy that gives rise to a highly selective deterioration of semantic knowledge. Despite pronounced anomia and poor comprehension of words and pictures, SD patients have well-formed, fluent speech and normal digit span. Given the intimate connection between phonological STM and word learning revealed by both neuropsychological and developmental studies, SD patients might be expected to show good acquisition of new phonological forms, even though their ability to map these onto meanings is impaired. In contradiction of these predictions, a limited amount of previous research has found poor learning of new phonological forms in SD. In a series of experiments, we examined whether SD patient, GE, could learn novel phonological sequences and, if so, under which circumstances. GE showed normal benefits of phonological knowledge in STM (i.e., normal phonotactic frequency and phonological similarity effects) but reduced support from semantic memory (i.e., poor immediate serial recall for semantically degraded words, characterised by frequent item errors). Next, we demonstrated normal learning of serial order information for repeated lists of single-digit number words using the Hebb paradigm: these items were well-understood allowing them to be repeated without frequent item errors. In contrast, patient GE showed little learning of nonsense syllable sequences using the same Hebb paradigm. Detailed analysis revealed that both GE and the controls showed a tendency to learn their own errors as opposed to the target items. Finally, we showed normal learning of phonological sequences for GE when he was prevented from repeating his errors. These findings confirm that the ATL atrophy in SD disrupts phonological processing for semantically degraded words but leaves the phonological architecture intact. Consequently, when item errors are minimised, phonological STM can support the acquisition of new phoneme

Delta Learning Rule for the Active Sites Model

OpenAIRE

Lingashetty, Krishna Chaithanya

2010-01-01

This paper reports the results on methods of comparing the memory retrieval capacity of the Hebbian neural network which implements the B-Matrix approach, by using the Widrow-Hoff rule of learning. We then, extend the recently proposed Active Sites model by developing a delta rule to increase memory capacity. Also, this paper extends the binary neural network to a multi-level (non-binary) neural network.

Learning a New Selection Rule in Visual and Frontal Cortex

NARCIS (Netherlands)

van der Togt, Chris; Stănişor, Liviu; Pooresmaeili, Arezoo; Albantakis, Larissa; Deco, Gustavo; Roelfsema, Pieter R

2016-01-01

How do you make a decision if you do not know the rules of the game? Models of sensory decision-making suggest that choices are slow if evidence is weak, but they may only apply if the subject knows the task rules. Here, we asked how the learning of a new rule influences neuronal activity in the

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

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Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

1988-01-01

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

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Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

2018-02-01

Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

Role of Prefrontal Cortex in Learning and Generalizing Hierarchical Rules in 8-Month-Old Infants.

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Werchan, Denise M; Collins, Anne G E; Frank, Michael J; Amso, Dima

2016-10-05

Recent research indicates that adults and infants spontaneously create and generalize hierarchical rule sets during incidental learning. Computational models and empirical data suggest that, in adults, this process is supported by circuits linking prefrontal cortex (PFC) with striatum and their modulation by dopamine, but the neural circuits supporting this form of learning in infants are largely unknown. We used near-infrared spectroscopy to record PFC activity in 8-month-old human infants during a simple audiovisual hierarchical-rule-learning task. Behavioral results confirmed that infants adopted hierarchical rule sets to learn and generalize spoken object-label mappings across different speaker contexts. Infants had increased activity over right dorsal lateral PFC when rule sets switched from one trial to the next, a neural marker related to updating rule sets into working memory in the adult literature. Infants' eye blink rate, a possible physiological correlate of striatal dopamine activity, also increased when rule sets switched from one trial to the next. Moreover, the increase in right dorsolateral PFC activity in conjunction with eye blink rate also predicted infants' generalization ability, providing exploratory evidence for frontostriatal involvement during learning. These findings provide evidence that PFC is involved in rudimentary hierarchical rule learning in 8-month-old infants, an ability that was previously thought to emerge later in life in concert with PFC maturation. Hierarchical rule learning is a powerful learning mechanism that allows rules to be selected in a context-appropriate fashion and transferred or reused in novel contexts. Data from computational models and adults suggests that this learning mechanism is supported by dopamine-innervated interactions between prefrontal cortex (PFC) and striatum. Here, we provide evidence that PFC also supports hierarchical rule learning during infancy, challenging the current dogma that PFC is an

Learning and innovative elements of strategy adoption rules expand cooperative network topologies.

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Wang, Shijun; Szalay, Máté S; Zhang, Changshui; Csermely, Peter

2008-04-09

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.

Ensemble learning with trees and rules: supervised, semi-supervised, unsupervised

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In this article, we propose several new approaches for post processing a large ensemble of conjunctive rules for supervised and semi-supervised learning problems. We show with various examples that for high dimensional regression problems the models constructed by the post processing the rules with ...

A supervised learning rule for classification of spatiotemporal spike patterns.

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Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

2016-08-01

This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

Serial-order learning impairment and hypersensitivity-to-interference in dyscalculia.

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De Visscher, Alice; Szmalec, Arnaud; Van Der Linden, Lize; Noël, Marie-Pascale

2015-11-01

In the context of heterogeneity, the different profiles of dyscalculia are still hypothetical. This study aims to link features of mathematical difficulties to certain potential etiologies. First, we wanted to test the hypothesis of a serial-order learning deficit in adults with dyscalculia. For this purpose we used a Hebb repetition learning task. Second, we wanted to explore a recent hypothesis according to which hypersensitivity-to-interference hampers the storage of arithmetic facts and leads to a particular profile of dyscalculia. We therefore used interfering and non-interfering repeated sequences in the Hebb paradigm. A final test was used to assess the memory trace of the non-interfering sequence and the capacity to manipulate it. In line with our predictions, we observed that people with dyscalculia who show good conceptual knowledge in mathematics but impaired arithmetic fluency suffer from increased sensitivity-to-interference compared to controls. Secondly, people with dyscalculia who show a deficit in a global mathematical test suffer from a serial-order learning deficit characterized by a slow learning and a quick degradation of the memory trace of the repeated sequence. A serial-order learning impairment could be one of the explanations for a basic numerical deficit, since it is necessary for the number-word sequence acquisition. Among the different profiles of dyscalculia, this study provides new evidence and refinement for two particular profiles. Copyright © 2015 Elsevier B.V. All rights reserved.

RuleML-Based Learning Object Interoperability on the Semantic Web

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Biletskiy, Yevgen; Boley, Harold; Ranganathan, Girish R.

2008-01-01

Purpose: The present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions. Design/methodology/approach: As proof of concept, a rule…

RULE-BASE METHOD FOR ANALYSIS OF QUALITY E-LEARNING IN HIGHER EDUCATION

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darsih darsih darsih

2016-04-01

Full Text Available ABSTRACT Assessing the quality of e-learning courses to measure the success of e-learning systems in online learning is essential. The system can be used to improve education. The study analyzes the quality of e-learning course on the web site www.kulon.undip.ac.id used a questionnaire with questions based on the variables of ISO 9126. Penilaiann Likert scale was used with a web app. Rule-base reasoning method is used to subject the quality of e-learningyang assessed. A case study conducted in four e-learning courses with 133 sample / respondents as users of the e-learning course. From the obtained results of research conducted both for the value of e-learning from each subject tested. In addition, each e-learning courses have different advantages depending on certain variables. Keywords : E-Learning, Rule-Base, Questionnaire, Likert, Measuring.

Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

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Haobo Lyu

2016-06-01

Full Text Available When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned from a given multi-temporal dataset to detect any other new target images without applying other learning processes. In this study, we consider the design of an efficient change rule having transferability to detect both binary and multi-class changes. The proposed method relies on an improved Long Short-Term Memory (LSTM model to acquire and record the change information of long-term sequence remote sensing data. In particular, a core memory cell is utilized to learn the change rule from the information concerning binary changes or multi-class changes. Three gates are utilized to control the input, output and update of the LSTM model for optimization. In addition, the learned rule can be applied to detect changes and transfer the change rule from one learned image to another new target multi-temporal image. In this study, binary experiments, transfer experiments and multi-class change experiments are exploited to demonstrate the superiority of our method. Three contributions of this work can be summarized as follows: (1 the proposed method can learn an effective change rule to provide reliable change information for multi-temporal images; (2 the learned change rule has good transferability for detecting changes in new target images without any extra learning process, and the new target images should have a multi-spectral distribution similar to that of the training images; and (3 to the authors’ best knowledge, this is the first time that deep learning in recurrent neural networks is exploited for change detection. In addition, under the framework of the proposed method, changes can be detected under both binary detection and multi-class change detection.

Effect of Neuroscience-Based Cognitive Skill Training on Growth of Cognitive Deficits Associated with Learning Disabilities in Children Grades 2-4

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Avtzon, Sarah Abitbol

2012-01-01

Working memory, executive functions, and cognitive processes associated with specific academic areas, are empirically identified as being the core underlying cognitive deficits in students with specific learning disabilities. Using Hebb's theory of neuroplasticity and the principle of automaticity as theoretical bases, this experimental study…

Compensatory Processing During Rule-Based Category Learning in Older Adults

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Bharani, Krishna L.; Paller, Ken A.; Reber, Paul J.; Weintraub, Sandra; Yanar, Jorge; Morrison, Robert G.

2016-01-01

Healthy older adults typically perform worse than younger adults at rule-based category learning, but better than patients with Alzheimer's or Parkinson's disease. To further investigate aging's effect on rule-based category learning, we monitored event-related potentials (ERPs) while younger and neuropsychologically typical older adults performed a visual category-learning task with a rule-based category structure and trial-by-trial feedback. Using these procedures, we previously identified ERPs sensitive to categorization strategy and accuracy in young participants. In addition, previous studies have demonstrated the importance of neural processing in the prefrontal cortex and the medial temporal lobe for this task. In this study, older adults showed lower accuracy and longer response times than younger adults, but there were two distinct subgroups of older adults. One subgroup showed near-chance performance throughout the procedure, never categorizing accurately. The other subgroup reached asymptotic accuracy that was equivalent to that in younger adults, although they categorized more slowly. These two subgroups were further distinguished via ERPs. Consistent with the compensation theory of cognitive aging, older adults who successfully learned showed larger frontal ERPs when compared with younger adults. Recruitment of prefrontal resources may have improved performance while slowing response times. Additionally, correlations of feedback-locked P300 amplitudes with category-learning accuracy differentiated successful younger and older adults. Overall, the results suggest that the ability to adapt one's behavior in response to feedback during learning varies across older individuals, and that the failure of some to adapt their behavior may reflect inadequate engagement of prefrontal cortex. PMID:26422522

Associative memory in an analog iterated-map neural network

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Marcus, C. M.; Waugh, F. R.; Westervelt, R. M.

1990-03-01

The behavior of an analog neural network with parallel dynamics is studied analytically and numerically for two associative-memory learning algorithms, the Hebb rule and the pseudoinverse rule. Phase diagrams in the parameter space of analog gain β and storage ratio α are presented. For both learning rules, the networks have large ``recall'' phases in which retrieval states exist and convergence to a fixed point is guaranteed by a global stability criterion. We also demonstrate numerically that using a reduced analog gain increases the probability of recall starting from a random initial state. This phenomenon is comparable to thermal annealing used to escape local minima but has the advantage of being deterministic, and therefore easily implemented in electronic hardware. Similarities and differences between analog neural networks and networks with two-state neurons at finite temperature are also discussed.

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

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Glaab, Enrico; Bacardit, Jaume; Garibaldi, Jonathan M; Krasnogor, Natalio

2012-01-01

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.

Rule-based category learning in children: the role of age and executive functioning.

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Rahel Rabi

Full Text Available Rule-based category learning was examined in 4-11 year-olds and adults. Participants were asked to learn a set of novel perceptual categories in a classification learning task. Categorization performance improved with age, with younger children showing the strongest rule-based deficit relative to older children and adults. Model-based analyses provided insight regarding the type of strategy being used to solve the categorization task, demonstrating that the use of the task appropriate strategy increased with age. When children and adults who identified the correct categorization rule were compared, the performance deficit was no longer evident. Executive functions were also measured. While both working memory and inhibitory control were related to rule-based categorization and improved with age, working memory specifically was found to marginally mediate the age-related improvements in categorization. When analyses focused only on the sample of children, results showed that working memory ability and inhibitory control were associated with categorization performance and strategy use. The current findings track changes in categorization performance across childhood, demonstrating at which points performance begins to mature and resemble that of adults. Additionally, findings highlight the potential role that working memory and inhibitory control may play in rule-based category learning.

Learning of Alignment Rules between Concept Hierarchies

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Ichise, Ryutaro; Takeda, Hideaki; Honiden, Shinichi

With the rapid advances of information technology, we are acquiring much information than ever before. As a result, we need tools for organizing this data. Concept hierarchies such as ontologies and information categorizations are powerful and convenient methods for accomplishing this goal, which have gained wide spread acceptance. Although each concept hierarchy is useful, it is difficult to employ multiple concept hierarchies at the same time because it is hard to align their conceptual structures. This paper proposes a rule learning method that inputs information from a source concept hierarchy and finds suitable location for them in a target hierarchy. The key idea is to find the most similar categories in each hierarchy, where similarity is measured by the κ(kappa) statistic that counts instances belonging to both categories. In order to evaluate our method, we conducted experiments using two internet directories: Yahoo! and LYCOS. We map information instances from the source directory into the target directory, and show that our learned rules agree with a human-generated assignment 76% of the time.

Learning of Rule Ensembles for Multiple Attribute Ranking Problems

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Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman; Szeląg, Marcin

In this paper, we consider the multiple attribute ranking problem from a Machine Learning perspective. We propose two approaches to statistical learning of an ensemble of decision rules from decision examples provided by the Decision Maker in terms of pairwise comparisons of some objects. The first approach consists in learning a preference function defining a binary preference relation for a pair of objects. The result of application of this function on all pairs of objects to be ranked is then exploited using the Net Flow Score procedure, giving a linear ranking of objects. The second approach consists in learning a utility function for single objects. The utility function also gives a linear ranking of objects. In both approaches, the learning is based on the boosting technique. The presented approaches to Preference Learning share good properties of the decision rule preference model and have good performance in the massive-data learning problems. As Preference Learning and Multiple Attribute Decision Aiding share many concepts and methodological issues, in the introduction, we review some aspects bridging these two fields. To illustrate the two approaches proposed in this paper, we solve with them a toy example concerning the ranking of a set of cars evaluated by multiple attributes. Then, we perform a large data experiment on real data sets. The first data set concerns credit rating. Since recent research in the field of Preference Learning is motivated by the increasing role of modeling preferences in recommender systems and information retrieval, we chose two other massive data sets from this area - one comes from movie recommender system MovieLens, and the other concerns ranking of text documents from 20 Newsgroups data set.

Effect of intermittent exposure to ethanol and MDMA during adolescence on learning and memory in adult mice

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Vidal-Infer Antonio

2012-06-01

Full Text Available Abstract Background Heavy binge drinking is increasingly frequent among adolescents, and consumption of 3,4-methylenedioxymethamphetamine (MDMA is often combined with ethanol (EtOH. The long-lasting effects of intermittent exposure to EtOH and MDMA during adolescence on learning and memory were evaluated in adult mice using the Hebb-Williams maze. Methods Adolescent OF1 mice were exposed to EtOH (1.25 g/kg on two consecutive days at 48-h intervals over a 14-day period (from PD 29 to 42. MDMA (10 or 20 mg/kg was injected twice daily at 4-h intervals over two consecutive days, and this schedule was repeated six days later (PD 33, 34, 41 and 42, resulting in a total of eight injections. Animals were initiated in the Hebb-Williams maze on PND 64. The concentration of brain monoamines in the striatum and hippocampus was then measured. Results At the doses employed, both EtOH and MDMA, administered alone or together, impaired learning in the Hebb-Williams maze, as treated animals required more time to reach the goal than their saline-treated counterparts. The groups treated during adolescence with EtOH, alone or plus MDMA, also presented longer latency scores and needed more trials to reach the acquisition criterion score. MDMA induced a decrease in striatal DA concentration, an effect that was augmented by the co-administration of EtOH. All the treatment groups displayed an imbalance in the interaction DA/serotonin. Conclusions The present findings indicate that the developing brain is highly vulnerable to the damaging effects of EtOH and/or MDMA, since mice receiving these drugs in a binge pattern during adolescence exhibit impaired learning and memory in adulthood.

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

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Enrico Glaab

Full Text Available Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.

Differential impact of relevant and irrelevant dimension primes on rule-based and information-integration category learning.

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Grimm, Lisa R; Maddox, W Todd

2013-11-01

Research has identified multiple category-learning systems with each being "tuned" for learning categories with different task demands and each governed by different neurobiological systems. Rule-based (RB) classification involves testing verbalizable rules for category membership while information-integration (II) classification requires the implicit learning of stimulus-response mappings. In the first study to directly test rule priming with RB and II category learning, we investigated the influence of the availability of information presented at the beginning of the task. Participants viewed lines that varied in length, orientation, and position on the screen, and were primed to focus on stimulus dimensions that were relevant or irrelevant to the correct classification rule. In Experiment 1, we used an RB category structure, and in Experiment 2, we used an II category structure. Accuracy and model-based analyses suggested that a focus on relevant dimensions improves RB task performance later in learning while a focus on an irrelevant dimension improves II task performance early in learning. © 2013.

Topic categorisation of statements in suicide notes with integrated rules and machine learning.

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Kovačević, Aleksandar; Dehghan, Azad; Keane, John A; Nenadic, Goran

2012-01-01

We describe and evaluate an automated approach used as part of the i2b2 2011 challenge to identify and categorise statements in suicide notes into one of 15 topics, including Love, Guilt, Thankfulness, Hopelessness and Instructions. The approach combines a set of lexico-syntactic rules with a set of models derived by machine learning from a training dataset. The machine learning models rely on named entities, lexical, lexico-semantic and presentation features, as well as the rules that are applicable to a given statement. On a testing set of 300 suicide notes, the approach showed the overall best micro F-measure of up to 53.36%. The best precision achieved was 67.17% when only rules are used, whereas best recall of 50.57% was with integrated rules and machine learning. While some topics (eg, Sorrow, Anger, Blame) prove challenging, the performance for relatively frequent (eg, Love) and well-scoped categories (eg, Thankfulness) was comparatively higher (precision between 68% and 79%), suggesting that automated text mining approaches can be effective in topic categorisation of suicide notes.

LTD windows of the STDP learning rule and synaptic connections having a large transmission delay enable robust sequence learning amid background noise.

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Hayashi, Hatsuo; Igarashi, Jun

2009-06-01

Spike-timing-dependent synaptic plasticity (STDP) is a simple and effective learning rule for sequence learning. However, synapses being subject to STDP rules are readily influenced in noisy circumstances because synaptic conductances are modified by pre- and postsynaptic spikes elicited within a few tens of milliseconds, regardless of whether those spikes convey information or not. Noisy firing existing everywhere in the brain may induce irrelevant enhancement of synaptic connections through STDP rules and would result in uncertain memory encoding and obscure memory patterns. We will here show that the LTD windows of the STDP rules enable robust sequence learning amid background noise in cooperation with a large signal transmission delay between neurons and a theta rhythm, using a network model of the entorhinal cortex layer II with entorhinal-hippocampal loop connections. The important element of the present model for robust sequence learning amid background noise is the symmetric STDP rule having LTD windows on both sides of the LTP window, in addition to the loop connections having a large signal transmission delay and the theta rhythm pacing activities of stellate cells. Above all, the LTD window in the range of positive spike-timing is important to prevent influences of noise with the progress of sequence learning.

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data.

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Pesesky, Mitchell W; Hussain, Tahir; Wallace, Meghan; Patel, Sanket; Andleeb, Saadia; Burnham, Carey-Ann D; Dantas, Gautam

2016-01-01

The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data

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Mitchell Pesesky

2016-11-01

Full Text Available The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitate initial use of empiric (frequently broad-spectrum antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0% and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance

Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices

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Lim, Suhwan; Bae, Jong-Ho; Eum, Jai-Ho; Lee, Sungtae; Kim, Chul-Heung; Kwon, Dongseok; Park, Byung-Gook; Lee, Jong-Ho

2017-01-01

In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron net...

A learning rule for very simple universal approximators consisting of a single layer of perceptrons.

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Auer, Peter; Burgsteiner, Harald; Maass, Wolfgang

2008-06-01

One may argue that the simplest type of neural networks beyond a single perceptron is an array of several perceptrons in parallel. In spite of their simplicity, such circuits can compute any Boolean function if one views the majority of the binary perceptron outputs as the binary output of the parallel perceptron, and they are universal approximators for arbitrary continuous functions with values in [0,1] if one views the fraction of perceptrons that output 1 as the analog output of the parallel perceptron. Note that in contrast to the familiar model of a "multi-layer perceptron" the parallel perceptron that we consider here has just binary values as outputs of gates on the hidden layer. For a long time one has thought that there exists no competitive learning algorithm for these extremely simple neural networks, which also came to be known as committee machines. It is commonly assumed that one has to replace the hard threshold gates on the hidden layer by sigmoidal gates (or RBF-gates) and that one has to tune the weights on at least two successive layers in order to achieve satisfactory learning results for any class of neural networks that yield universal approximators. We show that this assumption is not true, by exhibiting a simple learning algorithm for parallel perceptrons - the parallel delta rule (p-delta rule). In contrast to backprop for multi-layer perceptrons, the p-delta rule only has to tune a single layer of weights, and it does not require the computation and communication of analog values with high precision. Reduced communication also distinguishes our new learning rule from other learning rules for parallel perceptrons such as MADALINE. Obviously these features make the p-delta rule attractive as a biologically more realistic alternative to backprop in biological neural circuits, but also for implementations in special purpose hardware. We show that the p-delta rule also implements gradient descent-with regard to a suitable error measure

Finding Influential Users in Social Media Using Association Rule Learning

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Fredrik Erlandsson

2016-04-01

Full Text Available Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.

Hebbian errors in learning: an analysis using the Oja model.

Science.gov (United States)

Rădulescu, Anca; Cox, Kingsley; Adams, Paul

2009-06-21

Recent work on long term potentiation in brain slices shows that Hebb's rule is not completely synapse-specific, probably due to intersynapse diffusion of calcium or other factors. We previously suggested that such errors in Hebbian learning might be analogous to mutations in evolution. We examine this proposal quantitatively, extending the classical Oja unsupervised model of learning by a single linear neuron to include Hebbian inspecificity. We introduce an error matrix E, which expresses possible crosstalk between updating at different connections. When there is no inspecificity, this gives the classical result of convergence to the first principal component of the input distribution (PC1). We show the modified algorithm converges to the leading eigenvector of the matrix EC, where C is the input covariance matrix. In the most biologically plausible case when there are no intrinsically privileged connections, E has diagonal elements Q and off-diagonal elements (1-Q)/(n-1), where Q, the quality, is expected to decrease with the number of inputs n and with a synaptic parameter b that reflects synapse density, calcium diffusion, etc. We study the dependence of the learning accuracy on b, n and the amount of input activity or correlation (analytically and computationally). We find that accuracy increases (learning becomes gradually less useful) with increases in b, particularly for intermediate (i.e., biologically realistic) correlation strength, although some useful learning always occurs up to the trivial limit Q=1/n. We discuss the relation of our results to Hebbian unsupervised learning in the brain. When the mechanism lacks specificity, the network fails to learn the expected, and typically most useful, result, especially when the input correlation is weak. Hebbian crosstalk would reflect the very high density of synapses along dendrites, and inevitably degrades learning.

Fuzzy OLAP association rules mining-based modular reinforcement learning approach for multiagent systems.

Science.gov (United States)

Kaya, Mehmet; Alhajj, Reda

2005-04-01

Multiagent systems and data mining have recently attracted considerable attention in the field of computing. Reinforcement learning is the most commonly used learning process for multiagent systems. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are experienced much less than others, or some state-action pairs are never visited during the learning phase. Further, before completing the learning process, an agent cannot exhibit a certain behavior in some states that may be experienced sufficiently. In this study, we propose a novel multiagent learning approach to handle these problems. Our approach is based on utilizing the mining process for modular cooperative learning systems. It incorporates fuzziness and online analytical processing (OLAP) based mining to effectively process the information reported by agents. First, we describe a fuzzy data cube OLAP architecture which facilitates effective storage and processing of the state information reported by agents. This way, the action of the other agent, not even in the visual environment. of the agent under consideration, can simply be predicted by extracting online association rules, a well-known data mining technique, from the constructed data cube. Second, we present a new action selection model, which is also based on association rules mining. Finally, we generalize not sufficiently experienced states, by mining multilevel association rules from the proposed fuzzy data cube. Experimental results obtained on two different versions of a well-known pursuit domain show the robustness and effectiveness of the proposed fuzzy OLAP mining based modular learning approach. Finally, we tested the scalability of the approach presented in this paper and compared it with our previous work on modular-fuzzy Q-learning and ordinary Q-learning.

Genetic learning in rule-based and neural systems

Science.gov (United States)

Smith, Robert E.

1993-01-01

The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

Directory of Open Access Journals (Sweden)

Atif Shahzad

2016-02-01

Full Text Available A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment.

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music

Science.gov (United States)

Giraldo, Sergio I.; Ramirez, Rafael

2016-01-01

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music.

Science.gov (United States)

Giraldo, Sergio I; Ramirez, Rafael

2016-01-01

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules.

The ultrametric organization of memories in a neural network

International Nuclear Information System (INIS)

Parga, N.; Virasoro, M.A.

1985-08-01

In Hopfield's model of human memory the words to be stored must be orthogonal. From the point of view of human psychology this feature is unacceptable unless we reinterpret these words as primordial categories. But then one has to complete the model so as to be able to store a full hierarchical tree of categories embodying subcategories and so on. We use recent results on the spin glass mean field theories to show that this complementation can be done in a natural way with a minimal modification of Hebb's rule for learning. Categorization emerges naturally from an encoding stage structured in layers. (author)

Transcranial infrared laser stimulation improves rule-based, but not information-integration, category learning in humans.

Science.gov (United States)

Blanco, Nathaniel J; Saucedo, Celeste L; Gonzalez-Lima, F

2017-03-01

This is the first randomized, controlled study comparing the cognitive effects of transcranial laser stimulation on category learning tasks. Transcranial infrared laser stimulation is a new non-invasive form of brain stimulation that shows promise for wide-ranging experimental and neuropsychological applications. It involves using infrared laser to enhance cerebral oxygenation and energy metabolism through upregulation of the respiratory enzyme cytochrome oxidase, the primary infrared photon acceptor in cells. Previous research found that transcranial infrared laser stimulation aimed at the prefrontal cortex can improve sustained attention, short-term memory, and executive function. In this study, we directly investigated the influence of transcranial infrared laser stimulation on two neurobiologically dissociable systems of category learning: a prefrontal cortex mediated reflective system that learns categories using explicit rules, and a striatally mediated reflexive learning system that forms gradual stimulus-response associations. Participants (n=118) received either active infrared laser to the lateral prefrontal cortex or sham (placebo) stimulation, and then learned one of two category structures-a rule-based structure optimally learned by the reflective system, or an information-integration structure optimally learned by the reflexive system. We found that prefrontal rule-based learning was substantially improved following transcranial infrared laser stimulation as compared to placebo (treatment X block interaction: F(1, 298)=5.117, p=0.024), while information-integration learning did not show significant group differences (treatment X block interaction: F(1, 288)=1.633, p=0.202). These results highlight the exciting potential of transcranial infrared laser stimulation for cognitive enhancement and provide insight into the neurobiological underpinnings of category learning. Copyright © 2017 Elsevier Inc. All rights reserved.

The efficiency of the RULES-4 classification learning algorithm in predicting the density of agents

Directory of Open Access Journals (Sweden)

Ziad Salem

2014-12-01

Full Text Available Learning is the act of obtaining new or modifying existing knowledge, behaviours, skills or preferences. The ability to learn is found in humans, other organisms and some machines. Learning is always based on some sort of observations or data such as examples, direct experience or instruction. This paper presents a classification algorithm to learn the density of agents in an arena based on the measurements of six proximity sensors of a combined actuator sensor units (CASUs. Rules are presented that were induced by the learning algorithm that was trained with data-sets based on the CASU’s sensor data streams collected during a number of experiments with “Bristlebots (agents in the arena (environment”. It was found that a set of rules generated by the learning algorithm is able to predict the number of bristlebots in the arena based on the CASU’s sensor readings with satisfying accuracy.

Mixing Languages during Learning? Testing the One Subject—One Language Rule

Science.gov (United States)

2015-01-01

In bilingual communities, mixing languages is avoided in formal schooling: even if two languages are used on a daily basis for teaching, only one language is used to teach each given academic subject. This tenet known as the one subject-one language rule avoids mixing languages in formal schooling because it may hinder learning. The aim of this study was to test the scientific ground of this assumption by investigating the consequences of acquiring new concepts using a method in which two languages are mixed as compared to a purely monolingual method. Native balanced bilingual speakers of Basque and Spanish—adults (Experiment 1) and children (Experiment 2)—learnt new concepts by associating two different features to novel objects. Half of the participants completed the learning process in a multilingual context (one feature was described in Basque and the other one in Spanish); while the other half completed the learning phase in a purely monolingual context (both features were described in Spanish). Different measures of learning were taken, as well as direct and indirect indicators of concept consolidation. We found no evidence in favor of the non-mixing method when comparing the results of two groups in either experiment, and thus failed to give scientific support for the educational premise of the one subject—one language rule. PMID:26107624

Mixing Languages during Learning? Testing the One Subject-One Language Rule.

Directory of Open Access Journals (Sweden)

Eneko Antón

Full Text Available In bilingual communities, mixing languages is avoided in formal schooling: even if two languages are used on a daily basis for teaching, only one language is used to teach each given academic subject. This tenet known as the one subject-one language rule avoids mixing languages in formal schooling because it may hinder learning. The aim of this study was to test the scientific ground of this assumption by investigating the consequences of acquiring new concepts using a method in which two languages are mixed as compared to a purely monolingual method. Native balanced bilingual speakers of Basque and Spanish-adults (Experiment 1 and children (Experiment 2-learnt new concepts by associating two different features to novel objects. Half of the participants completed the learning process in a multilingual context (one feature was described in Basque and the other one in Spanish; while the other half completed the learning phase in a purely monolingual context (both features were described in Spanish. Different measures of learning were taken, as well as direct and indirect indicators of concept consolidation. We found no evidence in favor of the non-mixing method when comparing the results of two groups in either experiment, and thus failed to give scientific support for the educational premise of the one subject-one language rule.

Electronic conductivity of Ce(0.9)Gd(0.1)O(1.95-δ) and Ce(0.8)Pr(0.2)O(2-δ): Hebb-Wagner polarisation in the case of redox active dopants and interference

DEFF Research Database (Denmark)

Chatzichristodoulou, Christodoulos; Hendriksen, Peter Vang

2011-01-01

of the steady state I-V curve from the standard Hebb-Wagner equation was observed for the case of Ce(0.8)Pr(0.2)O(2-δ). It is shown that the I-V curve can be successfully reproduced when the presence of the redox active dopant, Pr(3+)/Pr(4+), is taken into account, whereas even better agreement can be reached...

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music

Directory of Open Access Journals (Sweden)

Sergio Ivan Giraldo

2016-12-01

Full Text Available Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1 quantitatively evaluate the accuracy of the induced models, (2 analyse the relative importance of the considered musical features, (3 discuss some of the learnt expressive performance rules in the context of previous work, and (4 assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules’ performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the

The Aptitude-Treatment Interaction Effects on the Learning of Grammar Rules

Science.gov (United States)

Hwu, Fenfang; Sun, Shuyan

2012-01-01

The present study investigates the interaction between two types of explicit instructional approaches, deduction and explicit-induction, and the level of foreign language aptitude in the learning of grammar rules. Results indicate that on the whole the two equally explicit instructional approaches did not differentially affect learning…

Effects of the Memorization of Rule Statements on Performance, Retention, and Transfer in a Computer-Based Learning Task.

Science.gov (United States)

Towle, Nelson J.

Research sought to determine whether memorization of rule statements before, during or after instruction in rule application skills would facilitate the acquisition and/or retention of rule-governed behavior as compared to no-rule statement memorization. A computer-assisted instructional (CAI) program required high school students to learn to a…

A SEMI-AUTOMATIC RULE SET BUILDING METHOD FOR URBAN LAND COVER CLASSIFICATION BASED ON MACHINE LEARNING AND HUMAN KNOWLEDGE

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H. Y. Gu

2017-09-01

Full Text Available Classification rule set is important for Land Cover classification, which refers to features and decision rules. The selection of features and decision are based on an iterative trial-and-error approach that is often utilized in GEOBIA, however, it is time-consuming and has a poor versatility. This study has put forward a rule set building method for Land cover classification based on human knowledge and machine learning. The use of machine learning is to build rule sets effectively which will overcome the iterative trial-and-error approach. The use of human knowledge is to solve the shortcomings of existing machine learning method on insufficient usage of prior knowledge, and improve the versatility of rule sets. A two-step workflow has been introduced, firstly, an initial rule is built based on Random Forest and CART decision tree. Secondly, the initial rule is analyzed and validated based on human knowledge, where we use statistical confidence interval to determine its threshold. The test site is located in Potsdam City. We utilised the TOP, DSM and ground truth data. The results show that the method could determine rule set for Land Cover classification semi-automatically, and there are static features for different land cover classes.

Statistical learning of music- and language-like sequences and tolerance for spectral shifts.

Science.gov (United States)

Daikoku, Tatsuya; Yatomi, Yutaka; Yumoto, Masato

2015-02-01

In our previous study (Daikoku, Yatomi, & Yumoto, 2014), we demonstrated that the N1m response could be a marker for the statistical learning process of pitch sequence, in which each tone was ordered by a Markov stochastic model. The aim of the present study was to investigate how the statistical learning of music- and language-like auditory sequences is reflected in the N1m responses based on the assumption that both language and music share domain generality. By using vowel sounds generated by a formant synthesizer, we devised music- and language-like auditory sequences in which higher-ordered transitional rules were embedded according to a Markov stochastic model by controlling fundamental (F0) and/or formant frequencies (F1-F2). In each sequence, F0 and/or F1-F2 were spectrally shifted in the last one-third of the tone sequence. Neuromagnetic responses to the tone sequences were recorded from 14 right-handed normal volunteers. In the music- and language-like sequences with pitch change, the N1m responses to the tones that appeared with higher transitional probability were significantly decreased compared with the responses to the tones that appeared with lower transitional probability within the first two-thirds of each sequence. Moreover, the amplitude difference was even retained within the last one-third of the sequence after the spectral shifts. However, in the language-like sequence without pitch change, no significant difference could be detected. The pitch change may facilitate the statistical learning in language and music. Statistically acquired knowledge may be appropriated to process altered auditory sequences with spectral shifts. The relative processing of spectral sequences may be a domain-general auditory mechanism that is innate to humans. Copyright © 2014 Elsevier Inc. All rights reserved.

Optimal monetary policy rules: the problem of stability under heterogeneous learning

Czech Academy of Sciences Publication Activity Database

Bogomolova, Anna; Kolyuzhnov, Dmitri

-, č. 379 (2008), s. 1-34 ISSN 1211-3298 R&D Projects: GA MŠk LC542 Institutional research plan: CEZ:AV0Z70850503 Keywords : monetary policy rules * New Keynesian model * adaptive learning Subject RIV: AH - Economics http://www.cerge-ei.cz/pdf/wp/Wp379.pdf

Lessons learned from the Maintenance Rule implementation at Northeast Utilities operating plants

International Nuclear Information System (INIS)

Hastings, K.B.; Khalil, Y.F.; Johnson, W.

1996-01-01

The Maintenance Rule as described in 10CFR50.65 requires holders of all operating nuclear power plants to monitor the performance of structures, systems, and components (SSCs) against licensee-established performance criteria. The Industry with the assistance of the Nuclear Energy Institute (NEI) developed a guideline, which includes all parts of the Maintenance Rule, to establish these performance criteria while incorporating safety and reliability of the operating plants. The NUMARC 93-01 Guideline introduced the term ''Risk Significant'' to categorize subsets of the SSCs which would require increased focus, from a Maintenance Rule perspective, in setting their performance criteria. Northeast Utilities Company (NU) operates five nuclear plants three at Millstone Station in Waterford, Connecticut; the Connecticut Yankee plant in Haddam Neck, Connecticut; and the Seabrook Station in Seabrook, New Hampshire. NU started the implementation process of the Maintenance Rule program at its five operating plants since early 1994, and have identified a population of risk significant SSCs at each plant. Recently, Northeast Utilities' Maintenance Rule Team re-examined the initial risk significant determinations to further refine these populations, and to establish consistencies among its operating units. As a result of the re-examination process, a number of inconsistencies and areas for improvement have been identified. The lessons learned provide valuable insights to consider in the future as one implements more risk based initiatives such as Graded QA and Risk-Based ISI and IST. This paper discusses the risk significance criteria, how Northeast Utilities utilized NUMARC 93-01 Guideline to determine the risk significant SSCs for its operating plants, and lessons learned. The results provided here do not include the Seabrook Station

Effects of neonatal inferior prefrontal and medial temporal lesions on learning the rule for delayed nonmatching-to-sample.

Science.gov (United States)

Málková, L; Bachevalier, J; Webster, M; Mishkin, M

2000-01-01

The ability of rhesus monkeys to master the rule for delayed nonmatching-to-sample (DNMS) has a protracted ontogenetic development, reaching adult levels of proficiency around 4 to 5 years of age (Bachevalier, 1990). To test the possibility that this slow development could be due, at least in part, to immaturity of the prefrontal component of a temporo-prefrontal circuit important for DNMS rule learning (Kowalska, Bachevalier, & Mishkin, 1991; Weinstein, Saunders, & Mishkin, 1988), monkeys with neonatal lesions of the inferior prefrontal convexity were compared on DNMS with both normal controls and animals given neonatal lesions of the medial temporal lobe. Consistent with our previous results (Bachevalier & Mishkin, 1994; Málková, Mishkin, & Bachevalier, 1995), the neonatal medial temporal lesions led to marked impairment in rule learning (as well as in recognition memory with long delays and list lengths) at both 3 months and 2 years of age. By contrast, the neonatal inferior convexity lesions yielded no impairment in rule-learning at 3 months and only a mild impairment at 2 years, a finding that also contrasts sharply with the marked effects of the same lesion made in adulthood. This pattern of sparing closely resembles the one found earlier after neonatal lesions to the cortical visual area TE (Bachevalier & Mishkin, 1994; Málková et al., 1995). The functional sparing at 3 months probably reflects the fact that the temporo-prefrontal circuit is nonfunctional at this early age, resulting in a total dependency on medial temporal contributions to rule learning. With further development, however, this circuit begins to provide a supplementary route for learning.

Enhancement of learning capacity and cholinergic synaptic function by carnitine in aging rats.

Science.gov (United States)

Ando, S; Tadenuma, T; Tanaka, Y; Fukui, F; Kobayashi, S; Ohashi, Y; Kawabata, T

2001-10-15

The effects of a carnitine derivative, acetyl-L-carnitine (ALCAR), on the cognitive and cholinergic activities of aging rats were examined. Rats were given ALCAR (100 mg/kg) per os for 3 months and were subjected to the Hebb-Williams tasks and a new maze task, AKON-1, to assess their learning capacity. The learning capacity of the ALCAR-treated group was superior to that of the control. Cholinergic activities were determined with synaptosomes isolated from the cortices. The high-affinity choline uptake by synaptosomes, acetylcholine synthesis in synaptosomes, and acetylcholine release from synaptosomes on membrane depolarization were all enhanced in the ALCAR group. This study indicates that chronic administration of ALCAR increases cholinergic synaptic transmission and consequently enhances learning capacity as a cognitive function in aging rats. Copyright 2001 Wiley-Liss, Inc.

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.

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Alireza Alemi

2015-08-01

Full Text Available Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the

Monetary Policy Rules, Learning and Stability: a Survey of the Recent Literature (In French)

OpenAIRE

Martin ZUMPE (GREThA UMR CNRS 5113)

2010-01-01

This paper presents the literature about econometric learning and its impact on the performances of monetary policy rules in the framework of the new canonical macroeconomic model. Rational expectations which are a building block of the original model can thus be replaced by expectations based on estimation algorithms. The permanent updating of these estimations can be interpreted as a learning proces of the model’s agents. This learning proces induces additional dynamics into the model. The ...

Rule-bases construction through self-learning for a table-based Sugeno-Takagi fuzzy logic control system

Directory of Open Access Journals (Sweden)

C. Boldisor

2009-12-01

Full Text Available A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC is presented and verified, aiming to engage intelligent characteristics to a fuzzy logic control systems. The methodology is a simplified version of those presented in today literature. Some aspects are intentionally ignored since it rarely appears in control system engineering and a SISO process is considered here. The fuzzy inference system obtained is a table-based Sugeno-Takagi type. System’s desired performance is defined by a reference model and rules are extracted from recorded data, after the correct control actions are learned. The presented algorithm is tested in constructing the rule-base of a fuzzy controller for a DC drive application. System’s performances and method’s viability are analyzed.

Learning "Rules" of Practice within the Context of the Practicum Triad: A Case Study of Learning to Teach

Science.gov (United States)

Chalies, Sebastien; Escalie, Guillaume; Stefano, Bertone; Clarke, Anthony

2012-01-01

This case study sought to determine the professional development circumstances in which a preservice teacher learned rules of practice (Wittgenstein, 1996) on practicum while interacting with a cooperating teacher and university supervisor. Borrowing from a theoretical conceptualization of teacher professional development based on the postulates…

Criterial noise effects on rule-based category learning: the impact of delayed feedback.

Science.gov (United States)

Ell, Shawn W; Ing, A David; Maddox, W Todd

2009-08-01

Variability in the representation of the decision criterion is assumed in many category-learning models, yet few studies have directly examined its impact. On each trial, criterial noise should result in drift in the criterion and will negatively impact categorization accuracy, particularly in rule-based categorization tasks, where learning depends on the maintenance and manipulation of decision criteria. In three experiments, we tested this hypothesis and examined the impact of working memory on slowing the drift rate. In Experiment 1, we examined the effect of drift by inserting a 5-sec delay between the categorization response and the delivery of corrective feedback, and working memory demand was manipulated by varying the number of decision criteria to be learned. Delayed feedback adversely affected performance, but only when working memory demand was high. In Experiment 2, we built on a classic finding in the absolute identification literature and demonstrated that distributing the criteria across multiple dimensions decreases the impact of drift during the delay. In Experiment 3, we confirmed that the effect of drift during the delay is moderated by working memory. These results provide important insights into the interplay between criterial noise and working memory, as well as providing important constraints for models of rule-based category learning.

Visual perceptual learning by operant conditioning training follows rules of contingency

Science.gov (United States)

Kim, Dongho; Seitz, Aaron R; Watanabe, Takeo

2015-01-01

Visual perceptual learning (VPL) can occur as a result of a repetitive stimulus-reward pairing in the absence of any task. This suggests that rules that guide Conditioning, such as stimulus-reward contingency (e.g. that stimulus predicts the likelihood of reward), may also guide the formation of VPL. To address this question, we trained subjects with an operant conditioning task in which there were contingencies between the response to one of three orientations and the presence of reward. Results showed that VPL only occurred for positive contingencies, but not for neutral or negative contingencies. These results suggest that the formation of VPL is influenced by similar rules that guide the process of Conditioning. PMID:26028984

Visual perceptual learning by operant conditioning training follows rules of contingency.

Science.gov (United States)

Kim, Dongho; Seitz, Aaron R; Watanabe, Takeo

2015-01-01

Visual perceptual learning (VPL) can occur as a result of a repetitive stimulus-reward pairing in the absence of any task. This suggests that rules that guide Conditioning, such as stimulus-reward contingency (e.g. that stimulus predicts the likelihood of reward), may also guide the formation of VPL. To address this question, we trained subjects with an operant conditioning task in which there were contingencies between the response to one of three orientations and the presence of reward. Results showed that VPL only occurred for positive contingencies, but not for neutral or negative contingencies. These results suggest that the formation of VPL is influenced by similar rules that guide the process of Conditioning.

Aberrant learning in Parkinson's disease: A neurocomputational study on bradykinesia.

Science.gov (United States)

Ursino, Mauro; Baston, Chiara

2018-05-22

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a progressive decline in motor functions, such as bradykinesia, caused by the pathological denervation of nigrostriatal dopaminergic neurons within the basal ganglia (BG). It is acknowledged that dopamine (DA) directly affects the modulatory role of BG towards the cortex. However, a growing body of literature is suggesting that DA-induced aberrant synaptic plasticity could play a role in the core symptoms of PD, thus recalling for a "reconceptualization" of the pathophysiology. The aim of this work was to investigate DA-driven aberrant learning as a concurrent cause of bradykinesia, using a comprehensive, biologically inspired neurocomputational model of action selection in the BG. The model includes the three main pathways operating in the BG circuitry, that is the direct, indirect and hyperdirect pathways, and use a two-term Hebb rule to train synapses in the striatum, based on previous history of rewards and punishments. Levodopa pharmacodynamics is also incorporated. Through model simulations of the Alternate Finger Tapping motor task, we assessed the role of aberrant learning on bradykinesia. The results show that training under drug medication (levodopa) provides not only immediate but also delayed benefit lasting in time. Conversely, if performed in conditions of vanishing levodopa efficacy, training may result in dysfunctional corticostriatal synaptic plasticity, further worsening motor performances in PD subjects. This suggests that bradykinesia may result from the concurrent effects of low DA levels and dysfunctional plasticity and that training can be exploited in medicated subjects to improve levodopa treatment. © 2018 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Building machines that learn and think like people.

Science.gov (United States)

Lake, Brenden M; Ullman, Tomer D; Tenenbaum, Joshua B; Gershman, Samuel J

2017-01-01

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

Chargaff's "Grammar of Biology": New Fractal-like Rules

OpenAIRE

Yamagishi, Michel Eduardo Beleza; Herai, Roberto H.

2011-01-01

Chargaff once said that "I saw before me in dark contours the beginning of a grammar of Biology". In linguistics, "grammar" is the set of natural language rules, but we do not know for sure what Chargaff meant by "grammar" of Biology. Nevertheless, assuming the metaphor, Chargaff himself started a "grammar of Biology" discovering the so called Chargaff's rules. In this work, we further develop his grammar. Using new concepts, we were able to discovery new genomic rules that seem to be invaria...

Energy spectra of the hyperbolic and second Poeschl-Teller like potentials solved by new exact quantization rule

International Nuclear Information System (INIS)

Dong Shihai; Gonzalez-Cisneros, A.

2008-01-01

A new exact quantization rule simplifies the calculation of the energy levels for the exactly solvable quantum system. In this work we calculate the energy levels of the Schroedinger equation with the hyperbolic potential by this quantization rule. The corresponding eigenfunction is also derived for completeness. The second Poeschl-Teller like potential case is also carried out

Learning the rules of the rock-paper-scissors game: chimpanzees versus children.

Science.gov (United States)

Gao, Jie; Su, Yanjie; Tomonaga, Masaki; Matsuzawa, Tetsuro

2018-01-01

The present study aimed to investigate whether chimpanzees (Pan troglodytes) could learn a transverse pattern by being trained in the rules of the rock-paper-scissors game in which "paper" beats "rock," "rock" beats "scissors," and "scissors" beats "paper." Additionally, this study compared the learning processes between chimpanzees and children. Seven chimpanzees were tested using a computer-controlled task. They were trained to choose the stronger of two options according to the game rules. The chimpanzees first engaged in the paper-rock sessions until they reached the learning criterion. Subsequently, they engaged in the rock-scissors and scissors-paper sessions, before progressing to sessions with all three pairs mixed. Five of the seven chimpanzees completed training after a mean of 307 sessions, which indicates that they learned the circular pattern. The chimpanzees required more scissors-paper sessions (14.29 ± 6.89), the third learnt pair, than paper-rock (1.71 ± 0.18) and rock-scissors (3.14 ± 0.70) sessions, suggesting they had difficulty finalizing the circularity. The chimpanzees then received generalization tests using new stimuli, which they learned quickly. A similar procedure was performed with children (35-71 months, n = 38) who needed the same number of trials for all three pairs during single-paired sessions. Their accuracy during the mixed-pair sessions improved with age and was better than chance from 50 months of age, which indicates that the ability to solve the transverse patterning problem might develop at around 4 years of age. The present findings show that chimpanzees were able to learn the task but had difficulties with circularity, whereas children learned the task more easily and developed the relevant ability at approximately 4 years of age. Furthermore, the chimpanzees' performance during the mixed-pair sessions was similar to that of 4-year-old children during the corresponding stage of training.

Videogame-Like Applications to Enhance Autonomous Learning

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Berns, Anke; Valero-Franco, Concepción

2013-01-01

This paper presents the results of an ongoing study which has been carried out with a group of German Foreign Language students at the University of Cadiz since 2012. The purpose of the study was to analyze the impact of videogame-like applications on foreign language learning and their motivational potential to increase learning beyond the…

Learning of Grammar-Like Visual Sequences by Adults with and without Language-Learning Disabilities

Science.gov (United States)

Aguilar, Jessica M.; Plante, Elena

2014-01-01

Purpose: Two studies examined learning of grammar-like visual sequences to determine whether a general deficit in statistical learning characterizes this population. Furthermore, we tested the hypothesis that difficulty in sustaining attention during the learning task might account for differences in statistical learning. Method: In Study 1,…

Sequence-specific procedural learning deficits in children with specific language impairment.

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Hsu, Hsinjen Julie; Bishop, Dorothy V M

2014-05-01

This study tested the procedural deficit hypothesis of specific language impairment (SLI) by comparing children's performance in two motor procedural learning tasks and an implicit verbal sequence learning task. Participants were 7- to 11-year-old children with SLI (n = 48), typically developing age-matched children (n = 20) and younger typically developing children matched for receptive grammar (n = 28). In a serial reaction time task, the children with SLI performed at the same level as the grammar-matched children, but poorer than age-matched controls in learning motor sequences. When tested with a motor procedural learning task that did not involve learning sequential relationships between discrete elements (i.e. pursuit rotor), the children with SLI performed comparably with age-matched children and better than younger grammar-matched controls. In addition, poor implicit learning of word sequences in a verbal memory task (the Hebb effect) was found in the children with SLI. Together, these findings suggest that SLI might be characterized by deficits in learning sequence-specific information, rather than generally weak procedural learning. © 2014 The Authors. Developmental Science Published by John Wiley & Sons Ltd.

Using Machine Learning Methods Jointly to Find Better Set of Rules in Data Mining

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SUG Hyontai

2017-01-01

Full Text Available Rough set-based data mining algorithms are one of widely accepted machine learning technologies because of their strong mathematical background and capability of finding optimal rules based on given data sets only without room for prejudiced views to be inserted on the data. But, because the algorithms find rules very precisely, we may confront with the overfitting problem. On the other hand, association rule algorithms find rules of association, where the association resides between sets of items in database. The algorithms find itemsets that occur more than given minimum support, so that they can find the itemsets practically in reasonable time even for very large databases by supplying the minimum support appropriately. In order to overcome the problem of the overfitting problem in rough set-based algorithms, first we find large itemsets, after that we select attributes that cover the large itemsets. By using the selected attributes only, we may find better set of rules based on rough set theory. Results from experiments support our suggested method.

Knowledge Sharing Practice in a Play-Like Learning Environment

DEFF Research Database (Denmark)

Benjaminsen, Nana

2007-01-01

The topic of this paper is play-like learning as it occurs when technology based learning environments is invited into the classroom. Observations of 5th grade classes playing with Lego Robolab, is used to illustrate that different ways of learning becomes visible when digital technology...

Social inference and social anxiety: evidence of a fear-congruent self-referential learning bias.

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Button, Katherine S; Browning, Michael; Munafò, Marcus R; Lewis, Glyn

2012-12-01

Fears of negative evaluation characterise social anxiety, and preferential processing of fear-relevant information is implicated in maintaining symptoms. Little is known, however, about the relationship between social anxiety and the process of inferring negative evaluation. The ability to use social information to learn what others think about one, referred to here as self-referential learning, is fundamental for effective social interaction. The aim of this research was to examine whether social anxiety is associated with self-referential learning. 102 Females with either high (n = 52) or low (n = 50) self-reported social anxiety completed a novel probabilistic social learning task. Using trial and error, the task required participants to learn two self-referential rules, 'I am liked' and 'I am disliked'. Participants across the sample were better at learning the positive rule 'I am liked' than the negative rule 'I am disliked', β = -6.4, 95% CI [-8.0, -4.7], p learning positive self-referential information was strongest in the lowest socially anxious and was abolished in the most symptomatic participants. Relative to the low group, the high anxiety group were better at learning they were disliked and worse at learning they were liked, social anxiety by rule interaction β = 3.6; 95% CI [+0.3, +7.0], p = 0.03. The specificity of the results to self-referential processing requires further research. Healthy individuals show a robust preference for learning that they are liked relative to disliked. This positive self-referential bias is reduced in social anxiety in a way that would be expected to exacerbate anxiety symptoms. Copyright © 2012 Elsevier Ltd. All rights reserved.

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients.

Science.gov (United States)

Cangelosi, Davide; Blengio, Fabiola; Versteeg, Rogier; Eggert, Angelika; Garaventa, Alberto; Gambini, Claudio; Conte, Massimo; Eva, Alessandra; Muselli, Marco; Varesio, Luigi

2013-01-01

Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting. Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging. The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients

Science.gov (United States)

2013-01-01

Background Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting. Results Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging. Conclusions The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four

Collaborative Working e-Learning Environments Supported by Rule-Based e-Tutor

Directory of Open Access Journals (Sweden)

Salaheddin Odeh

2007-10-01

Full Text Available Collaborative working environments for distance education sets a goal of convenience and an adaptation into our technologically advanced societies. To achieve this revolutionary new way of learning, environments must allow the different participants to communicate and coordinate with each other in a productive manner. Productivity and efficiency is obtained through synchronized communication between the different coordinating partners, which means that multiple users can execute an experiment simultaneously. Within this process, coordination can be accomplished by voice communication and chat tools. In recent times, multi-user environments have been successfully applied in many applications such as air traffic control systems, team-oriented military systems, chat text tools, and multi-player games. Thus, understanding the ideas and the techniques behind these systems can be of great significance regarding the contribution of newer ideas to collaborative working e-learning environments. However, many problems still exist in distance learning and tele-education, such as not finding the proper assistance while performing the remote experiment. Therefore, the students become overwhelmed and the experiment will fail. In this paper, we are going to discuss a solution that enables students to obtain an automated help by either a human tutor or a rule-based e-tutor (embedded rule-based system for the purpose of student support in complex remote experimentative environments. The technical implementation of the system can be realized by using the powerful Microsoft .NET, which offers a complete integrated developmental environment (IDE with a wide collection of products and technologies. Once the system is developed, groups of students are independently able to coordinate and to execute the experiment at any time and from any place, organizing the work between them positively.

Children's Reasoning about Self-Presentation Following Rule Violations: The Role of Self-Focused Attention

Science.gov (United States)

Banerjee, Robin; Bennett, Mark; Luke, Nikki

2012-01-01

Rule violations are likely to serve as key contexts for learning to reason about public identity. In an initial study with 91 children aged 4-9 years, social emotions and self-presentational concerns were more likely to be cited when children were responding to hypothetical vignettes involving social-conventional rather than moral violations. In 2…

Targeted training of the decision rule benefits rule-guided behavior in Parkinson's disease.

Science.gov (United States)

Ell, Shawn W

2013-12-01

The impact of Parkinson's disease (PD) on rule-guided behavior has received considerable attention in cognitive neuroscience. The majority of research has used PD as a model of dysfunction in frontostriatal networks, but very few attempts have been made to investigate the possibility of adapting common experimental techniques in an effort to identify the conditions that are most likely to facilitate successful performance. The present study investigated a targeted training paradigm designed to facilitate rule learning and application using rule-based categorization as a model task. Participants received targeted training in which there was no selective-attention demand (i.e., stimuli varied along a single, relevant dimension) or nontargeted training in which there was selective-attention demand (i.e., stimuli varied along a relevant dimension as well as an irrelevant dimension). Following training, all participants were tested on a rule-based task with selective-attention demand. During the test phase, PD patients who received targeted training performed similarly to control participants and outperformed patients who did not receive targeted training. As a preliminary test of the generalizability of the benefit of targeted training, a subset of the PD patients were tested on the Wisconsin card sorting task (WCST). PD patients who received targeted training outperformed PD patients who did not receive targeted training on several WCST performance measures. These data further characterize the contribution of frontostriatal circuitry to rule-guided behavior. Importantly, these data also suggest that PD patient impairment, on selective-attention-demanding tasks of rule-guided behavior, is not inevitable and highlight the potential benefit of targeted training.

A Voltage-Based STDP Rule Combined with Fast BCM-Like Metaplasticity Accounts for LTP and Concurrent "Heterosynaptic" LTD in the Dentate Gyrus In Vivo.

Directory of Open Access Journals (Sweden)

Peter Jedlicka

2015-11-01

Full Text Available Long-term potentiation (LTP and long-term depression (LTD are widely accepted to be synaptic mechanisms involved in learning and memory. It remains uncertain, however, which particular activity rules are utilized by hippocampal neurons to induce LTP and LTD in behaving animals. Recent experiments in the dentate gyrus of freely moving rats revealed an unexpected pattern of LTP and LTD from high-frequency perforant path stimulation. While 400 Hz theta-burst stimulation (400-TBS and 400 Hz delta-burst stimulation (400-DBS elicited substantial LTP of the tetanized medial path input and, concurrently, LTD of the non-tetanized lateral path input, 100 Hz theta-burst stimulation (100-TBS, a normally efficient LTP protocol for in vitro preparations produced only weak LTP and concurrent LTD. Here we show in a biophysically realistic compartmental granule cell model that this pattern of results can be accounted for by a voltage-based spike-timing-dependent plasticity (STDP rule combined with a relatively fast Bienenstock-Cooper-Munro (BCM-like homeostatic metaplasticity rule, all on a background of ongoing spontaneous activity in the input fibers. Our results suggest that, at least for dentate granule cells, the interplay of STDP-BCM plasticity rules and ongoing pre- and postsynaptic background activity determines not only the degree of input-specific LTP elicited by various plasticity-inducing protocols, but also the degree of associated LTD in neighboring non-tetanized inputs, as generated by the ongoing constitutive activity at these synapses.

Applying cognitive developmental psychology to middle school physics learning: The rule assessment method

Science.gov (United States)

Hallinen, Nicole R.; Chi, Min; Chin, Doris B.; Prempeh, Joe; Blair, Kristen P.; Schwartz, Daniel L.

2013-01-01

Cognitive developmental psychology often describes children's growing qualitative understanding of the physical world. Physics educators may be able to use the relevant methods to advantage for characterizing changes in students' qualitative reasoning. Siegler developed the "rule assessment" method for characterizing levels of qualitative understanding for two factor situations (e.g., volume and mass for density). The method assigns children to rule levels that correspond to the degree they notice and coordinate the two factors. Here, we provide a brief tutorial plus a demonstration of how we have used this method to evaluate instructional outcomes with middle-school students who learned about torque, projectile motion, and collisions using different instructional methods with simulations.

Learning and liking of melody and harmony: further studies in artificial grammar learning.

Science.gov (United States)

Loui, Psyche

2012-10-01

Much of what we know and love about music is based on implicitly acquired mental representations of musical pitches and the relationships between them. While previous studies have shown that these mental representations of music can be acquired rapidly and can influence preference, it is still unclear which aspects of music influence learning and preference formation. This article reports two experiments that use an artificial musical system to examine two questions: (1) which aspects of music matter most for learning, and (2) which aspects of music matter most for preference formation. Two aspects of music are tested: melody and harmony. In Experiment 1 we tested the learning and liking of a new musical system that is manipulated melodically so that only some of the possible conditional probabilities between successive notes are presented. In Experiment 2 we administered the same tests for learning and liking, but we used a musical system that is manipulated harmonically to eliminate the property of harmonic whole-integer ratios between pitches. Results show that disrupting melody (Experiment 1) disabled the learning of music without disrupting preference formation, whereas disrupting harmony (Experiment 2) does not affect learning and memory but disrupts preference formation. Results point to a possible dissociation between learning and preference in musical knowledge. Copyright © 2012 Cognitive Science Society, Inc.

Toward A Dual-Learning Systems Model of Speech Category Learning

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Bharath eChandrasekaran

2014-07-01

Full Text Available More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article we describe a neurobiologically-constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. We find an age related deficit in reflective-optimal but not reflexive-optimal auditory category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, uni-dimensional rules to more complex, reflexive, multi-dimensional rules. In a second application we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

Beyond Motivation - History as a method for the learning of meta-discursive rules in mathematics

DEFF Research Database (Denmark)

Kjeldsen, Tinne Hoff; Blomhøj, Morten

2012-01-01

In this paper, we argue that history might have a profound role to play for learning mathematics by providing a self-evident (if not indispensable) strategy for revealing meta-discursive rules in mathematics and turning them into explicit objects of reflection for students. Our argument is based...

Infants learn better from left to right: a directional bias in infants' sequence learning.

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Bulf, Hermann; de Hevia, Maria Dolores; Gariboldi, Valeria; Macchi Cassia, Viola

2017-05-26

A wealth of studies show that human adults map ordered information onto a directional spatial continuum. We asked whether mapping ordinal information into a directional space constitutes an early predisposition, already functional prior to the acquisition of symbolic knowledge and language. While it is known that preverbal infants represent numerical order along a left-to-right spatial continuum, no studies have investigated yet whether infants, like adults, organize any kind of ordinal information onto a directional space. We investigated whether 7-month-olds' ability to learn high-order rule-like patterns from visual sequences of geometric shapes was affected by the spatial orientation of the sequences (left-to-right vs. right-to-left). Results showed that infants readily learn rule-like patterns when visual sequences were presented from left to right, but not when presented from right to left. This result provides evidence that spatial orientation critically determines preverbal infants' ability to perceive and learn ordered information in visual sequences, opening to the idea that a left-to-right spatially organized mental representation of ordered dimensions might be rooted in biologically-determined constraints on human brain development.

Electronuclear sum rules

International Nuclear Information System (INIS)

Arenhoevel, H.; Drechsel, D.; Weber, H.J.

1978-01-01

Generalized sum rules are derived by integrating the electromagnetic structure functions along lines of constant ratio of momentum and energy transfer. For non-relativistic systems these sum rules are related to the conventional photonuclear sum rules by a scaling transformation. The generalized sum rules are connected with the absorptive part of the forward scattering amplitude of virtual photons. The analytic structure of the scattering amplitudes and the possible existence of dispersion relations have been investigated in schematic relativistic and non-relativistic models. While for the non-relativistic case analyticity does not hold, the relativistic scattering amplitude is analytical for time-like (but not for space-like) photons and relations similar to the Gell-Mann-Goldberger-Thirring sum rule exist. (Auth.)

Reversal of long-term potentiation-like plasticity processes after motor learning disrupts skill retention.

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Cantarero, Gabriela; Lloyd, Ashley; Celnik, Pablo

2013-07-31

Plasticity of synaptic connections in the primary motor cortex (M1) is thought to play an essential role in learning and memory. Human and animal studies have shown that motor learning results in long-term potentiation (LTP)-like plasticity processes, namely potentiation of M1 and a temporary occlusion of additional LTP-like plasticity. Moreover, biochemical processes essential for LTP are also crucial for certain types of motor learning and memory. Thus, it has been speculated that the occlusion of LTP-like plasticity after learning, indicative of how much LTP was used to learn, is essential for retention. Here we provide supporting evidence of it in humans. Induction of LTP-like plasticity can be abolished using a depotentiation protocol (DePo) consisting of brief continuous theta burst stimulation. We used transcranial magnetic stimulation to assess whether application of DePo over M1 after motor learning affected (1) occlusion of LTP-like plasticity and (2) retention of motor skill learning. We found that the magnitude of motor memory retention is proportional to the magnitude of occlusion of LTP-like plasticity. Moreover, DePo stimulation over M1, but not over a control site, reversed the occlusion of LTP-like plasticity induced by motor learning and disrupted skill retention relative to control subjects. Altogether, these results provide evidence of a link between occlusion of LTP-like plasticity and retention and that this measure could be used as a biomarker to predict retention. Importantly, attempts to reverse the occlusion of LTP-like plasticity after motor learning comes with the cost of reducing retention of motor learning.

Neuromodulated Spike-Timing-Dependent Plasticity and Theory of Three-Factor Learning Rules

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Wulfram eGerstner

2016-01-01

Full Text Available Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulatorson synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide 'when' to create new memories in response to a flow of sensory stimuli.In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discusssome experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity.We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators.

Learning-induced pattern classification in a chaotic neural network

International Nuclear Information System (INIS)

Li, Yang; Zhu, Ping; Xie, Xiaoping; He, Guoguang; Aihara, Kazuyuki

2012-01-01

In this Letter, we propose a Hebbian learning rule with passive forgetting (HLRPF) for use in a chaotic neural network (CNN). We then define the indices based on the Euclidean distance to investigate the evolution of the weights in a simplified way. Numerical simulations demonstrate that, under suitable external stimulations, the CNN with the proposed HLRPF acts as a fuzzy-like pattern classifier that performs much better than an ordinary CNN. The results imply relationship between learning and recognition. -- Highlights: ► Proposing a Hebbian learning rule with passive forgetting (HLRPF). ► Defining indices to investigate the evolution of the weights simply. ► The chaotic neural network with HLRPF acts as a fuzzy-like pattern classifier. ► The pattern classifier ability of the network is improved much.

CACNA1C gene regulates behavioral strategies in operant rule learning.

Science.gov (United States)

Koppe, Georgia; Mallien, Anne Stephanie; Berger, Stefan; Bartsch, Dusan; Gass, Peter; Vollmayr, Barbara; Durstewitz, Daniel

2017-06-01

Behavioral experiments are usually designed to tap into a specific cognitive function, but animals may solve a given task through a variety of different and individual behavioral strategies, some of them not foreseen by the experimenter. Animal learning may therefore be seen more as the process of selecting among, and adapting, potential behavioral policies, rather than mere strengthening of associative links. Calcium influx through high-voltage-gated Ca2+ channels is central to synaptic plasticity, and altered expression of Cav1.2 channels and the CACNA1C gene have been associated with severe learning deficits and psychiatric disorders. Given this, we were interested in how specifically a selective functional ablation of the Cacna1c gene would modulate the learning process. Using a detailed, individual-level analysis of learning on an operant cue discrimination task in terms of behavioral strategies, combined with Bayesian selection among computational models estimated from the empirical data, we show that a Cacna1c knockout does not impair learning in general but has a much more specific effect: the majority of Cacna1c knockout mice still managed to increase reward feedback across trials but did so by adapting an outcome-based strategy, while the majority of matched controls adopted the experimentally intended cue-association rule. Our results thus point to a quite specific role of a single gene in learning and highlight that much more mechanistic insight could be gained by examining response patterns in terms of a larger repertoire of potential behavioral strategies. The results may also have clinical implications for treating psychiatric disorders.

Rule-Governed and Contingency-Shaped Behavior of Learning-Disabled, Hyperactive, and Nonselected Elementary School Children.

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Metzger, Mary Ann; Freund, Lisa

The major purpose of this study was to describe the rule-governed and contingency-shaped behavior of learning-disabled, hyperactive, and nonselected elementary school children working on a computer-managed task. Hypotheses tested were (1) that the children would differ in the degree to which either instructions or external contingencies controlled…

Analysis of Rules for Islamic Inheritance Law in Indonesia Using Hybrid Rule Based Learning

Science.gov (United States)

Khosyi'ah, S.; Irfan, M.; Maylawati, D. S.; Mukhlas, O. S.

2018-01-01

Along with the development of human civilization in Indonesia, the changes and reform of Islamic inheritance law so as to conform to the conditions and culture cannot be denied. The distribution of inheritance in Indonesia can be done automatically by storing the rule of Islamic inheritance law in the expert system. In this study, we analyze the knowledge of experts in Islamic inheritance in Indonesia and represent it in the form of rules using rule-based Forward Chaining (FC) and Davis-Putman-Logemann-Loveland (DPLL) algorithms. By hybridizing FC and DPLL algorithms, the rules of Islamic inheritance law in Indonesia are clearly defined and measured. The rules were conceptually validated by some experts in Islamic laws and informatics. The results revealed that generally all rules were ready for use in an expert system.

Human-like brain hemispheric dominance in birdsong learning.

Science.gov (United States)

Moorman, Sanne; Gobes, Sharon M H; Kuijpers, Maaike; Kerkhofs, Amber; Zandbergen, Matthijs A; Bolhuis, Johan J

2012-07-31

Unlike nonhuman primates, songbirds learn to vocalize very much like human infants acquire spoken language. In humans, Broca's area in the frontal lobe and Wernicke's area in the temporal lobe are crucially involved in speech production and perception, respectively. Songbirds have analogous brain regions that show a similar neural dissociation between vocal production and auditory perception and memory. In both humans and songbirds, there is evidence for lateralization of neural responsiveness in these brain regions. Human infants already show left-sided dominance in their brain activation when exposed to speech. Moreover, a memory-specific left-sided dominance in Wernicke's area for speech perception has been demonstrated in 2.5-mo-old babies. It is possible that auditory-vocal learning is associated with hemispheric dominance and that this association arose in songbirds and humans through convergent evolution. Therefore, we investigated whether there is similar song memory-related lateralization in the songbird brain. We exposed male zebra finches to tutor or unfamiliar song. We found left-sided dominance of neuronal activation in a Broca-like brain region (HVC, a letter-based name) of juvenile and adult zebra finch males, independent of the song stimulus presented. In addition, juvenile males showed left-sided dominance for tutor song but not for unfamiliar song in a Wernicke-like brain region (the caudomedial nidopallium). Thus, left-sided dominance in the caudomedial nidopallium was specific for the song-learning phase and was memory-related. These findings demonstrate a remarkable neural parallel between birdsong and human spoken language, and they have important consequences for our understanding of the evolution of auditory-vocal learning and its neural mechanisms.

Motor learning interference is proportional to occlusion of LTP-like plasticity.

Science.gov (United States)

Cantarero, Gabriela; Tang, Byron; O'Malley, Rebecca; Salas, Rachel; Celnik, Pablo

2013-03-13

Learning interference occurs when learning something new causes forgetting of an older memory (retrograde interference) or when learning a new task disrupts learning of a second subsequent task (anterograde interference). This phenomenon, described in cognitive, sensory, and motor domains, limits our ability to learn multiple tasks in close succession. It has been suggested that the source of interference is competition of neural resources, although the neuronal mechanisms are unknown. Learning induces long-term potentiation (LTP), which can ultimately limit the ability to induce further LTP, a phenomenon known as occlusion. In humans we quantified the magnitude of occlusion of anodal transcranial direct current stimulation-induced increased excitability after learning a skill task as an index of the amount of LTP-like plasticity used. We found that retention of a newly acquired skill, as reflected by performance in the second day of practice, is proportional to the magnitude of occlusion. Moreover, the degree of behavioral interference was correlated with the magnitude of occlusion. Individuals with larger occlusion after learning the first skill were (1) more resilient to retrograde interference and (2) experienced larger anterograde interference when training a second task, as expressed by decreased performance of the learned skill in the second day of practice. This effect was not observed if sufficient time elapsed between training the two skills and LTP-like occlusion was not present. These findings suggest competition of LTP-like plasticity is a factor that limits the ability to remember multiple tasks trained in close succession.

Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation

International Nuclear Information System (INIS)

Cyr, André; Boukadoum, Mounir

2013-01-01

This paper presents a novel bio-inspired habituation function for robots under control by an artificial spiking neural network. This non-associative learning rule is modelled at the synaptic level and validated through robotic behaviours in reaction to different stimuli patterns in a dynamical virtual 3D world. Habituation is minimally represented to show an attenuated response after exposure to and perception of persistent external stimuli. Based on current neurosciences research, the originality of this rule includes modulated response to variable frequencies of the captured stimuli. Filtering out repetitive data from the natural habituation mechanism has been demonstrated to be a key factor in the attention phenomenon, and inserting such a rule operating at multiple temporal dimensions of stimuli increases a robot's adaptive behaviours by ignoring broader contextual irrelevant information. (paper)

Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation.

Science.gov (United States)

Cyr, André; Boukadoum, Mounir

2013-03-01

This paper presents a novel bio-inspired habituation function for robots under control by an artificial spiking neural network. This non-associative learning rule is modelled at the synaptic level and validated through robotic behaviours in reaction to different stimuli patterns in a dynamical virtual 3D world. Habituation is minimally represented to show an attenuated response after exposure to and perception of persistent external stimuli. Based on current neurosciences research, the originality of this rule includes modulated response to variable frequencies of the captured stimuli. Filtering out repetitive data from the natural habituation mechanism has been demonstrated to be a key factor in the attention phenomenon, and inserting such a rule operating at multiple temporal dimensions of stimuli increases a robot's adaptive behaviours by ignoring broader contextual irrelevant information.

Human likeness: cognitive and affective factors affecting adoption of robot-assisted learning systems

Science.gov (United States)

Yoo, Hosun; Kwon, Ohbyung; Lee, Namyeon

2016-07-01

With advances in robot technology, interest in robotic e-learning systems has increased. In some laboratories, experiments are being conducted with humanoid robots as artificial tutors because of their likeness to humans, the rich possibilities of using this type of media, and the multimodal interaction capabilities of these robots. The robot-assisted learning system, a special type of e-learning system, aims to increase the learner's concentration, pleasure, and learning performance dramatically. However, very few empirical studies have examined the effect on learning performance of incorporating humanoid robot technology into e-learning systems or people's willingness to accept or adopt robot-assisted learning systems. In particular, human likeness, the essential characteristic of humanoid robots as compared with conventional e-learning systems, has not been discussed in a theoretical context. Hence, the purpose of this study is to propose a theoretical model to explain the process of adoption of robot-assisted learning systems. In the proposed model, human likeness is conceptualized as a combination of media richness, multimodal interaction capabilities, and para-social relationships; these factors are considered as possible determinants of the degree to which human cognition and affection are related to the adoption of robot-assisted learning systems.

Delayed rule following

OpenAIRE

Schmitt, David R.

2001-01-01

Although the elements of a fully stated rule (discriminative stimulus [SD], some behavior, and a consequence) can occur nearly contemporaneously with the statement of the rule, there is often a delay between the rule statement and the SD. The effects of this delay on rule following have not been studied in behavior analysis, but they have been investigated in rule-like settings in the areas of prospective memory (remembering to do something in the future) and goal pursuit. Discriminative even...

The Rule-Assessment Approach and Education.

Science.gov (United States)

Siegler, Robert S.

1982-01-01

This paper describes the rule-assessment approach to cognitive development. The basic question that motivated the rule-assessment approach is how people's existing knowledge influences their ability to learn. Research using the rule-assessment approach is summarized in terms of eight conclusions, each illustrated with empirical examples.…

Binary translation using peephole translation rules

Science.gov (United States)

Bansal, Sorav; Aiken, Alex

2010-05-04

An efficient binary translator uses peephole translation rules to directly translate executable code from one instruction set to another. In a preferred embodiment, the translation rules are generated using superoptimization techniques that enable the translator to automatically learn translation rules for translating code from the source to target instruction set architecture.

Storage Capacity of Generalized Palimpsests

Science.gov (United States)

Bonnaz, D.

1997-12-01

A simple analytical study of a short term memory model is performed. This model consists of a symmetric p-neuron interaction between N neurons. Learning is achieved by a generalized Hebb rule. Saturation is prevented by the introduction of a bound A to the couplings. At each time step, an input pattern is drawn at random, independently of the previous ones. The determination of the life time T of a memorized pattern viewed as a function of A and N is accomplished by a statistical study of the dynamic of the learning process which has been made possible under the assumption that the couplings evolve independently. This simplification reduces the determination of T to a one-dimensional problem, by considering energies rather than couplings. The choice of the optimal value A_opt of A is a compromise between the success of the learning process and the maximization of T. The essential results are expressed by the formulae Tpropto A^2 and A_optpropto N^{frac{p-1}{2}}.

Learning the Rules of the Game

Science.gov (United States)

Smith, Donald A.

2018-03-01

Games have often been used in the classroom to teach physics ideas and concepts, but there has been less published on games that can be used to teach scientific thinking. D. Maloney and M. Masters describe an activity in which students attempt to infer rules to a game from a history of moves, but the students don't actually play the game. Giving the list of moves allows the instructor to emphasize the important fact that nature usually gives us incomplete data sets, but it does make the activity less immersive. E. Kimmel suggested letting students attempt to figure out the rules to Reversi by playing it, but this game only has two players, which makes it difficult to apply in a classroom setting. Kimmel himself admits the choice of Reversi is somewhat arbitrary. There are games, however, that are designed to make the process of figuring out the rules an integral aspect of play. These games involve more people and require only a deck or two of cards. I present here an activity constructed around the card game Mao, which can be used to help students recognize aspects of scientific thinking. The game is particularly good at illustrating the importance of falsification tests (questions designed to elicit a negative answer) over verification tests (examples that confirm what is already suspected) for illuminating the underlying rules.

A Constructionism Framework for Designing Game-Like Learning Systems: Its Effect on Different Learners

Science.gov (United States)

Li, Zhong-Zheng; Cheng, Yuan-Bang; Liu, Chen-Chung

2013-01-01

Game-like learning systems such as simulation games and digital toys are increasingly being applied to foster higher-level abilities in educational contexts, as they may facilitate an active learning experience. However, the effect of such game-like learning systems is not guaranteed because students may only be interested in the fantasy…

Learning of grammar-like visual sequences by adults with and without language-learning disabilities.

Science.gov (United States)

Aguilar, Jessica M; Plante, Elena

2014-08-01

Two studies examined learning of grammar-like visual sequences to determine whether a general deficit in statistical learning characterizes this population. Furthermore, we tested the hypothesis that difficulty in sustaining attention during the learning task might account for differences in statistical learning. In Study 1, adults with normal language (NL) or language-learning disability (LLD) were familiarized with the visual artificial grammar and then tested using items that conformed or deviated from the grammar. In Study 2, a 2nd sample of adults with NL and LLD were presented auditory word pairs with weak semantic associations (e.g., groom + clean) along with the visual learning task. Participants were instructed to attend to visual sequences and to ignore the auditory stimuli. Incidental encoding of these words would indicate reduced attention to the primary task. In Studies 1 and 2, both groups demonstrated learning and generalization of the artificial grammar. In Study 2, neither the NL nor the LLD group appeared to encode the words presented during the learning phase. The results argue against a general deficit in statistical learning for individuals with LLD and demonstrate that both NL and LLD learners can ignore extraneous auditory stimuli during visual learning.

Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models

Directory of Open Access Journals (Sweden)

Seyed Mehran Kazemi

2018-02-01

Full Text Available The aim of statistical relational learning is to learn statistical models from relational or graph-structured data. Three main statistical relational learning paradigms include weighted rule learning, random walks on graphs, and tensor factorization. These paradigms have been mostly developed and studied in isolation for many years, with few works attempting at understanding the relationship among them or combining them. In this article, we study the relationship between the path ranking algorithm (PRA, one of the most well-known relational learning methods in the graph random walk paradigm, and relational logistic regression (RLR, one of the recent developments in weighted rule learning. We provide a simple way to normalize relations and prove that relational logistic regression using normalized relations generalizes the path ranking algorithm. This result provides a better understanding of relational learning, especially for the weighted rule learning and graph random walk paradigms. It opens up the possibility of using the more flexible RLR rules within PRA models and even generalizing both by including normalized and unnormalized relations in the same model.

Single neurons in prefrontal cortex encode abstract rules.

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Wallis, J D; Anderson, K C; Miller, E K

2001-06-21

The ability to abstract principles or rules from direct experience allows behaviour to extend beyond specific circumstances to general situations. For example, we learn the 'rules' for restaurant dining from specific experiences and can then apply them in new restaurants. The use of such rules is thought to depend on the prefrontal cortex (PFC) because its damage often results in difficulty in following rules. Here we explore its neural basis by recording from single neurons in the PFC of monkeys trained to use two abstract rules. They were required to indicate whether two successively presented pictures were the same or different depending on which rule was currently in effect. The monkeys performed this task with new pictures, thus showing that they had learned two general principles that could be applied to stimuli that they had not yet experienced. The most prevalent neuronal activity observed in the PFC reflected the coding of these abstract rules.

Lessons learned from early implementation of the maintenance rule at nine nuclear power plants

International Nuclear Information System (INIS)

Petrone, C.D.; Correia, R.P.; Black, S.C.

1995-06-01

This report summarizes the lessons learned from the nine pilot site visits that were performed to review early implementation of the maintenance rule using the draft NRC Maintenance Inspection Procedure. Licensees followed NUMARC 93-01, ''Industry Guideline for Monitoring the Effectiveness of Maintenance at Nuclear Power Plants.'' In general, the licensees were thorough in determining which structures, systems, and components (SSCS) were within the scope of the maintenance rule at each site. The use of an expert panel was an appropriate and practical method of determining which SSCs are risk significant. When setting goals, all licensees considered safety but many licensees did not consider operating experience throughout the industry. Although required to do so, licensees were not monitoring at the system or train level the performance or condition for some systems used in standby service but not significant to risk. Most licensees had not established adequate monitoring of structures under the rule. Licensees established reasonable plans for doing periodic evaluations, balancing unavailability and reliability, and assessing the effect of taking equipment out of service for maintenance. However, these plans were not evaluated because they had not been fully implemented at the time of the site visits

Facilitated stimulus-response associative learning and long-term memory in mice lacking the NTAN1 amidase of the N-end rule pathway.

Science.gov (United States)

Balogh, S A; McDowell, C S; Tae Kwon, Y; Denenberg, V H

2001-02-23

The N-end rule relates the in vivo half-life of a protein to the identity of its N-terminal residue. Inactivation of the NTAN1 gene encoding the asparagine-specific N-terminal amidase in mice results in impaired spatial memory [26]. The studies described here were designed to further characterize the effects upon learning and memory of inactivating the NTAN1 gene. NTAN1-deficient mice were found to be better than wild-type mice on black-white and horizontal-vertical discrimination learning. They were also better at 8-week Morris maze retention testing when a reversal trial was not included in the testing procedures. In all three tasks NTAN1-deficient mice appeared to use a strong win-stay strategy. It is concluded that inactivating the asparagine-specific branch of the N-end rule pathway in mice results in impaired spatial learning with concomitant compensatory restructuring of the nervous system in favor of non-spatial (stimulus-response) learning.

Trading Rules on Stock Markets Using Genetic Network Programming with Reinforcement Learning and Importance Index

Science.gov (United States)

Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki

Genetic Network Programming (GNP) is an evolutionary computation which represents its solutions using graph structures. Since GNP can create quite compact programs and has an implicit memory function, it has been clarified that GNP works well especially in dynamic environments. In addition, a study on creating trading rules on stock markets using GNP with Importance Index (GNP-IMX) has been done. IMX is a new element which is a criterion for decision making. In this paper, we combined GNP-IMX with Actor-Critic (GNP-IMX&AC) and create trading rules on stock markets. Evolution-based methods evolve their programs after enough period of time because they must calculate fitness values, however reinforcement learning can change programs during the period, therefore the trading rules can be created efficiently. In the simulation, the proposed method is trained using the stock prices of 10 brands in 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. The simulation results show that the proposed method can obtain larger profits than GNP-IMX without AC and Buy&Hold.

Improving drivers' knowledge of road rules using digital games.

Science.gov (United States)

Li, Qing; Tay, Richard

2014-04-01

Although a proficient knowledge of the road rules is important to safe driving, many drivers do not retain the knowledge acquired after they have obtained their licenses. Hence, more innovative and appealing methods are needed to improve drivers' knowledge of the road rules. This study examines the effect of game based learning on drivers' knowledge acquisition and retention. We find that playing an entertaining game that is designed to impart knowledge of the road rules not only improves players' knowledge but also helps them retain such knowledge. Hence, learning by gaming appears to be a promising learning approach for driver education. Copyright © 2013 Elsevier Ltd. All rights reserved.

Gaia: "Thinking Like a Planet" as Transformative Learning

Science.gov (United States)

Haigh, Martin

2014-01-01

Transformative learning may involve gentle perspective widening or something more traumatic. This paper explores the impact of a transformative pedagogy in a course that challenges learners to "think like a planet". Among six sources of intellectual anxiety, learners worry about: why Gaia Theory is neglected by their other courses; the…

QCD Sum Rules, a Modern Perspective

CERN Document Server

Colangelo, Pietro; Colangelo, Pietro; Khodjamirian, Alexander

2001-01-01

An introduction to the method of QCD sum rules is given for those who want to learn how to use this method. Furthermore, we discuss various applications of sum rules, from the determination of quark masses to the calculation of hadronic form factors and structure functions. Finally, we explain the idea of the light-cone sum rules and outline the recent development of this approach.

Analysis of ensemble learning using simple perceptrons based on online learning theory

Science.gov (United States)

Miyoshi, Seiji; Hara, Kazuyuki; Okada, Masato

2005-03-01

Ensemble learning of K nonlinear perceptrons, which determine their outputs by sign functions, is discussed within the framework of online learning and statistical mechanics. One purpose of statistical learning theory is to theoretically obtain the generalization error. This paper shows that ensemble generalization error can be calculated by using two order parameters, that is, the similarity between a teacher and a student, and the similarity among students. The differential equations that describe the dynamical behaviors of these order parameters are derived in the case of general learning rules. The concrete forms of these differential equations are derived analytically in the cases of three well-known rules: Hebbian learning, perceptron learning, and AdaTron (adaptive perceptron) learning. Ensemble generalization errors of these three rules are calculated by using the results determined by solving their differential equations. As a result, these three rules show different characteristics in their affinity for ensemble learning, that is “maintaining variety among students.” Results show that AdaTron learning is superior to the other two rules with respect to that affinity.

Learning to Learn.

Science.gov (United States)

Weiss, Helen; Weiss, Martin

1988-01-01

The article reviews theories of learning (e.g., stimulus-response, trial and error, operant conditioning, cognitive), considers the role of motivation, and summarizes nine research-supported rules of effective learning. Suggestions are applied to teaching learning strategies to learning-disabled students. (DB)

The nuclear receptor E75A has a novel pair-rule-like function in patterning the milkweed bug, Oncopeltus fasciatus.

Science.gov (United States)

Erezyilmaz, Deniz F; Kelstrup, Hans C; Riddiford, Lynn M

2009-10-01

Genetic studies of the fruit fly Drosophila have revealed a hierarchy of segmentation genes (maternal, gap, pair-rule and HOX) that subdivide the syncytial blastoderm into sequentially finer-scale coordinates. Within this hierarchy, the pair-rule genes translate gradients of information into periodic stripes of expression. How pair-rule genes function during the progressive mode of segmentation seen in short and intermediate-germ insects is an ongoing question. Here we report that the nuclear receptor Of'E75A is expressed with double segment periodicity in the head and thorax. In the abdomen, Of'E75A is expressed in a unique pattern during posterior elongation, and briefly resembles a sequence that is typical of pair-rule genes. Depletion of Of'E75A mRNA caused loss of a subset of odd-numbered parasegments, as well as parasegment 6. Because these parasegments straddle segment boundaries, we observe fusions between adjacent segments. Finally, expression of Of'E75A in the blastoderm requires even-skipped, which is a gap gene in Oncopeltus. These data show that the function of Of'E75A during embryogenesis shares many properties with canonical pair-rule genes in other insects. They further suggest that parasegment specification may occur through irregular and episodic pair-rule-like activity.

Comparison of Natural Language Processing Rules-based and Machine-learning Systems to Identify Lumbar Spine Imaging Findings Related to Low Back Pain.

Science.gov (United States)

Tan, W Katherine; Hassanpour, Saeed; Heagerty, Patrick J; Rundell, Sean D; Suri, Pradeep; Huhdanpaa, Hannu T; James, Kathryn; Carrell, David S; Langlotz, Curtis P; Organ, Nancy L; Meier, Eric N; Sherman, Karen J; Kallmes, David F; Luetmer, Patrick H; Griffith, Brent; Nerenz, David R; Jarvik, Jeffrey G

2018-03-28

To evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems. We used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N = 178,333 reports, we randomly selected N = 871 to form a reference-standard dataset, consisting of N = 413 x-ray reports and N = 458 MR reports. Using standardized criteria, four spine experts annotated the presence of 26 findings, where 71 reports were annotated by all four experts and 800 were each annotated by two experts. We calculated inter-rater agreement and finding prevalence from annotated data. We randomly split the annotated data into development (80%) and testing (20%) sets. We developed an NLP system from both rule-based and machine-learned models. We validated the system using accuracy metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The multirater annotated dataset achieved inter-rater agreement of Cohen's kappa > 0.60 (substantial agreement) for 25 of 26 findings, with finding prevalence ranging from 3% to 89%. In the testing sample, rule-based and machine-learned predictions both had comparable average specificity (0.97 and 0.95, respectively). The machine-learned approach had a higher average sensitivity (0.94, compared to 0.83 for rules-based), and a higher overall AUC (0.98, compared to 0.90 for rules-based). Our NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC. Copyright © 2018 The Association of University Radiologists. All rights reserved.

Value learning through reinforcement : The basics of dopamine and reinforcement learning

NARCIS (Netherlands)

Daw, N.D.; Tobler, P.N.; Glimcher, P.W.; Fehr, E.

2013-01-01

This chapter provides an overview of reinforcement learning and temporal difference learning and relates these topics to the firing properties of midbrain dopamine neurons. First, we review the RescorlaWagner learning rule and basic learning phenomena, such as blocking, which the rule explains. Then

Attentional Bias in Human Category Learning: The Case of Deep Learning.

Science.gov (United States)

Hanson, Catherine; Caglar, Leyla Roskan; Hanson, Stephen José

2018-01-01

Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This "failure" to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures

Attentional Bias in Human Category Learning: The Case of Deep Learning

Directory of Open Access Journals (Sweden)

Catherine Hanson

2018-04-01

Full Text Available Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987 showed that stimuli can have structures with features that are statistically uncorrelated (separable or statistically correlated (integral within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974. In contrast to humans, a single hidden layer backpropagation (BP neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993. This “failure” to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1 by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2 by investigating whether a Deep Learning (DL network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc., would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993. Second, we show that using the same low dimensional stimuli, Deep Learning (DL, unlike BP but similar to humans, learns separable category structures more quickly than integral category

Different neurophysiological mechanisms underlying word and rule extraction from speech.

Directory of Open Access Journals (Sweden)

Ruth De Diego Balaguer

Full Text Available The initial process of identifying words from spoken language and the detection of more subtle regularities underlying their structure are mandatory processes for language acquisition. Little is known about the cognitive mechanisms that allow us to extract these two types of information and their specific time-course of acquisition following initial contact with a new language. We report time-related electrophysiological changes that occurred while participants learned an artificial language. These changes strongly correlated with the discovery of the structural rules embedded in the words. These changes were clearly different from those related to word learning and occurred during the first minutes of exposition. There is a functional distinction in the nature of the electrophysiological signals during acquisition: an increase in negativity (N400 in the central electrodes is related to word-learning and development of a frontal positivity (P2 is related to rule-learning. In addition, the results of an online implicit and a post-learning test indicate that, once the rules of the language have been acquired, new words following the rule are processed as words of the language. By contrast, new words violating the rule induce syntax-related electrophysiological responses when inserted online in the stream (an early frontal negativity followed by a late posterior positivity and clear lexical effects when presented in isolation (N400 modulation. The present study provides direct evidence suggesting that the mechanisms to extract words and structural dependencies from continuous speech are functionally segregated. When these mechanisms are engaged, the electrophysiological marker associated with rule-learning appears very quickly, during the earliest phases of exposition to a new language.

Environmental enrichment ameliorates depressive-like symptoms in young rats bred for learned helplessness.

Science.gov (United States)

Richter, S Helene; Zeuch, Benjamin; Riva, Marco A; Gass, Peter; Vollmayr, Barbara

2013-09-01

The incidence of major depression is known to be influenced by both genetic and environmental factors. In the current study, we therefore set out to investigate depressive-like behavior and its modification by environmental enrichment using rats bred for 'learned helplessness'. 45 males of congenitally helpless (cLH, n=22) and non-helpless (cNLH, n=23) rats of two different generations were used to systematically investigate differential effects of environmental enrichment on learned helpless behavior, anhedonic-like behavior (sweetened condensed milk consumption) and spontaneous behavior in the home cage. While enrichment was found to reduce learned helpless behavior in 14 weeks old, but not 28 weeks old cLH rats, it did not affect the consumption of sweetened condensed milk. Regarding the home cage behavior, no consistent changes between rats of different strains, housing conditions, and ages were observed. We could thus demonstrate that a genetic predisposition for learned helplessness may interact with environmental conditions in mediating some, but not all depressive-like symptoms in congenitally learned helpless rats. However, future efforts are needed to isolate the differential benefits of environmental factors in mediating the different depression-related symptoms. Copyright © 2013 Elsevier B.V. All rights reserved.

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

Directory of Open Access Journals (Sweden)

Yang Li

Full Text Available In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA methods, a new rule extraction method based on extreme learning machine (ELM and an improved Ant-miner (IAM algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

Science.gov (United States)

Li, Yang; Li, Guoqing; Wang, Zhenhao

2015-01-01

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

Proof Rules for Recursive Procedures

NARCIS (Netherlands)

Hesselink, Wim H.

1993-01-01

Four proof rules for recursive procedures in a Pascal-like language are presented. The main rule deals with total correctness and is based on results of Gries and Martin. The rule is easier to apply than Martin's. It is introduced as an extension of a specification format for Pascal-procedures, with

Robot Grasp Learning by Demonstration without Predefined Rules

Directory of Open Access Journals (Sweden)

César Fernández

2011-12-01

Full Text Available A learning-based approach to autonomous robot grasping is presented. Pattern recognition techniques are used to measure the similarity between a set of previously stored example grasps and all the possible candidate grasps for a new object. Two sets of features are defined in order to characterize grasps: point attributes describe the surroundings of a contact point; point-set attributes describe the relationship between the set of n contact points (assuming an n-fingered robot gripper is used. In the experiments performed, the nearest neighbour classifier outperforms other approaches like multilayer perceptrons, radial basis functions or decision trees, in terms of classification accuracy, while computational load is not excessive for a real time application (a grasp is fully synthesized in 0.2 seconds. The results obtained on a synthetic database show that the proposed system is able to imitate the grasping behaviour of the user (e.g. the system learns to grasp a mug by its handle. All the code has been made available for testing purposes.

Functional networks inference from rule-based machine learning models.

Science.gov (United States)

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

The Postal Acceptance Rule in the Digital Age

OpenAIRE

Al Ibrahim, Marwan; Ababneh, Ala’eldin; Tahat, Hisham

2007-01-01

This article examines the application of the postal acceptance rule to email acceptances. Differentviews have been argued against the application of traditional rule like the postal acceptance rule, which wasestablished in 1818 as a legal norm in contract formation to modern communications like the email. The paperpresents the arguments and rationale behind the application of this rule and contends its applicability to themodern communication via e-mail. The paper posits that email is not an ...

Biclustering Learning of Trading Rules.

Science.gov (United States)

Huang, Qinghua; Wang, Ting; Tao, Dacheng; Li, Xuelong

2015-10-01

Technical analysis with numerous indicators and patterns has been regarded as important evidence for making trading decisions in financial markets. However, it is extremely difficult for investors to find useful trading rules based on numerous technical indicators. This paper innovatively proposes the use of biclustering mining to discover effective technical trading patterns that contain a combination of indicators from historical financial data series. This is the first attempt to use biclustering algorithm on trading data. The mined patterns are regarded as trading rules and can be classified as three trading actions (i.e., the buy, the sell, and no-action signals) with respect to the maximum support. A modified K nearest neighborhood ( K -NN) method is applied to classification of trading days in the testing period. The proposed method [called biclustering algorithm and the K nearest neighbor (BIC- K -NN)] was implemented on four historical datasets and the average performance was compared with the conventional buy-and-hold strategy and three previously reported intelligent trading systems. Experimental results demonstrate that the proposed trading system outperforms its counterparts and will be useful for investment in various financial markets.

Do Group Decision Rules Affect Trust? A Laboratory Experiment on Group Decision Rules and Trust

DEFF Research Database (Denmark)

Nielsen, Julie Hassing

2016-01-01

Enhanced participation has been prescribed as the way forward for improving democratic decision making while generating positive attributes like trust. Yet we do not know the extent to which rules affect the outcome of decision making. This article investigates how different group decision rules......-hierarchical decision-making procedures enhance trust vis-à-vis other more hierarchical decision-making procedures....... affect group trust by testing three ideal types of decision rules (i.e., a Unilateral rule, a Representative rule and a 'Non-rule') in a laboratory experiment. The article shows significant differences between the three decision rules on trust after deliberation. Interestingly, however, it finds...

Derivation of sum rules for quark and baryon fields. [light-like charges

Energy Technology Data Exchange (ETDEWEB)

Bongardt, K [Karlsruhe Univ. (TH) (Germany, F.R.). Inst. fuer Theoretische Kernphysik

1978-08-21

In an analogous way to the Weinberg sum rules, two spectral-function sum rules for quark and baryon fields are derived by means of the concept of lightlike charges. The baryon sum rules are valid for the case of SU/sub 3/ as well as for SU/sub 4/ and the one-particle approximation yields a linear mass relation. This relation is not in disagreement with the normal linear GMO formula for the baryons. The calculated masses of the first resonance states agree very well with the experimental data.

Synaptic theory of Replicator-like melioration

Directory of Open Access Journals (Sweden)

Yonatan Loewenstein

2010-06-01

Full Text Available According to the theory of Melioration, organisms in repeated choice settings shift their choice preference in favor of the alternative that provides the highest return. The goal of this paper is to explain how this learning behavior can emerge from microscopic changes in the efficacies of synapses, in the context of two-alternative repeated-choice experiment. I consider a large family of synaptic plasticity rules in which changes in synaptic efficacies are driven by the covariance between reward and neural activity. I construct a general framework that predicts the learning dynamics of any decision-making neural network that implements this synaptic plasticity rule and show that melioration naturally emerges in such networks. Moreover, the resultant learning dynamics follows the Replicator equation which is commonly used to phenomenologically describe changes in behavior in operant conditioning experiments. Several examples demonstrate how the learning rate of the network is affected by its properties and by the specifics of the plasticity rule. These results help bridge the gap between cellular physiology and learning behavior.

Feature Binding and the Hebb Repetition Effect

OpenAIRE

Barrett, Maeve

2008-01-01

Previous studies have found no evidence that long-term learning of integrated objects and individual features benefit visual short term memory tasks (Logie, Brockmole, & Vandenbroucke, in press; Olson & Jiang, 2004; Treisman, 2006). These findings may have been due to stimulus interference as a restricted number of features were utilised in these studies to form objects in the stimulus arrays. In these studies, participants would have needed to break apart the features of several objects in a...

Evolving fuzzy rules for relaxed-criteria negotiation.

Science.gov (United States)

Sim, Kwang Mong

2008-12-01

In the literature on automated negotiation, very few negotiation agents are designed with the flexibility to slightly relax their negotiation criteria to reach a consensus more rapidly and with more certainty. Furthermore, these relaxed-criteria negotiation agents were not equipped with the ability to enhance their performance by learning and evolving their relaxed-criteria negotiation rules. The impetus of this work is designing market-driven negotiation agents (MDAs) that not only have the flexibility of relaxing bargaining criteria using fuzzy rules, but can also evolve their structures by learning new relaxed-criteria fuzzy rules to improve their negotiation outcomes as they participate in negotiations in more e-markets. To this end, an evolutionary algorithm for adapting and evolving relaxed-criteria fuzzy rules was developed. Implementing the idea in a testbed, two kinds of experiments for evaluating and comparing EvEMDAs (MDAs with relaxed-criteria rules that are evolved using the evolutionary algorithm) and EMDAs (MDAs with relaxed-criteria rules that are manually constructed) were carried out through stochastic simulations. Empirical results show that: 1) EvEMDAs generally outperformed EMDAs in different types of e-markets and 2) the negotiation outcomes of EvEMDAs generally improved as they negotiated in more e-markets.

Multiagent -Learning for Aloha-Like Spectrum Access in Cognitive Radio Systems

Directory of Open Access Journals (Sweden)

Li Husheng

2010-01-01

Full Text Available An Aloha-like spectrum access scheme without negotiation is considered for multiuser and multichannel cognitive radio systems. To avoid collisions incurred by the lack of coordination, each secondary user learns how to select channels according to its experience. Multiagent reinforcement leaning (MARL is applied for the secondary users to learn good strategies of channel selection. Specifically, the framework of -learning is extended from single user case to multiagent case by considering other secondary users as a part of the environment. The dynamics of the -learning are illustrated using a Metrick-Polak plot, which shows the traces of -values in the two-user case. For both complete and partial observation cases, rigorous proofs of the convergence of multiagent -learning without communications, under certain conditions, are provided using the Robins-Monro algorithm and contraction mapping, respectively. The learning performance (speed and gain in utility is evaluated by numerical simulations.

Alteration of a motor learning rule under mirror-reversal transformation does not depend on the amplitude of visual error.

Science.gov (United States)

Kasuga, Shoko; Kurata, Makiko; Liu, Meigen; Ushiba, Junichi

2015-05-01

Human's sophisticated motor learning system paradoxically interferes with motor performance when visual information is mirror-reversed (MR), because normal movement error correction further aggravates the error. This error-increasing mechanism makes performing even a simple reaching task difficult, but is overcome by alterations in the error correction rule during the trials. To isolate factors that trigger learners to change the error correction rule, we manipulated the gain of visual angular errors when participants made arm-reaching movements with mirror-reversed visual feedback, and compared the rule alteration timing between groups with normal or reduced gain. Trial-by-trial changes in the visual angular error was tracked to explain the timing of the change in the error correction rule. Under both gain conditions, visual angular errors increased under the MR transformation, and suddenly decreased after 3-5 trials with increase. The increase became degressive at different amplitude between the two groups, nearly proportional to the visual gain. The findings suggest that the alteration of the error-correction rule is not dependent on the amplitude of visual angular errors, and possibly determined by the number of trials over which the errors increased or statistical property of the environment. The current results encourage future intensive studies focusing on the exact rule-change mechanism. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

A short-term neural network memory

Energy Technology Data Exchange (ETDEWEB)

Morris, R.J.T.; Wong, W.S.

1988-12-01

Neural network memories with storage prescriptions based on Hebb's rule are known to collapse as more words are stored. By requiring that the most recently stored word be remembered precisely, a new simple short-term neutral network memory is obtained and its steady state capacity analyzed and simulated. Comparisons are drawn with Hopfield's method, the delta method of Widrow and Hoff, and the revised marginalist model of Mezard, Nadal, and Toulouse.

Dissociation of Category-Learning Systems via Brain Potentials

Directory of Open Access Journals (Sweden)

Robert G Morrison

2015-07-01

Full Text Available Behavioral, neuropsychological, and neuroimaging evidence has suggested that categories can often be learned via either an explicit rule-based mechanism critically dependent on medial temporal and prefrontal brain regions, or via an implicit information-integration mechanism relying on the basal ganglia. In this study, participants viewed sine-wave gratings (i.e., Gabor patches that varied on two dimensions and learned to categorize them via trial-by-trial feedback. Two different stimulus distributions were used; one was intended to encourage an explicit rule-based process and the other an implicit information-integration process. We monitored brain activity with scalp electroencephalography (EEG while each participant (1 passively observed stimuli represented of both distributions, (2 categorized stimuli from one distribution, and, one week later, (3 categorized stimuli from the other distribution. Categorization accuracy was similar for the two distributions. Subtractions of Event-Related Potentials (ERPs for correct and incorrect trials were used to identify neural differences in rule-based and information-integration categorization processes. We identified an occipital brain potential that was differentially modulated by categorization condition accuracy at an early latency (150 - 250 ms, likely reflecting the degree of holistic processing. A stimulus-locked late positive complex associated with explicit memory updating was modulated by accuracy in the rule-based, but not the information-integration task. Likewise, a feedback-locked P300 ERP associated with expectancy was correlated with performance only in the rule-based, but not the information-integration condition. These results provide additional evidence for distinct brain mechanisms supporting rule-based versus implicit information-integration category learning and use.

Game-Like Language Learning in 3-D Virtual Environments

Science.gov (United States)

Berns, Anke; Gonzalez-Pardo, Antonio; Camacho, David

2013-01-01

This paper presents our recent experiences with the design of game-like applications in 3-D virtual environments as well as its impact on student motivation and learning. Therefore our paper starts with a brief analysis of the motivational aspects of videogames and virtual worlds (VWs). We then go on to explore the possible benefits of both in the…

Building machines that learn and think like people

OpenAIRE

Lake, Brenden M.; Ullman, Tomer David; Tenenbaum, Joshua B; Gershman, Samuel J

2016-01-01

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitiv...

How to Learn English Grammar?

Institute of Scientific and Technical Information of China (English)

肖琳燃

2017-01-01

Grammar is an aspect of language about which learners have different opinions. Some learners are very interested in ifnding out or learning grammar rules and doing lots of grammar exercises. Others hate grammar and think it is the most boring part of learning a new language. Whatever opinion you have, however, you cannot escape from grammar; it is in every sentence you read or write, speak or hear. Grammar is simply the word for the rules that people follow when they use a language. We need those rules in the same way as we need the rules in a game. If there are no rules, or if everybody follows their own rules, the game would soon break down. It's the same with language; without rules we would not be able to communicate with other people. So you cannot escape from grammar, but the key question here is: what is the best way to learn grammar? You can learn the rules of a game by simply playing the game. You will certainly make mistakes; you may even get hurt. Eventually, however, you will know how to play. Of course, the rules of a language are very much more complicated than the rules of any game, but in fact this is exactly how you learned your own language. Nobody taught you the rules of your mother tongue as you were growing up but now you never make a grammar mistake.

A Combination of Machine Learning and Cerebellar-like Neural Networks for the Motor Control and Motor Learning of the Fable Modular Robot

DEFF Research Database (Denmark)

Baira Ojeda, Ismael; Tolu, Silvia; Pacheco, Moises

2017-01-01

We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, in the form of a Unit Learning Machine. The LWPR algorithm optimizes...... the input space and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar-like microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar-like circuits including analytical...

A forecast-based STDP rule suitable for neuromorphic implementation.

Science.gov (United States)

Davies, S; Galluppi, F; Rast, A D; Furber, S B

2012-08-01

Artificial neural networks increasingly involve spiking dynamics to permit greater computational efficiency. This becomes especially attractive for on-chip implementation using dedicated neuromorphic hardware. However, both spiking neural networks and neuromorphic hardware have historically found difficulties in implementing efficient, effective learning rules. The best-known spiking neural network learning paradigm is Spike Timing Dependent Plasticity (STDP) which adjusts the strength of a connection in response to the time difference between the pre- and post-synaptic spikes. Approaches that relate learning features to the membrane potential of the post-synaptic neuron have emerged as possible alternatives to the more common STDP rule, with various implementations and approximations. Here we use a new type of neuromorphic hardware, SpiNNaker, which represents the flexible "neuromimetic" architecture, to demonstrate a new approach to this problem. Based on the standard STDP algorithm with modifications and approximations, a new rule, called STDP TTS (Time-To-Spike) relates the membrane potential with the Long Term Potentiation (LTP) part of the basic STDP rule. Meanwhile, we use the standard STDP rule for the Long Term Depression (LTD) part of the algorithm. We show that on the basis of the membrane potential it is possible to make a statistical prediction of the time needed by the neuron to reach the threshold, and therefore the LTP part of the STDP algorithm can be triggered when the neuron receives a spike. In our system these approximations allow efficient memory access, reducing the overall computational time and the memory bandwidth required. The improvements here presented are significant for real-time applications such as the ones for which the SpiNNaker system has been designed. We present simulation results that show the efficacy of this algorithm using one or more input patterns repeated over the whole time of the simulation. On-chip results show that

Learning Cultures

DEFF Research Database (Denmark)

Rasmussen, Lauge Baungaard

1998-01-01

the article present different concepts and modelsof learning. It discuss some strutural tendenciesof developing environmental management systemsand point out alternatives to increasing formalization of rules.......the article present different concepts and modelsof learning. It discuss some strutural tendenciesof developing environmental management systemsand point out alternatives to increasing formalization of rules....

Effects of Memorization of Rule Statements on Acquisition and Retention of Rule-Governed Behavior in a Computer-Based Learning Task.

Science.gov (United States)

Towle, Nelson J.

One hundred and twenty-four high school students were randomly assigned to four groups: 33 subjects memorized the rule statement before, 29 subjects memorized the rule statement during, and 30 subjects memorized the rule statement after instruction in rule application skills. Thirty-two subjects were not required to memorize rule statements.…

Predictions of the spontaneous symmetry-breaking theory for visual code completeness and spatial scaling in single-cell learning rules.

Science.gov (United States)

Webber, C J

2001-05-01

This article shows analytically that single-cell learning rules that give rise to oriented and localized receptive fields, when their synaptic weights are randomly and independently initialized according to a plausible assumption of zero prior information, will generate visual codes that are invariant under two-dimensional translations, rotations, and scale magnifications, provided that the statistics of their training images are sufficiently invariant under these transformations. Such codes span different image locations, orientations, and size scales with equal economy. Thus, single-cell rules could account for the spatial scaling property of the cortical simple-cell code. This prediction is tested computationally by training with natural scenes; it is demonstrated that a single-cell learning rule can give rise to simple-cell receptive fields spanning the full range of orientations, image locations, and spatial frequencies (except at the extreme high and low frequencies at which the scale invariance of the statistics of digitally sampled images must ultimately break down, because of the image boundary and the finite pixel resolution). Thus, no constraint on completeness, or any other coupling between cells, is necessary to induce the visual code to span wide ranges of locations, orientations, and size scales. This prediction is made using the theory of spontaneous symmetry breaking, which we have previously shown can also explain the data-driven self-organization of a wide variety of transformation invariances in neurons' responses, such as the translation invariance of complex cell response.

Detecting Solar-like Oscillations in Red Giants with Deep Learning

Science.gov (United States)

Hon, Marc; Stello, Dennis; Zinn, Joel C.

2018-05-01

Time-resolved photometry of tens of thousands of red giant stars from space missions like Kepler and K2 has created the need for automated asteroseismic analysis methods. The first and most fundamental step in such analysis is to identify which stars show oscillations. It is critical that this step be performed with no, or little, detection bias, particularly when performing subsequent ensemble analyses that aim to compare the properties of observed stellar populations with those from galactic models. However, an efficient, automated solution to this initial detection step still has not been found, meaning that expert visual inspection of data from each star is required to obtain the highest level of detections. Hence, to mimic how an expert eye analyzes the data, we use supervised deep learning to not only detect oscillations in red giants, but also to predict the location of the frequency at maximum power, ν max, by observing features in 2D images of power spectra. By training on Kepler data, we benchmark our deep-learning classifier against K2 data that are given detections by the expert eye, achieving a detection accuracy of 98% on K2 Campaign 6 stars and a detection accuracy of 99% on K2 Campaign 3 stars. We further find that the estimated uncertainty of our deep-learning-based ν max predictions is about 5%. This is comparable to human-level performance using visual inspection. When examining outliers, we find that the deep-learning results are more likely to provide robust ν max estimates than the classical model-fitting method.

Drivers of Changes in Product Development Rules

DEFF Research Database (Denmark)

Christiansen, John K.; Varnes, Claus J.

2015-01-01

regimes. However, the analysis here indicates that there are different drivers, both internal and external, that cause companies to adopt new rules or modify their existing ones, such as changes in organizational structures, organizational conflicts, and changes in ownership or strategy. In addition......Purpose: - The purpose of this research is to investigate the drivers that induce companies to change their rules for managing product development. Most companies use a form of rule-based management approach, but surprisingly little is known about what makes companies change these rules...... 10 years based on three rounds of interviews with 40 managers. Findings: - Previous research has assumed that the dynamics of product development rules are based on internal learning processes, and that increasingly competent management will stimulate the implementation of newer and more complex rule...

A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning.

Science.gov (United States)

Tan, Javan; Quek, Chai

2010-06-01

Self-organizing neurofuzzy approaches have matured in their online learning of fuzzy-associative structures under time-invariant conditions. To maximize their operative value for online reasoning, these self-sustaining mechanisms must also be able to reorganize fuzzy-associative knowledge in real-time dynamic environments. Hence, it is critical to recognize that they would require self-reorganizational skills to rebuild fluid associative structures when their existing organizations fail to respond well to changing circumstances. In this light, while Hebbian theory (Hebb, 1949) is the basic computational framework for associative learning, it is less attractive for time-variant online learning because it suffers from stability limitations that impedes unlearning. Instead, this paper adopts the Bienenstock-Cooper-Munro (BCM) theory of neurological learning via meta-plasticity principles (Bienenstock et al., 1982) that provides for both online associative and dissociative learning. For almost three decades, BCM theory has been shown to effectively brace physiological evidence of synaptic potentiation (association) and depression (dissociation) into a sound mathematical framework for computational learning. This paper proposes an interpretation of the BCM theory of meta-plasticity for an online self-reorganizing fuzzy-associative learning system to realize online-reasoning capabilities. Experimental findings are twofold: 1) the analysis using S&P-500 stock index illustrated that the self-reorganizing approach could follow the trajectory shifts in the time-variant S&P-500 index for about 60 years, and 2) the benchmark profiles showed that the fuzzy-associative approach yielded comparable results with other fuzzy-precision models with similar online objectives.

Phonological Concept Learning.

Science.gov (United States)

Moreton, Elliott; Pater, Joe; Pertsova, Katya

2017-01-01

Linguistic and non-linguistic pattern learning have been studied separately, but we argue for a comparative approach. Analogous inductive problems arise in phonological and visual pattern learning. Evidence from three experiments shows that human learners can solve them in analogous ways, and that human performance in both cases can be captured by the same models. We test GMECCS (Gradual Maximum Entropy with a Conjunctive Constraint Schema), an implementation of the Configural Cue Model (Gluck & Bower, ) in a Maximum Entropy phonotactic-learning framework (Goldwater & Johnson, ; Hayes & Wilson, ) with a single free parameter, against the alternative hypothesis that learners seek featurally simple algebraic rules ("rule-seeking"). We study the full typology of patterns introduced by Shepard, Hovland, and Jenkins () ("SHJ"), instantiated as both phonotactic patterns and visual analogs, using unsupervised training. Unlike SHJ, Experiments 1 and 2 found that both phonotactic and visual patterns that depended on fewer features could be more difficult than those that depended on more features, as predicted by GMECCS but not by rule-seeking. GMECCS also correctly predicted performance differences between stimulus subclasses within each pattern. A third experiment tried supervised training (which can facilitate rule-seeking in visual learning) to elicit simple rule-seeking phonotactic learning, but cue-based behavior persisted. We conclude that similar cue-based cognitive processes are available for phonological and visual concept learning, and hence that studying either kind of learning can lead to significant insights about the other. Copyright © 2015 Cognitive Science Society, Inc.

Working memory supports inference learning just like classification learning.

Science.gov (United States)

Craig, Stewart; Lewandowsky, Stephan

2013-08-01

Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.

Associative memory in phasing neuron networks

Energy Technology Data Exchange (ETDEWEB)

Nair, Niketh S [ORNL; Bochove, Erik J. [United States Air Force Research Laboratory, Kirtland Air Force Base; Braiman, Yehuda [ORNL

2014-01-01

We studied pattern formation in a network of coupled Hindmarsh-Rose model neurons and introduced a new model for associative memory retrieval using networks of Kuramoto oscillators. Hindmarsh-Rose Neural Networks can exhibit a rich set of collective dynamics that can be controlled by their connectivity. Specifically, we showed an instance of Hebb's rule where spiking was correlated with network topology. Based on this, we presented a simple model of associative memory in coupled phase oscillators.

Delayed rule following.

Science.gov (United States)

Schmitt, D R

2001-01-01

Although the elements of a fully stated rule (discriminative stimulus [S(D)], some behavior, and a consequence) can occur nearly contemporaneously with the statement of the rule, there is often a delay between the rule statement and the S(D). The effects of this delay on rule following have not been studied in behavior analysis, but they have been investigated in rule-like settings in the areas of prospective memory (remembering to do something in the future) and goal pursuit. Discriminative events for some behavior can be event based (a specific setting stimulus) or time based. The latter are more demanding with respect to intention following and show age-related deficits. Studies suggest that the specificity with which the components of a rule (termed intention) are stated has a substantial effect on intention following, with more detailed specifications increasing following. Reminders of an intention, too, are most effective when they refer specifically to both the behavior and its occasion. Covert review and written notes are two effective strategies for remembering everyday intentions, but people who use notes appear not to be able to switch quickly to covert review. By focusing on aspects of the setting and rule structure, research on prospective memory and goal pursuit expands the agenda for a more complete explanation of rule effects.

q-state Potts-glass neural network based on pseudoinverse rule

International Nuclear Information System (INIS)

Xiong Daxing; Zhao Hong

2010-01-01

We study the q-state Potts-glass neural network with the pseudoinverse (PI) rule. Its performance is investigated and compared with that of the counterpart network with the Hebbian rule instead. We find that there exists a critical point of q, i.e., q cr =14, below which the storage capacity and the retrieval quality can be greatly improved by introducing the PI rule. We show that the dynamics of the neural networks constructed with the two learning rules respectively are quite different; but however, regardless of the learning rules, in the q-state Potts-glass neural networks with q≥3 there is a common novel dynamical phase in which the spurious memories are completely suppressed. This property has never been noticed in the symmetric feedback neural networks. Free from the spurious memories implies that the multistate Potts-glass neural networks would not be trapped in the metastable states, which is a favorable property for their applications.

Generating Concise Rules for Human Motion Retrieval

Science.gov (United States)

Mukai, Tomohiko; Wakisaka, Ken-Ichi; Kuriyama, Shigeru

This paper proposes a method for retrieving human motion data with concise retrieval rules based on the spatio-temporal features of motion appearance. Our method first converts motion clip into a form of clausal language that represents geometrical relations between body parts and their temporal relationship. A retrieval rule is then learned from the set of manually classified examples using inductive logic programming (ILP). ILP automatically discovers the essential rule in the same clausal form with a user-defined hypothesis-testing procedure. All motions are indexed using this clausal language, and the desired clips are retrieved by subsequence matching using the rule. Such rule-based retrieval offers reasonable performance and the rule can be intuitively edited in the same language form. Consequently, our method enables efficient and flexible search from a large dataset with simple query language.

Listen, learn, like! Dorsolateral prefrontal cortex involved in the mere exposure effect in music.

Science.gov (United States)

Green, Anders C; Bærentsen, Klaus B; Stødkilde-Jørgensen, Hans; Roepstorff, Andreas; Vuust, Peter

2012-01-01

We used functional magnetic resonance imaging to investigate the neural basis of the mere exposure effect in music listening, which links previous exposure to liking. Prior to scanning, participants underwent a learning phase, where exposure to melodies was systematically varied. During scanning, participants rated liking for each melody and, later, their recognition of them. Participants showed learning effects, better recognising melodies heard more often. Melodies heard most often were most liked, consistent with the mere exposure effect. We found neural activations as a function of previous exposure in bilateral dorsolateral prefrontal and inferior parietal cortex, probably reflecting retrieval and working memory-related processes. This was despite the fact that the task during scanning was to judge liking, not recognition, thus suggesting that appreciation of music relies strongly on memory processes. Subjective liking per se caused differential activation in the left hemisphere, of the anterior insula, the caudate nucleus, and the putamen.

Class association rules mining from students’ test data (Abstract)

NARCIS (Netherlands)

Romero, C.; Ventura, S.; Vasilyeva, E.; Pechenizkiy, M.; Baker, de R.S.J.; Merceron, A.; Pavlik Jr., P.I.

2010-01-01

In this paper we propose the use of a special type of association rules mining for discovering interesting relationships from the students’ test data collected in our case with Moodle learning management system (LMS). Particularly, we apply Class Association Rule (CAR) mining to different data

A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.

Science.gov (United States)

Siri, Benoît; Berry, Hugues; Cessac, Bruno; Delord, Bruno; Quoy, Mathias

2008-12-01

We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.

Learning the Rules of the Game

Science.gov (United States)

Smith, Donald A.

2018-01-01

Games have often been used in the classroom to teach physics ideas and concepts, but there has been less published on games that can be used to teach scientific thinking. D. Maloney and M. Masters describe an activity in which students attempt to infer rules to a game from a history of moves, but the students do not actually play the game. Giving…

Learning to think like a nurse: stories from new nurse graduates.

Science.gov (United States)

Etheridge, Sharon A

2007-01-01

One aim of nursing education is to help students learn to be beginning practitioners, which includes making clinical judgments that ensure patient safety. Clinical judgments often determine how quickly nurses detect a life-threatening complication, how soon patients leave the hospital, or how quickly patients learn to take care of themselves. However, current research shows that new graduates do not perform well when making clinical judgments, despite having graduated from accredited schools of nursing and passing the NCLEX examination. This descriptive, qualitative study examined the perceptions of recent nursing graduates about learning to make clinical judgments. Graduates with baccalaureate degrees in nursing were interviewed three times in 9 months to determine their perceptions of how they learned to think like nurses. The results of this study should be useful in identifying strategies to help new graduates make the transition from students to registered nurses.

Listen, Learn, Like! Dorsolateral Prefrontal Cortex Involved in the Mere Exposure Effect in Music

Directory of Open Access Journals (Sweden)

Anders C. Green

2012-01-01

Full Text Available We used functional magnetic resonance imaging to investigate the neural basis of the mere exposure effect in music listening, which links previous exposure to liking. Prior to scanning, participants underwent a learning phase, where exposure to melodies was systematically varied. During scanning, participants rated liking for each melody and, later, their recognition of them. Participants showed learning effects, better recognising melodies heard more often. Melodies heard most often were most liked, consistent with the mere exposure effect. We found neural activations as a function of previous exposure in bilateral dorsolateral prefrontal and inferior parietal cortex, probably reflecting retrieval and working memory-related processes. This was despite the fact that the task during scanning was to judge liking, not recognition, thus suggesting that appreciation of music relies strongly on memory processes. Subjective liking per se caused differential activation in the left hemisphere, of the anterior insula, the caudate nucleus, and the putamen.

Student-Centered Support Systems to Sustain Logo-Like Learning

OpenAIRE

Martinez , Sylvia

2007-01-01

Conventional wisdom attributes the lack of effective technology use in classrooms to a shortage of professional development or poorly run professional development. At the same time, logo-like learning environments require teachers to develop more expertise not only in technology but also in pedagogy. This paper proposes that the perceived lack of technology professional development is a myth and that traditional professional development is ill-suited to teaching teachers how to create log...

Rules of (Student) Engagement

Science.gov (United States)

Buskist, William; Busler, Jessica N.; Kirby, Lauren A. J.

2018-01-01

Teachers often think of student engagement in terms of hands-on activities that get students involved in their courses. They seldom consider the larger aspects of the teaching--learning environment that often influence the extent to which students are willing to become engaged in their coursework. In this chapter, we describe five "rules of…

A fuzzy controller with a robust learning function

International Nuclear Information System (INIS)

Tanji, Jun-ichi; Kinoshita, Mitsuo

1987-01-01

A self-organizing fuzzy controller is able to use linguistic decision rules of control strategy and has a strong adaptive property by virture of its rule learning function. While a simple linguistic description of the learning algorithm first introduced by Procyk, et al. has much flexibility for applications to a wide range of different processes, its detailed formulation, in particular with control stability and learning process convergence, is not clear. In this paper, we describe the formulation of an analytical basis for a self-organizing fuzzy controller by using a method of model reference adaptive control systems (MRACS) for which stability in the adaptive loop is theoretically proven. A detailed formulation is described regarding performance evaluation and rule modification in the rule learning process of the controller. Furthermore, an improved learning algorithm using adaptive rule is proposed. An adaptive rule gives a modification coefficient for a rule change estimating the effect of disturbance occurrence in performance evaluation. The effect of introducing an adaptive rule to improve the learning convergency is described by using a simple iterative formulation. Simulation tests are presented for an application of the proposed self-organizing fuzzy controller to the pressure control system in a Boiling Water Reactor (BWR) plant. Results with the tests confirm the improved learning algorithm has strong convergent properties, even in a very disturbed environment. (author)

Abstract rule learning in 11- and 14-month-old infants.

Science.gov (United States)

Koulaguina, Elena; Shi, Rushen

2013-02-01

This study tests the hypothesis that distributional information can guide infants in the generalization of word order movement rules at the initial stage of language acquisition. Participants were 11- and 14-month-old infants. Stimuli were sentences in Russian, a language that was unknown to our infants. During training the word order of each sentence was transformed following a consistent pattern (e.g., ABC-BAC). During the test phase infants heard novel sentences that respected the trained rule and ones that violated the trained rule (i.e., a different transformation such as ABC-ACB). Stimuli words had highly variable phonological and morphological shapes. The cue available was the positional information of words and their non-adjacent relations across sentences. We found that 14-month-olds, but not 11-month-olds, showed evidence of abstract rule generalization to novel instances. The implications of this finding to early syntactic acquisition are discussed.

Dynamic Observation of Brain-Like Learning in a Ferroelectric Synapse Device

Science.gov (United States)

Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito; Fujii, Eiji; Tsujimura, Ayumu

2013-04-01

A brain-like learning function was implemented in an electronic synapse device using a ferroelectric-gate field effect transistor (FeFET). The FeFET was a bottom-gate type FET with a ZnO channel and a ferroelectric Pb(Zr,Ti)O3 (PZT) gate insulator. The synaptic weight, which is represented by the channel conductance of the FeFET, is updated by applying a gate voltage through a change in the ferroelectric polarization in the PZT. A learning function based on the symmetric spike-timing dependent synaptic plasticity was implemented in the synapse device using the multilevel weight update by applying a pulse gate voltage. The dynamic weighting and learning behavior in the synapse device was observed as a change in the membrane potential in a spiking neuron circuit.

Sleep promotes the extraction of grammatical rules.

Directory of Open Access Journals (Sweden)

Ingrid L C Nieuwenhuis

Full Text Available Grammar acquisition is a high level cognitive function that requires the extraction of complex rules. While it has been proposed that offline time might benefit this type of rule extraction, this remains to be tested. Here, we addressed this question using an artificial grammar learning paradigm. During a short-term memory cover task, eighty-one human participants were exposed to letter sequences generated according to an unknown artificial grammar. Following a time delay of 15 min, 12 h (wake or sleep or 24 h, participants classified novel test sequences as Grammatical or Non-Grammatical. Previous behavioral and functional neuroimaging work has shown that classification can be guided by two distinct underlying processes: (1 the holistic abstraction of the underlying grammar rules and (2 the detection of sequence chunks that appear at varying frequencies during exposure. Here, we show that classification performance improved after sleep. Moreover, this improvement was due to an enhancement of rule abstraction, while the effect of chunk frequency was unaltered by sleep. These findings suggest that sleep plays a critical role in extracting complex structure from separate but related items during integrative memory processing. Our findings stress the importance of alternating periods of learning with sleep in settings in which complex information must be acquired.

Rule of Thumb and Dynamic Programming

NARCIS (Netherlands)

Lettau, M.; Uhlig, H.F.H.V.S.

1995-01-01

This paper studies the relationships between learning about rules of thumb (represented by classifier systems) and dynamic programming. Building on a result about Markovian stochastic approximation algorithms, we characterize all decision functions that can be asymptotically obtained through

Learning unlearnable problems with perceptrons

Science.gov (United States)

Watkin, Timothy L. H.; Rau, Albrecht

1992-03-01

We study how well perceptrons learn to solve problems for which there is no perfect answer (the usual case), taking as examples a rule with a threshold, a rule in which the answer is not a monotonic function of the overlap between question and teacher, and a rule with many teachers (a ``hard'' unlearnable problem). In general there is a tendency for first-order transitions, even using spherical perceptrons, as networks compromise between conflicting requirements. Some existing learning schemes fail completely-occasionally even finding the worst possible solution; others are more successful. High-temperature learning seems more satisfactory than zero-temperature algorithms and avoids ``overlearning'' and ``overfitting,'' but care must be taken to avoid ``trapping'' in spurious free-energy minima. For some rules examples alone are not enough to learn from, and some prior information is required.

Contributions of Lateral and Orbital Frontal Regions to Abstract Rule Acquisition and Reversal in Monkeys

Science.gov (United States)

La Camera, Giancarlo; Bouret, Sebastien; Richmond, Barry J.

2018-01-01

The ability to learn and follow abstract rules relies on intact prefrontal regions including the lateral prefrontal cortex (LPFC) and the orbitofrontal cortex (OFC). Here, we investigate the specific roles of these brain regions in learning rules that depend critically on the formation of abstract concepts as opposed to simpler input-output associations. To this aim, we tested monkeys with bilateral removals of either LPFC or OFC on a rapidly learned task requiring the formation of the abstract concept of same vs. different. While monkeys with OFC removals were significantly slower than controls at both acquiring and reversing the concept-based rule, monkeys with LPFC removals were not impaired in acquiring the task, but were significantly slower at rule reversal. Neither group was impaired in the acquisition or reversal of a delayed visual cue-outcome association task without a concept-based rule. These results suggest that OFC is essential for the implementation of a concept-based rule, whereas LPFC seems essential for its modification once established. PMID:29615854

Online Rule Generation Software Process Model

OpenAIRE

Sudeep Marwaha; Alka Aroa; Satma M C; Rajni Jain; R C Goyal

2013-01-01

For production systems like expert systems, a rule generation software can facilitate the faster deployment. The software process model for rule generation using decision tree classifier refers to the various steps required to be executed for the development of a web based software model for decision rule generation. The Royce’s final waterfall model has been used in this paper to explain the software development process. The paper presents the specific output of various steps of modified wat...

Learning by statistical cooperation of self-interested neuron-like computing elements.

Science.gov (United States)

Barto, A G

1985-01-01

Since the usual approaches to cooperative computation in networks of neuron-like computating elements do not assume that network components have any "preferences", they do not make substantive contact with game theoretic concepts, despite their use of some of the same terminology. In the approach presented here, however, each network component, or adaptive element, is a self-interested agent that prefers some inputs over others and "works" toward obtaining the most highly preferred inputs. Here we describe an adaptive element that is robust enough to learn to cooperate with other elements like itself in order to further its self-interests. It is argued that some of the longstanding problems concerning adaptation and learning by networks might be solvable by this form of cooperativity, and computer simulation experiments are described that show how networks of self-interested components that are sufficiently robust can solve rather difficult learning problems. We then place the approach in its proper historical and theoretical perspective through comparison with a number of related algorithms. A secondary aim of this article is to suggest that beyond what is explicitly illustrated here, there is a wealth of ideas from game theory and allied disciplines such as mathematical economics that can be of use in thinking about cooperative computation in both nervous systems and man-made systems.

Females do not express learned helplessness like males do.

Science.gov (United States)

Dalla, Christina; Edgecomb, Carol; Whetstone, Abigail S; Shors, Tracey J

2008-06-01

Women are more likely than men to suffer from stress-related mental disorders, such as depression. In the present experiments, we identified sex differences in one of the most common animal models of depression, that of learned helplessness. Male and female rats were trained to escape a mild footshock each day for 7 days (controllable stress). Each rat was yoked to another rat that could not escape (uncontrollable stress), but was exposed to the same amount of shock. One day later, all stressed rats and unstressed controls were tested on a more difficult escape task in a different context. Most males exposed to uncontrollable stress did not learn to escape and were therefore helpless. In contrast, most females did learn to escape on the more difficult escape task, irrespective of whether they had been exposed to controllable or uncontrollable stress. The sex differences in helplessness behavior were not dependent on the presence of sex hormones in adulthood, because neither ovariectomy of females nor castration of males abolished them. The absence of helplessness in females was neither dependent on organizational effects of testosterone during the day of birth, because masculinized females did not express helplessness as adults. Thus, sex differences in helplessness behavior are independent of gonadal hormones in adulthood and testosterone exposure during perinatal development. Learned helplessness may not constitute a valid model for depressive behavior in women, at least as reflected by the response of female rats to operant conditioning procedures after stressful experience.

A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-like Network

DEFF Research Database (Denmark)

Baira Ojeda, Ismael; Tolu, Silvia; Lund, Henrik Hautop

2017-01-01

Combining Fable robot, a modular robot, with a neuroinspired controller, we present the proof of principle of a system that can scale to several neurally controlled compliant modules. The motor control and learning of a robot module are carried out by a Unit Learning Machine (ULM) that embeds...... the Locally Weighted Projection Regression algorithm (LWPR) and a spiking cerebellar-like microcircuit. The LWPR guarantees both an optimized representation of the input space and the learning of the dynamic internal model (IM) of the robot. However, the cerebellar-like sub-circuit integrates LWPR input...

Blackboxing: social learning strategies and cultural evolution.

Science.gov (United States)

Heyes, Cecilia

2016-05-05

Social learning strategies (SLSs) enable humans, non-human animals, and artificial agents to make adaptive decisions aboutwhenthey should copy other agents, andwhothey should copy. Behavioural ecologists and economists have discovered an impressive range of SLSs, and explored their likely impact on behavioural efficiency and reproductive fitness while using the 'phenotypic gambit'; ignoring, or remaining deliberately agnostic about, the nature and origins of the cognitive processes that implement SLSs. Here I argue that this 'blackboxing' of SLSs is no longer a viable scientific strategy. It has contributed, through the 'social learning strategies tournament', to the premature conclusion that social learning is generally better than asocial learning, and to a deep puzzle about the relationship between SLSs and cultural evolution. The puzzle can be solved by recognizing that whereas most SLSs are 'planetary'--they depend on domain-general cognitive processes--some SLSs, found only in humans, are 'cook-like'--they depend on explicit, metacognitive rules, such ascopy digital natives. These metacognitive SLSs contribute to cultural evolution by fostering the development of processes that enhance the exclusivity, specificity, and accuracy of social learning. © 2016 The Author(s).

Blackboxing: social learning strategies and cultural evolution

Science.gov (United States)

Heyes, Cecilia

2016-01-01

Social learning strategies (SLSs) enable humans, non-human animals, and artificial agents to make adaptive decisions about when they should copy other agents, and who they should copy. Behavioural ecologists and economists have discovered an impressive range of SLSs, and explored their likely impact on behavioural efficiency and reproductive fitness while using the ‘phenotypic gambit’; ignoring, or remaining deliberately agnostic about, the nature and origins of the cognitive processes that implement SLSs. Here I argue that this ‘blackboxing' of SLSs is no longer a viable scientific strategy. It has contributed, through the ‘social learning strategies tournament', to the premature conclusion that social learning is generally better than asocial learning, and to a deep puzzle about the relationship between SLSs and cultural evolution. The puzzle can be solved by recognizing that whereas most SLSs are ‘planetary'—they depend on domain-general cognitive processes—some SLSs, found only in humans, are ‘cook-like'—they depend on explicit, metacognitive rules, such as copy digital natives. These metacognitive SLSs contribute to cultural evolution by fostering the development of processes that enhance the exclusivity, specificity, and accuracy of social learning. PMID:27069046

Comparison of Heuristics for Inhibitory Rule Optimization

KAUST Repository

Alsolami, Fawaz; Chikalov, Igor; Moshkov, Mikhail

2014-01-01

Friedman test with Nemenyi post-hoc are used to compare the greedy algorithms statistically against each other for length and coverage. The experiments are carried out on real datasets from UCI Machine Learning Repository. For leading heuristics, the constructed rules are compared with optimal ones obtained based on dynamic programming approach. The results seem to be promising for the best heuristics: the average relative difference between length (coverage) of constructed and optimal rules is at most 2.27% (7%, respectively). Furthermore, the quality of classifiers based on sets of inhibitory rules constructed by the considered heuristics are compared against each other, and the results show that the three best heuristics from the point of view classification accuracy coincides with the three well-performed heuristics from the point of view of rule length minimization.

Ellipsoidal fuzzy learning for smart car platoons

Science.gov (United States)

Dickerson, Julie A.; Kosko, Bart

1993-12-01

A neural-fuzzy system combined supervised and unsupervised learning to find and tune the fuzzy-rules. An additive fuzzy system approximates a function by covering its graph with fuzzy rules. A fuzzy rule patch can take the form of an ellipsoid in the input-output space. Unsupervised competitive learning found the statistics of data clusters. The covariance matrix of each synaptic quantization vector defined on ellipsoid centered at the centroid of the data cluster. Tightly clustered data gave smaller ellipsoids or more certain rules. Sparse data gave larger ellipsoids or less certain rules. Supervised learning tuned the ellipsoids to improve the approximation. The supervised neural system used gradient descent to find the ellipsoidal fuzzy patches. It locally minimized the mean-squared error of the fuzzy approximation. Hybrid ellipsoidal learning estimated the control surface for a smart car controller.

Memory and learning with rapid audiovisual sequences

Science.gov (United States)

Keller, Arielle S.; Sekuler, Robert

2015-01-01

We examined short-term memory for sequences of visual stimuli embedded in varying multisensory contexts. In two experiments, subjects judged the structure of the visual sequences while disregarding concurrent, but task-irrelevant auditory sequences. Stimuli were eight-item sequences in which varying luminances and frequencies were presented concurrently and rapidly (at 8 Hz). Subjects judged whether the final four items in a visual sequence identically replicated the first four items. Luminances and frequencies in each sequence were either perceptually correlated (Congruent) or were unrelated to one another (Incongruent). Experiment 1 showed that, despite encouragement to ignore the auditory stream, subjects' categorization of visual sequences was strongly influenced by the accompanying auditory sequences. Moreover, this influence tracked the similarity between a stimulus's separate audio and visual sequences, demonstrating that task-irrelevant auditory sequences underwent a considerable degree of processing. Using a variant of Hebb's repetition design, Experiment 2 compared musically trained subjects and subjects who had little or no musical training on the same task as used in Experiment 1. Test sequences included some that intermittently and randomly recurred, which produced better performance than sequences that were generated anew for each trial. The auditory component of a recurring audiovisual sequence influenced musically trained subjects more than it did other subjects. This result demonstrates that stimulus-selective, task-irrelevant learning of sequences can occur even when such learning is an incidental by-product of the task being performed. PMID:26575193

Memory and learning with rapid audiovisual sequences.

Science.gov (United States)

Keller, Arielle S; Sekuler, Robert

2015-01-01

We examined short-term memory for sequences of visual stimuli embedded in varying multisensory contexts. In two experiments, subjects judged the structure of the visual sequences while disregarding concurrent, but task-irrelevant auditory sequences. Stimuli were eight-item sequences in which varying luminances and frequencies were presented concurrently and rapidly (at 8 Hz). Subjects judged whether the final four items in a visual sequence identically replicated the first four items. Luminances and frequencies in each sequence were either perceptually correlated (Congruent) or were unrelated to one another (Incongruent). Experiment 1 showed that, despite encouragement to ignore the auditory stream, subjects' categorization of visual sequences was strongly influenced by the accompanying auditory sequences. Moreover, this influence tracked the similarity between a stimulus's separate audio and visual sequences, demonstrating that task-irrelevant auditory sequences underwent a considerable degree of processing. Using a variant of Hebb's repetition design, Experiment 2 compared musically trained subjects and subjects who had little or no musical training on the same task as used in Experiment 1. Test sequences included some that intermittently and randomly recurred, which produced better performance than sequences that were generated anew for each trial. The auditory component of a recurring audiovisual sequence influenced musically trained subjects more than it did other subjects. This result demonstrates that stimulus-selective, task-irrelevant learning of sequences can occur even when such learning is an incidental by-product of the task being performed.

Explanation-based learning in infancy.

Science.gov (United States)

Baillargeon, Renée; DeJong, Gerald F

2017-10-01

In explanation-based learning (EBL), domain knowledge is leveraged in order to learn general rules from few examples. An explanation is constructed for initial exemplars and is then generalized into a candidate rule that uses only the relevant features specified in the explanation; if the rule proves accurate for a few additional exemplars, it is adopted. EBL is thus highly efficient because it combines both analytic and empirical evidence. EBL has been proposed as one of the mechanisms that help infants acquire and revise their physical rules. To evaluate this proposal, 11- and 12-month-olds (n = 260) were taught to replace their current support rule (that an object is stable when half or more of its bottom surface is supported) with a more sophisticated rule (that an object is stable when half or more of the entire object is supported). Infants saw teaching events in which asymmetrical objects were placed on a base, followed by static test displays involving a novel asymmetrical object and a novel base. When the teaching events were designed to facilitate EBL, infants learned the new rule with as few as two (12-month-olds) or three (11-month-olds) exemplars. When the teaching events were designed to impede EBL, however, infants failed to learn the rule. Together, these results demonstrate that even infants, with their limited knowledge about the world, benefit from the knowledge-based approach of EBL.

What Older People Like to Play: Genre Preferences and Acceptance of Casual Games

OpenAIRE

Chesham, Alvin; Wyss, Patric; M?ri, Ren? Martin; Mosimann, Urs Peter; Nef, Tobias

2017-01-01

BACKGROUND In recent computerized cognitive training studies, video games have emerged as a promising tool that can benefit cognitive function and well-being. Whereas most video game training studies have used first-person shooter (FPS) action video games, subsequent studies found that older adults dislike this type of game and generally prefer casual video games (CVGs), which are a subtype of video games that are easy to learn and use simple rules and interfaces. Like other video games, ...

Increasing the thermal stability of cellulase C using rules learned from thermophilic proteins: a pilot study.

Science.gov (United States)

Németh, Attila; Kamondi, Szilárd; Szilágyi, András; Magyar, Csaba; Kovári, Zoltán; Závodszky, Péter

2002-05-02

Some structural features underlying the increased thermostability of enzymes from thermophilic organisms relative to their homologues from mesophiles are known from earlier studies. We used cellulase C from Clostridium thermocellum to test whether thermostability can be increased by mutations designed using rules learned from thermophilic proteins. Cellulase C has a TIM barrel fold with an additional helical subdomain. We designed and produced a number of mutants with the aim to increase its thermostability. Five mutants were designed to create new electrostatic interactions. They all retained catalytic activity but exhibited decreased thermostability relative to the wild-type enzyme. Here, the stabilizing contributions are obviously smaller than the destabilization caused by the introduction of the new side chains. In another mutant, the small helical subdomain was deleted. This mutant lost activity but its melting point was only 3 degrees C lower than that of the wild-type enzyme, which suggests that the subdomain is an independent folding unit and is important for catalytic function. A double mutant was designed to introduce a new disulfide bridge into the enzyme. This mutant is active and has an increased stability (deltaT(m)=3 degrees C, delta(deltaG(u))=1.73 kcal/mol) relative to the wild-type enzyme. Reduction of the disulfide bridge results in destabilization and an altered thermal denaturation behavior. We conclude that rules learned from thermophilic proteins cannot be used in a straightforward way to increase the thermostability of a protein. Creating a crosslink such as a disulfide bond is a relatively sure-fire method but the stabilization may be smaller than calculated due to coupled destabilizing effects.

Comparison of Heuristics for Inhibitory Rule Optimization

KAUST Repository

Alsolami, Fawaz

2014-09-13

Knowledge representation and extraction are very important tasks in data mining. In this work, we proposed a variety of rule-based greedy algorithms that able to obtain knowledge contained in a given dataset as a series of inhibitory rules containing an expression “attribute ≠ value” on the right-hand side. The main goal of this paper is to determine based on rule characteristics, rule length and coverage, whether the proposed rule heuristics are statistically significantly different or not; if so, we aim to identify the best performing rule heuristics for minimization of rule length and maximization of rule coverage. Friedman test with Nemenyi post-hoc are used to compare the greedy algorithms statistically against each other for length and coverage. The experiments are carried out on real datasets from UCI Machine Learning Repository. For leading heuristics, the constructed rules are compared with optimal ones obtained based on dynamic programming approach. The results seem to be promising for the best heuristics: the average relative difference between length (coverage) of constructed and optimal rules is at most 2.27% (7%, respectively). Furthermore, the quality of classifiers based on sets of inhibitory rules constructed by the considered heuristics are compared against each other, and the results show that the three best heuristics from the point of view classification accuracy coincides with the three well-performed heuristics from the point of view of rule length minimization.

Comparison of rule induction, decision trees and formal concept analysis approaches for classification

Science.gov (United States)

Kotelnikov, E. V.; Milov, V. R.

2018-05-01

Rule-based learning algorithms have higher transparency and easiness to interpret in comparison with neural networks and deep learning algorithms. These properties make it possible to effectively use such algorithms to solve descriptive tasks of data mining. The choice of an algorithm depends also on its ability to solve predictive tasks. The article compares the quality of the solution of the problems with binary and multiclass classification based on the experiments with six datasets from the UCI Machine Learning Repository. The authors investigate three algorithms: Ripper (rule induction), C4.5 (decision trees), In-Close (formal concept analysis). The results of the experiments show that In-Close demonstrates the best quality of classification in comparison with Ripper and C4.5, however the latter two generate more compact rule sets.

Precursors to natural grammar learning: preliminary evidence from 4-month-old infants.

Science.gov (United States)

Friederici, Angela D; Mueller, Jutta L; Oberecker, Regine

2011-03-22

When learning a new language, grammar--although difficult--is very important, as grammatical rules determine the relations between the words in a sentence. There is evidence that very young infants can detect rules determining the relation between neighbouring syllables in short syllable sequences. A critical feature of all natural languages, however, is that many grammatical rules concern the dependency relation between non-neighbouring words or elements in a sentence i.e. between an auxiliary and verb inflection as in is singing. Thus, the issue of when and how children begin to recognize such non-adjacent dependencies is fundamental to our understanding of language acquisition. Here, we use brain potential measures to demonstrate that the ability to recognize dependencies between non-adjacent elements in a novel natural language is observable by the age of 4 months. Brain responses indicate that 4-month-old German infants discriminate between grammatical and ungrammatical dependencies in auditorily presented Italian sentences after only brief exposure to correct sentences of the same type. As the grammatical dependencies are realized by phonologically distinct syllables the present data most likely reflect phonologically based implicit learning mechanisms which can serve as a precursor to later grammar learning.

An Historical Analysis of Monetary Policy Rules

OpenAIRE

John B. Taylor

1998-01-01

This paper examines several episodes in U.S. monetary history using the framework of an interest rate rule for monetary policy. The main finding is that a monetary policy rule in which the interest rate responds to inflation and real output more aggressively than it did in the 1960s and 1970s, or than during the time of the international gold standard, and more like the late 1980s and 1990s, is a good policy rule. Moreover, if one defines rule, then such mistakes have been associated with eit...

Intergroup Bias in Parliamentary Rule Enforcement

DEFF Research Database (Denmark)

Hjorth, Frederik Georg

2016-01-01

Political actors are often assigned roles requiring them to enforce rules without giving in-groups special treatment. But are such institutional roles likely to be successful? Here, I exploit a special case of exogenously assigned intergroup relations: debates in the Danish Parliament, in which P...... of clear rules, complete observability, and a tradition of parliamentary cooperation....

Who Knows? Metacognitive Social Learning Strategies.

Science.gov (United States)

Heyes, Cecilia

2016-03-01

To make good use of learning from others (social learning), we need to learn from the right others; from agents who know better than we do. Research on social learning strategies (SLSs) has identified rules that focus social learning on the right agents, and has shown that the behaviour of many animals conforms to these rules. However, it has not asked what the rules are made of, that is, about the cognitive processes implementing SLSs. Here, I suggest that most SLSs depend on domain-general, sensorimotor processes. However, some SLSs have the characteristics tacitly ascribed to all of them. These metacognitive SLSs represent 'who knows' in a conscious, reportable way, and have the power to promote cultural evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

Storage capacity of the Tilinglike Learning Algorithm

International Nuclear Information System (INIS)

Buhot, Arnaud; Gordon, Mirta B.

2001-01-01

The storage capacity of an incremental learning algorithm for the parity machine, the Tilinglike Learning Algorithm, is analytically determined in the limit of a large number of hidden perceptrons. Different learning rules for the simple perceptron are investigated. The usual Gardner-Derrida rule leads to a storage capacity close to the upper bound, which is independent of the learning algorithm considered

Decision rule classifiers for multi-label decision tables

KAUST Repository

Alsolami, Fawaz

2014-01-01

Recently, multi-label classification problem has received significant attention in the research community. This paper is devoted to study the effect of the considered rule heuristic parameters on the generalization error. The results of experiments for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. © 2014 Springer International Publishing.

Rule-governed behavior and behavioral anthropology.

Science.gov (United States)

Malott, R W

1988-01-01

According to cultural materialism, cultural practices result from the materialistic outcomes of those practices, not from sociobiological, mentalistic, or mystical predispositions (e.g., Hindus worship cows because, in the long run, that worship results in more food, not less food). However, according to behavior analysis, such materialistic outcomes do not reinforce or punish the cultural practices, because such outcomes are too delayed, too improbable, or individually too small to directly reinforce or punish the cultural practices (e.g., the food increase is too delayed to reinforce the cow worship). Therefore, the molar, materialistic contingencies need the support of molecular, behavioral contingencies. And according to the present theory of rule-governed behavior, the statement of rules describing those molar, materialistic contingencies can establish the needed molecular contingencies. Given the proper behavioral history, such rule statements combine with noncompliance to produce a learned aversive condition (often labeled fear, anxiety, or guilt). The termination of this aversive condition reinforces compliance, just as its presentation punishes noncompliance (e.g., the termination of guilt reinforces the tending to a sick cow). In addition, supernatural rules often supplement these materialistic rules. Furthermore, the production of both materialistic and supernatural rules needs cultural designers who understand the molar, materialistic contingencies.

Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA.

Science.gov (United States)

Ngo, T-D; Tran, T-D; Le, M-T; Thai, K-M

2016-09-01

The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acctrain-diverse = 84%; accinternal-test = 92% and accexternal-test = 100%). For ligand promiscuity between P-gp and NorA, perceptual maps and pharmacophore models were generated for the detection of rules and features. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening in an attempt to restore drug sensitivity in cancer cells and bacteria.

Learning and coding in biological neural networks

Science.gov (United States)

Fiete, Ila Rani

How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and

When more is less: Feedback effects in perceptual category learning ☆

Science.gov (United States)

Maddox, W. Todd; Love, Bradley C.; Glass, Brian D.; Filoteo, J. Vincent

2008-01-01

Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether their response was correct or incorrect, but are not informed of the correct category assignment. With full feedback subjects are informed of the correctness of their response and are also informed of the correct category assignment. An examination of the distinct neural circuits that subserve rule-based and information-integration category learning leads to the counterintuitive prediction that full feedback should facilitate rule-based learning but should also hinder information-integration learning. This prediction was supported in the experiment reported below. The implications of these results for theories of learning are discussed. PMID:18455155

Learning in Artificial Neural Systems

Science.gov (United States)

Matheus, Christopher J.; Hohensee, William E.

1987-01-01

This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

Boltzmann learning of parameters in cellular neural networks

DEFF Research Database (Denmark)

Hansen, Lars Kai

1992-01-01

The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified...

Fuzzy-logic based learning style prediction in e-learning using web ...

Indian Academy of Sciences (India)

tion, especially in web environments and proposes to use Fuzzy rules to handle the uncertainty in .... learning in safe and supportive environment ... working of the proposed Fuzzy-logic based learning style prediction in e-learning. Section 4.

Learning Expressive Linkage Rules for Entity Matching using Genetic Programming

OpenAIRE

Isele, Robert

2013-01-01

A central problem in data integration and data cleansing is to identify pairs of entities in data sets that describe the same real-world object. Many existing methods for matching entities rely on explicit linkage rules, which specify how two entities are compared for equivalence. Unfortunately, writing accurate linkage rules by hand is a non-trivial problem that requires detailed knowledge of the involved data sets. Another important issue is the efficient execution of link...

Dissociable Hippocampal and Amygdalar D1-like receptor contribution to Discriminated Pavlovian conditioned approach learning

Science.gov (United States)

Andrzejewski, Matthew E; Ryals, Curtis

2016-01-01

Pavlovian conditioning is an elementary form of reward-related behavioral adaptation. The mesolimbic dopamine system is widely considered to mediate critical aspects of reward-related learning. For example, initial acquisition of positively-reinforced operant behavior requires dopamine (DA) D1 receptor (D1R) activation in the basolateral amygdala (BLA), central nucleus of the amygdala (CeA), and the ventral subiculum (vSUB). However, the role of D1R activation in these areas on appetitive, non-drug-related, Pavlovian learning is not currently known. In separate experiments, microinfusions of the D1-like receptor antagonist SCH-23390 (3.0 nmol/0.5 μL per side) into the amygdala and subiculum preceded discriminated Pavlovian conditioned approach (dPCA) training sessions. D1-like antagonism in all three structures impaired the acquisition of discriminated approach, but had no effect on performance after conditioning was asymptotic. Moreover, dissociable effects of D1-like antagonism in the three structures on components of discriminated responding were obtained. Lastly, the lack of latent inhibition in drug-treated groups may elucidate the role of D1-like in reward-related Pavlovian conditioning. The present data suggest a role for the D1 receptors in the amygdala and hippocampus in learning the significance of conditional stimuli, but not in the expression of conditional responses. PMID:26632336

Fear of negative evaluation biases social evaluation inference: evidence from a probabilistic learning task.

Science.gov (United States)

Button, Katherine S; Kounali, Daphne; Stapinski, Lexine; Rapee, Ronald M; Lewis, Glyn; Munafò, Marcus R

2015-01-01

Fear of negative evaluation (FNE) defines social anxiety yet the process of inferring social evaluation, and its potential role in maintaining social anxiety, is poorly understood. We developed an instrumental learning task to model social evaluation learning, predicting that FNE would specifically bias learning about the self but not others. During six test blocks (3 self-referential, 3 other-referential), participants (n = 100) met six personas and selected a word from a positive/negative pair to finish their social evaluation sentences "I think [you are / George is]…". Feedback contingencies corresponded to 3 rules, liked, neutral and disliked, with P[positive word correct] = 0.8, 0.5 and 0.2, respectively. As FNE increased participants selected fewer positive words (β = -0.4, 95% CI -0.7, -0.2, p = 0.001), which was strongest in the self-referential condition (FNE × condition 0.28, 95% CI 0.01, 0.54, p = 0.04), and the neutral and dislike rules (FNE × condition × rule, p = 0.07). At low FNE the proportion of positive words selected for self-neutral and self-disliked greatly exceeded the feedback contingency, indicating poor learning, which improved as FNE increased. FNE is associated with differences in processing social-evaluative information specifically about the self. At low FNE this manifests as insensitivity to learning negative self-referential evaluation. High FNE individuals are equally sensitive to learning positive or negative evaluation, which although objectively more accurate, may have detrimental effects on mental health.

Stochastic Variational Learning in Recurrent Spiking Networks

Directory of Open Access Journals (Sweden)

Danilo eJimenez Rezende

2014-04-01

Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

Stochastic variational learning in recurrent spiking networks.

Science.gov (United States)

Jimenez Rezende, Danilo; Gerstner, Wulfram

2014-01-01

The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.

Science.gov (United States)

D'Souza, Prashanth; Liu, Shih-Chii; Hahnloser, Richard H R

2010-03-09

It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensory-guided processing.

What do animals learn in artificial grammar studies?

Science.gov (United States)

Beckers, Gabriël J L; Berwick, Robert C; Okanoya, Kazuo; Bolhuis, Johan J

2017-10-01

Artificial grammar learning is a popular paradigm to study syntactic ability in nonhuman animals. Subjects are first trained to recognize strings of tokens that are sequenced according to grammatical rules. Next, to test if recognition depends on grammaticality, subjects are presented with grammar-consistent and grammar-violating test strings, which they should discriminate between. However, simpler cues may underlie discrimination if they are available. Here, we review stimulus design in a sample of studies that use particular sounds as tokens, and that claim or suggest their results demonstrate a form of sequence rule learning. To assess the extent of acoustic similarity between training and test strings, we use four simple measures corresponding to cues that are likely salient. All stimulus sets contain biases in similarity measures such that grammatical test stimuli resemble training stimuli acoustically more than do non-grammatical test stimuli. These biases may contribute to response behaviour, reducing the strength of grammatical explanations. We conclude that acoustic confounds are a blind spot in artificial grammar learning studies in nonhuman animals. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Rule-based decision making model

International Nuclear Information System (INIS)

Sirola, Miki

1998-01-01

A rule-based decision making model is designed in G2 environment. A theoretical and methodological frame for the model is composed and motivated. The rule-based decision making model is based on object-oriented modelling, knowledge engineering and decision theory. The idea of safety objective tree is utilized. Advanced rule-based methodologies are applied. A general decision making model 'decision element' is constructed. The strategy planning of the decision element is based on e.g. value theory and utility theory. A hypothetical process model is built to give input data for the decision element. The basic principle of the object model in decision making is division in tasks. Probability models are used in characterizing component availabilities. Bayes' theorem is used to recalculate the probability figures when new information is got. The model includes simple learning features to save the solution path. A decision analytic interpretation is given to the decision making process. (author)

Optimization of β-decision rules relative to number of misclassifications

KAUST Repository

Zielosko, Beata

2012-01-01

In the paper, we present an algorithm for optimization of approximate decision rules relative to the number of misclassifications. The considered algorithm is based on extensions of dynamic programming and constructs a directed acyclic graph Δ β (T). Based on this graph we can describe the whole set of so-called irredundant β-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 Springer-Verlag.

The effect of negative performance stereotypes on learning.

Science.gov (United States)

Rydell, Robert J; Rydell, Michael T; Boucher, Kathryn L

2010-12-01

Stereotype threat (ST) research has focused exclusively on how negative group stereotypes reduce performance. The present work examines if pejorative stereotypes about women in math inhibit their ability to learn the mathematical rules and operations necessary to solve math problems. In Experiment 1, women experiencing ST had difficulty encoding math-related information into memory and, therefore, learned fewer mathematical rules and showed poorer math performance than did controls. In Experiment 2, women experiencing ST while learning modular arithmetic (MA) performed more poorly than did controls on easy MA problems; this effect was due to reduced learning of the mathematical operations underlying MA. In Experiment 3, ST reduced women's, but not men's, ability to learn abstract mathematical rules and to transfer these rules to a second, isomorphic task. This work provides the first evidence that negative stereotypes about women in math reduce their level of mathematical learning and demonstrates that reduced learning due to stereotype threat can lead to poorer performance in negatively stereotyped domains. PsycINFO Database Record (c) 2010 APA, all rights reserved.

Exploring the Virtuality Continuum for Complex Rule-Set Education in the Context of Soccer Rule Comprehension

Directory of Open Access Journals (Sweden)

Andrés N. Vargas González

2017-11-01

Full Text Available We present an exploratory study to assess the benefits of using Augmented Reality (AR in training sports rule comprehension. Soccer is the chosen context for this study due to the wide range of complexity in the rules and regulations. Observers must understand and holistically evaluate the proximity of players in the game to the ball and other visual objects, such as the goal, penalty area, and other players. Grounded in previous literature investigating the effects of Virtual Reality (VR scenarios on transfer of training (ToT, we explore how three different interfaces influence user perception using both qualitative and quantitative measures. To better understand how effective augmented reality technology is when combined with learning systems, we compare results on the effects of learning outcomes in three interface conditions: AR, VR and a traditional Desktop interface. We also compare these interfaces as measured by user experience, engagement, and immersion. Results show that there were no significance difference among VR and AR; however, these participants outperformed the Desktop group which needed a higher number of adaptations to acquire the same knowledge.

Listen, learn, like! Dorsolateral prefrontal cortex involved in the mere exposure effect in music

DEFF Research Database (Denmark)

Green, Anders Christian; Bærentsen, Klaus B.; Stødkilde-Jørgensen, Hans

2012-01-01

, participants rated liking for each melody and, later, their recognition of them. Participants showed learning effects, better recognising melodies heard more often. Melodies heard most often were most liked, consistent with the mere exposure effect. We found neural activations as a function of previous...... exposure in bilateral dorsolateral prefrontal and inferior parietal cortex, probably reflecting retrieval and working memory-related processes. This was despite the fact that the task during scanning was to judge liking, not recognition, thus suggesting that appreciation of music relies strongly on memory...

Group learning versus local learning: Which is prefer for public cooperation?

Science.gov (United States)

Yang, Shi-Han; Song, Qi-Qing

2018-01-01

We study the evolution of cooperation in public goods games on various graphs, focusing on the effects that are brought by different kinds of strategy donors. This highlights a basic feature of a public good game, for which there exists a remarkable difference between the interactive players and the players who are imitated. A player can learn from all the groups where the player is a member or from the typically local nearest neighbors, and the results show that the group learning rules have better performance in promoting cooperation on many networks than the local learning rules. The heterogeneity of networks' degree may be an effective mechanism for harvesting the cooperation expectation in many cases, however, we find that heterogeneity does not definitely mean the high frequency of cooperators in a population under group learning rules. It was shown that cooperators always hardly evolve whenever the interaction and the replacement do not coincide for evolutionary pairwise dilemmas on graphs, while for PG games we find that breaking the symmetry is conducive to the survival of cooperators.

Optimal Sequential Rules for Computer-Based Instruction.

Science.gov (United States)

Vos, Hans J.

1998-01-01

Formulates sequential rules for adapting the appropriate amount of instruction to learning needs in the context of computer-based instruction. Topics include Bayesian decision theory, threshold and linear-utility structure, psychometric model, optimal sequential number of test questions, and an empirical example of sequential instructional…

Decision rule classifiers for multi-label decision tables

KAUST Repository

Alsolami, Fawaz; Azad, Mohammad; Chikalov, Igor; Moshkov, Mikhail

2014-01-01

for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion. © 2014 Springer International

Diversity of Rule-based Approaches: Classic Systems and Recent Applications

Directory of Open Access Journals (Sweden)

Grzegorz J. Nalepa

2016-11-01

Full Text Available Rules are a common symbolic model of knowledge. Rule-based systems share roots in cognitive science and artificial intelligence. In the former, they are mostly used in cognitive architectures; in the latter, they are developed in several domains including knowledge engineering and machine learning. This paper aims to give an overview of these issues with the focus on the current research perspective of artificial intelligence. Moreover, in this setting we discuss our results in the design of rule-based systems and their applications in context-aware and business intelligence systems.

A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.

Science.gov (United States)

Nguyen, Su; Mei, Yi; Xue, Bing; Zhang, Mengjie

2018-06-04

Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This paper develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

Learning from Errors

Science.gov (United States)

Metcalfe, Janet

2017-01-01

Although error avoidance during learning appears to be the rule in American classrooms, laboratory studies suggest that it may be a counterproductive strategy, at least for neurologically typical students. Experimental investigations indicate that errorful learning followed by corrective feedback is beneficial to learning. Interestingly, the…

Retrieving Knowledge in Social Situations: A Test of the Implicit Rules Model.

Science.gov (United States)

Meyer, Janet R.

1996-01-01

Supports the Implicit Rules Model, which suggests that individuals acquire implicit rules that connect request situation schemas to behaviors. Shows how individuals, in two experiments, learned, based on feedback, which behaviors were "correct" for multiple instances, and then, on their own, chose the correct behavior for new instances.…

Looking for exceptions on knowledge rules induced from HIV cleavage data set

Directory of Open Access Journals (Sweden)

Ronaldo Cristiano Prati

2004-01-01

Full Text Available The aim of data mining is to find useful knowledge inout of databases. In order to extract such knowledge, several methods can be used, among them machine learning (ML algorithms. In this work we focus on ML algorithms that express the extracted knowledge in a symbolic form, such as rules. This representation may allow us to ''explain'' the data. Rule learning algorithms are mainly designed to induce classification rules that can predict new cases with high accuracy. However, these sorts of rules generally express common sense knowledge, resulting in many interesting and useful rules not being discovered. Furthermore, the domain independent biases, especially those related to the language used to express the induced knowledge, could induce rules that are difficult to understand. Exceptions might be used in order to overcome these drawbacks. Exceptions are defined as rules that contradict common believebeliefs. This kind of rules can play an important role in the process of understanding the underlying data as well as in making critical decisions. By contradicting the user's common beliefves, exceptions are bound to be interesting. This work proposes a method to find exceptions. In order to illustrate the potential of our approach, we apply the method in a real world data set to discover rules and exceptions in the HIV virus protein cleavage process. A good understanding of the process that generates this data plays an important role oin the research of cleavage inhibitors. We consider believe that the proposed approach may help the domain expert to further understand this process.

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

Science.gov (United States)

Gardner, Brian; Grüning, André

2016-01-01

Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

Can power spectrum observations rule out slow-roll inflation?

OpenAIRE

Vieira, J. P. P.; Byrnes, Christian T.; Lewis, Antony

2017-01-01

The spectral index of scalar perturbations is an important observable that allows us to learn about inflationary physics. In particular, a detection of a significant deviation from a constant spectral index could enable us to rule out the simplest class of inflation models. We investigate whether future observations could rule out canonical single-field slow-roll inflation given the parameters allowed by current observational constraints. We find that future measurements of a constant running...

Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array

Directory of Open Access Journals (Sweden)

Sukru Burc Eryilmaz

2014-07-01

Full Text Available Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nanoscale memory devices as synaptic elements. Although there has been experimental work that demonstrated the operation of nanoscale synaptic element at the single device level, network level studies have been limited to simulations. In this work, we demonstrate, using experiments, array level associative learning using phase change synaptic devices connected in a grid like configuration similar to the organization of the biological brain. Implementing Hebbian learning with phase change memory cells, the synaptic grid was able to store presented patterns and recall missing patterns in an associative brain-like fashion. We found that the system is robust to device variations, and large variations in cell resistance states can be accommodated by increasing the number of training epochs. We illustrated the tradeoff between variation tolerance of the network and the overall energy consumption, and found that energy consumption is decreased significantly for lower variation tolerance.

Identifying Human Phenotype Terms by Combining Machine Learning and Validation Rules

Directory of Open Access Journals (Sweden)

Manuel Lobo

2017-01-01

Full Text Available Named-Entity Recognition is commonly used to identify biological entities such as proteins, genes, and chemical compounds found in scientific articles. The Human Phenotype Ontology (HPO is an ontology that provides a standardized vocabulary for phenotypic abnormalities found in human diseases. This article presents the Identifying Human Phenotypes (IHP system, tuned to recognize HPO entities in unstructured text. IHP uses Stanford CoreNLP for text processing and applies Conditional Random Fields trained with a rich feature set, which includes linguistic, orthographic, morphologic, lexical, and context features created for the machine learning-based classifier. However, the main novelty of IHP is its validation step based on a set of carefully crafted manual rules, such as the negative connotation analysis, that combined with a dictionary can filter incorrectly identified entities, find missed entities, and combine adjacent entities. The performance of IHP was evaluated using the recently published HPO Gold Standardized Corpora (GSC, where the system Bio-LarK CR obtained the best F-measure of 0.56. IHP achieved an F-measure of 0.65 on the GSC. Due to inconsistencies found in the GSC, an extended version of the GSC was created, adding 881 entities and modifying 4 entities. IHP achieved an F-measure of 0.863 on the new GSC.

A Biologically Inspired Computational Model of Basal Ganglia in Action Selection.

Science.gov (United States)

Baston, Chiara; Ursino, Mauro

2015-01-01

The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go), indirect (NoGo), and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves) during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges), synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication). Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments.

A Biologically Inspired Computational Model of Basal Ganglia in Action Selection

Directory of Open Access Journals (Sweden)

Chiara Baston

2015-01-01

Full Text Available The basal ganglia (BG are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go, indirect (NoGo, and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges, synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication. Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments.

A Numerical Comparison of Rule Ensemble Methods and Support Vector Machines

Energy Technology Data Exchange (ETDEWEB)

Meza, Juan C.; Woods, Mark

2009-12-18

Machine or statistical learning is a growing field that encompasses many scientific problems including estimating parameters from data, identifying risk factors in health studies, image recognition, and finding clusters within datasets, to name just a few examples. Statistical learning can be described as 'learning from data' , with the goal of making a prediction of some outcome of interest. This prediction is usually made on the basis of a computer model that is built using data where the outcomes and a set of features have been previously matched. The computer model is called a learner, hence the name machine learning. In this paper, we present two such algorithms, a support vector machine method and a rule ensemble method. We compared their predictive power on three supernova type 1a data sets provided by the Nearby Supernova Factory and found that while both methods give accuracies of approximately 95%, the rule ensemble method gives much lower false negative rates.

Relationships between length and coverage of decision rules

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2014-01-01

The paper describes a new tool for study relationships between length and coverage of exact decision rules. This tool is based on dynamic programming approach. We also present results of experiments with decision tables from UCI Machine Learning Repository.

Relationships between length and coverage of decision rules

KAUST Repository

Amin, Talha

2014-02-14

The paper describes a new tool for study relationships between length and coverage of exact decision rules. This tool is based on dynamic programming approach. We also present results of experiments with decision tables from UCI Machine Learning Repository.

New Safety rule for Chemical Agents

CERN Multimedia

Safety Commission

2010-01-01

The following Safety rule has been issued on 08-01-2010: Safety Regulation SR-C Chemical Agents This document applies to all persons under the Director General’s authority. It sets out the minimal requirements for the protection of persons from risks to their safety and health arising, or likely to arise, from the effects of hazardous chemical agents used in any CERN activity. All Safety rules are available on the web pages.

playing games with rules in early child care and beyond

DEFF Research Database (Denmark)

Winther-Lindqvist, Ditte Alexandra

2017-01-01

’s) play is undecided and debated in the literature, and often reflects whether gaming with rules is seen as a version of a universal play-phenomenon or considered a play-form of its own. Very often the discussion also revolves around whether all playing is considered to be involving rules or whether rules...... are only regarded relevant to some forms of play - scholars arguing for the former; they think of rules in broader terms, more like a sort of social norms, whereas those who argue that rules are only prominent in games think of formal and explicit rules. Rather than discussing whether or not rules...

Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

Science.gov (United States)

Macesic, Nenad; Polubriaginof, Fernanda; Tatonetti, Nicholas P

2017-12-01

Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible. To date, it has been used for antimicrobial susceptibility genotype/phenotype prediction, development of AMR clinical decision rules, novel antimicrobial agent discovery and antimicrobial therapy optimization. Application of machine learning to studying AMR is feasible but remains limited. Implementation of machine learning in clinical settings faces barriers to uptake with concerns regarding model interpretability and data quality.Future applications of machine learning to AMR are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction.

Fear of Negative Evaluation Biases Social Evaluation Inference: Evidence from a Probabilistic Learning Task

Science.gov (United States)

Button, Katherine S.; Kounali, Daphne; Stapinski, Lexine; Rapee, Ronald M.; Lewis, Glyn; Munafò, Marcus R.

2015-01-01

Background Fear of negative evaluation (FNE) defines social anxiety yet the process of inferring social evaluation, and its potential role in maintaining social anxiety, is poorly understood. We developed an instrumental learning task to model social evaluation learning, predicting that FNE would specifically bias learning about the self but not others. Methods During six test blocks (3 self-referential, 3 other-referential), participants (n = 100) met six personas and selected a word from a positive/negative pair to finish their social evaluation sentences “I think [you are / George is]…”. Feedback contingencies corresponded to 3 rules, liked, neutral and disliked, with P[positive word correct] = 0.8, 0.5 and 0.2, respectively. Results As FNE increased participants selected fewer positive words (β = −0.4, 95% CI −0.7, −0.2, p = 0.001), which was strongest in the self-referential condition (FNE × condition 0.28, 95% CI 0.01, 0.54, p = 0.04), and the neutral and dislike rules (FNE × condition × rule, p = 0.07). At low FNE the proportion of positive words selected for self-neutral and self-disliked greatly exceeded the feedback contingency, indicating poor learning, which improved as FNE increased. Conclusions FNE is associated with differences in processing social-evaluative information specifically about the self. At low FNE this manifests as insensitivity to learning negative self-referential evaluation. High FNE individuals are equally sensitive to learning positive or negative evaluation, which although objectively more accurate, may have detrimental effects on mental health. PMID:25853835

Learned helplessness: validity and reliability of depressive-like states in mice.

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Chourbaji, S; Zacher, C; Sanchis-Segura, C; Dormann, C; Vollmayr, B; Gass, P

2005-12-01

The learned helplessness paradigm is a depression model in which animals are exposed to unpredictable and uncontrollable stress, e.g. electroshocks, and subsequently develop coping deficits for aversive but escapable situations (J.B. Overmier, M.E. Seligman, Effects of inescapable shock upon subsequent escape and avoidance responding, J. Comp. Physiol. Psychol. 63 (1967) 28-33 ). It represents a model with good similarity to the symptoms of depression, construct, and predictive validity in rats. Despite an increased need to investigate emotional, in particular depression-like behaviors in transgenic mice, so far only a few studies have been published using the learned helplessness paradigm. One reason may be the fact that-in contrast to rats (B. Vollmayr, F.A. Henn, Learned helplessness in the rat: improvements in validity and reliability, Brain Res. Brain Res. Protoc. 8 (2001) 1-7)--there is no generally accepted learned helplessness protocol available for mice. This prompted us to develop a reliable helplessness procedure in C57BL/6N mice, to exclude possible artifacts, and to establish a protocol, which yields a consistent fraction of helpless mice following the shock exposure. Furthermore, we validated this protocol pharmacologically using the tricyclic antidepressant imipramine. Here, we present a mouse model with good face and predictive validity that can be used for transgenic, behavioral, and pharmacological studies.

Evolution of learned strategy choice in a frequency-dependent game.

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Katsnelson, Edith; Motro, Uzi; Feldman, Marcus W; Lotem, Arnon

2012-03-22

In frequency-dependent games, strategy choice may be innate or learned. While experimental evidence in the producer-scrounger game suggests that learned strategy choice may be common, a recent theoretical analysis demonstrated that learning by only some individuals prevents learning from evolving in others. Here, however, we model learning explicitly, and demonstrate that learning can easily evolve in the whole population. We used an agent-based evolutionary simulation of the producer-scrounger game to test the success of two general learning rules for strategy choice. We found that learning was eventually acquired by all individuals under a sufficient degree of environmental fluctuation, and when players were phenotypically asymmetric. In the absence of sufficient environmental change or phenotypic asymmetries, the correct target for learning seems to be confounded by game dynamics, and innate strategy choice is likely to be fixed in the population. The results demonstrate that under biologically plausible conditions, learning can easily evolve in the whole population and that phenotypic asymmetry is important for the evolution of learned strategy choice, especially in a stable or mildly changing environment.

Automatic Learning of Fine Operating Rules for Online Power System Security Control.

Science.gov (United States)

Sun, Hongbin; Zhao, Feng; Wang, Hao; Wang, Kang; Jiang, Weiyong; Guo, Qinglai; Zhang, Boming; Wehenkel, Louis

2016-08-01

Fine operating rules for security control and an automatic system for their online discovery were developed to adapt to the development of smart grids. The automatic system uses the real-time system state to determine critical flowgates, and then a continuation power flow-based security analysis is used to compute the initial transfer capability of critical flowgates. Next, the system applies the Monte Carlo simulations to expected short-term operating condition changes, feature selection, and a linear least squares fitting of the fine operating rules. The proposed system was validated both on an academic test system and on a provincial power system in China. The results indicated that the derived rules provide accuracy and good interpretability and are suitable for real-time power system security control. The use of high-performance computing systems enables these fine operating rules to be refreshed online every 15 min.

Dynamic programming approach for partial decision rule optimization

KAUST Repository

Amin, Talha

2012-10-04

This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between number of rows in a decision table T and number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δ γ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δ γ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.

Dynamic programming approach for partial decision rule optimization

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2012-01-01

This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between number of rows in a decision table T and number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δ γ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δ γ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.

Complexity, Training Paradigm Design, and the Contribution of Memory Subsystems to Grammar Learning

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Ettlinger, Marc; Wong, Patrick C. M.

2016-01-01

Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative, procedural, and working memory subsystems. Experiment 1 demonstrated that passive, exposure-based training boosted learning of both simple and complex grammatical rules, relative to no training. Additionally, procedural memory correlated with simple rule learning, whereas declarative memory correlated with complex rule learning. Experiment 2 showed that presenting corrective feedback during the test phase did not improve learning. Experiment 3 revealed that structuring the order of training so that subjects are first exposed to the simple rule and then the complex improved learning. The cumulative findings shed light on the contributions of grammatical complexity, training paradigm design, and domain-general memory subsystems in determining grammar learning success. PMID:27391085

Complexity, Training Paradigm Design, and the Contribution of Memory Subsystems to Grammar Learning.

Science.gov (United States)

Antoniou, Mark; Ettlinger, Marc; Wong, Patrick C M

2016-01-01

Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative, procedural, and working memory subsystems. Experiment 1 demonstrated that passive, exposure-based training boosted learning of both simple and complex grammatical rules, relative to no training. Additionally, procedural memory correlated with simple rule learning, whereas declarative memory correlated with complex rule learning. Experiment 2 showed that presenting corrective feedback during the test phase did not improve learning. Experiment 3 revealed that structuring the order of training so that subjects are first exposed to the simple rule and then the complex improved learning. The cumulative findings shed light on the contributions of grammatical complexity, training paradigm design, and domain-general memory subsystems in determining grammar learning success.

Complexity, Training Paradigm Design, and the Contribution of Memory Subsystems to Grammar Learning.

Directory of Open Access Journals (Sweden)

Mark Antoniou

Full Text Available Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative, procedural, and working memory subsystems. Experiment 1 demonstrated that passive, exposure-based training boosted learning of both simple and complex grammatical rules, relative to no training. Additionally, procedural memory correlated with simple rule learning, whereas declarative memory correlated with complex rule learning. Experiment 2 showed that presenting corrective feedback during the test phase did not improve learning. Experiment 3 revealed that structuring the order of training so that subjects are first exposed to the simple rule and then the complex improved learning. The cumulative findings shed light on the contributions of grammatical complexity, training paradigm design, and domain-general memory subsystems in determining grammar learning success.

The Role of Age and Executive Function in Auditory Category Learning

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Reetzke, Rachel; Maddox, W. Todd; Chandrasekaran, Bharath

2015-01-01

Auditory categorization is a natural and adaptive process that allows for the organization of high-dimensional, continuous acoustic information into discrete representations. Studies in the visual domain have identified a rule-based learning system that learns and reasons via a hypothesis-testing process that requires working memory and executive attention. The rule-based learning system in vision shows a protracted development, reflecting the influence of maturing prefrontal function on visual categorization. The aim of the current study is two-fold: (a) to examine the developmental trajectory of rule-based auditory category learning from childhood through adolescence, into early adulthood; and (b) to examine the extent to which individual differences in rule-based category learning relate to individual differences in executive function. Sixty participants with normal hearing, 20 children (age range, 7–12), 21 adolescents (age range, 13–19), and 19 young adults (age range, 20–23), learned to categorize novel dynamic ripple sounds using trial-by-trial feedback. The spectrotemporally modulated ripple sounds are considered the auditory equivalent of the well-studied Gabor patches in the visual domain. Results revealed that auditory categorization accuracy improved with age, with young adults outperforming children and adolescents. Computational modeling analyses indicated that the use of the task-optimal strategy (i.e. a conjunctive rule-based learning strategy) improved with age. Notably, individual differences in executive flexibility significantly predicted auditory category learning success. The current findings demonstrate a protracted development of rule-based auditory categorization. The results further suggest that executive flexibility coupled with perceptual processes play important roles in successful rule-based auditory category learning. PMID:26491987

Evolution of cooperation driven by incremental learning

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Li, Pei; Duan, Haibin

2015-02-01

It has been shown that the details of microscopic rules in structured populations can have a crucial impact on the ultimate outcome in evolutionary games. So alternative formulations of strategies and their revision processes exploring how strategies are actually adopted and spread within the interaction network need to be studied. In the present work, we formulate the strategy update rule as an incremental learning process, wherein knowledge is refreshed according to one's own experience learned from the past (self-learning) and that gained from social interaction (social-learning). More precisely, we propose a continuous version of strategy update rules, by introducing the willingness to cooperate W, to better capture the flexibility of decision making behavior. Importantly, the newly gained knowledge including self-learning and social learning is weighted by the parameter ω, establishing a strategy update rule involving innovative element. Moreover, we quantify the macroscopic features of the emerging patterns to inspect the underlying mechanisms of the evolutionary process using six cluster characteristics. In order to further support our results, we examine the time evolution course for these characteristics. Our results might provide insights for understanding cooperative behaviors and have several important implications for understanding how individuals adjust their strategies under real-life conditions.

"Learning to Like Learning": An Appreciative Inquiry into Emotions in Education

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Naude, L.; van den Bergh, T. J.; Kruger, I. S.

2014-01-01

Various learning philosophies, such as humanistic, constructivist, and socio-cultural approaches, have accentuated the importance of emotion in learning. In this article, we reviewed these approaches and explored the affective dimensions of learning. We conducted focus group and individual interviews with a group of female students in the…

Decision Rules, Trees and Tests for Tables with Many-valued Decisions–comparative Study

KAUST Repository

Azad, Mohammad; Zielosko, Beata; Moshkov, Mikhail; Chikalov, Igor

2013-01-01

In this paper, we present three approaches for construction of decision rules for decision tables with many-valued decisions. We construct decision rules directly for rows of decision table, based on paths in decision tree, and based on attributes contained in a test (super-reduct). Experimental results for the data sets taken from UCI Machine Learning Repository, contain comparison of the maximum and the average length of rules for the mentioned approaches.

Decision Rules, Trees and Tests for Tables with Many-valued Decisions–comparative Study

KAUST Repository

Azad, Mohammad

2013-10-04

In this paper, we present three approaches for construction of decision rules for decision tables with many-valued decisions. We construct decision rules directly for rows of decision table, based on paths in decision tree, and based on attributes contained in a test (super-reduct). Experimental results for the data sets taken from UCI Machine Learning Repository, contain comparison of the maximum and the average length of rules for the mentioned approaches.

Learning the inverse kinetics of an octopus-like manipulator in three-dimensional space.

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Giorelli, M; Renda, F; Calisti, M; Arienti, A; Ferri, G; Laschi, C

2015-05-13

This work addresses the inverse kinematics problem of a bioinspired octopus-like manipulator moving in three-dimensional space. The bioinspired manipulator has a conical soft structure that confers the ability of twirling around objects as a real octopus arm does. Despite the simple design, the soft conical shape manipulator driven by cables is described by nonlinear differential equations, which are difficult to solve analytically. Since exact solutions of the equations are not available, the Jacobian matrix cannot be calculated analytically and the classical iterative methods cannot be used. To overcome the intrinsic problems of methods based on the Jacobian matrix, this paper proposes a neural network learning the inverse kinematics of a soft octopus-like manipulator driven by cables. After the learning phase, a feed-forward neural network is able to represent the relation between manipulator tip positions and forces applied to the cables. Experimental results show that a desired tip position can be achieved in a short time, since heavy computations are avoided, with a degree of accuracy of 8% relative average error with respect to the total arm length.

Knowledge discovery with classification rules in a cardiovascular dataset.

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Podgorelec, Vili; Kokol, Peter; Stiglic, Milojka Molan; Hericko, Marjan; Rozman, Ivan

2005-12-01

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

Assessing predation risk: optimal behaviour and rules of thumb.

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Welton, Nicky J; McNamara, John M; Houston, Alasdair I

2003-12-01

We look at a simple model in which an animal makes behavioural decisions over time in an environment in which all parameters are known to the animal except predation risk. In the model there is a trade-off between gaining information about predation risk and anti-predator behaviour. All predator attacks lead to death for the prey, so that the prey learns about predation risk by virtue of the fact that it is still alive. We show that it is not usually optimal to behave as if the current unbiased estimate of the predation risk is its true value. We consider two different ways to model reproduction; in the first scenario the animal reproduces throughout its life until it dies, and in the second scenario expected reproductive success depends on the level of energy reserves the animal has gained by some point in time. For both of these scenarios we find results on the form of the optimal strategy and give numerical examples which compare optimal behaviour with behaviour under simple rules of thumb. The numerical examples suggest that the value of the optimal strategy over the rules of thumb is greatest when there is little current information about predation risk, learning is not too costly in terms of predation, and it is energetically advantageous to learn about predation. We find that for the model and parameters investigated, a very simple rule of thumb such as 'use the best constant control' performs well.

Learning and memory enhancing activity of Ficus carica (Fig: An experimental study in rats

Directory of Open Access Journals (Sweden)

Meera Sumanth

2014-01-01

Full Text Available Objective: The study aimed to assess the learning and memory enhancing activity of the ethanolic fruit extract of Ficus carica in rats using elevated plus maze (EPM, Hebb-William maze (HWM and Morris water maze (MWM. Materials and Methods: Wistar rats (100-150 g of either sex, were divided into 5 groups (n = 6. Group I (control animals received vehicle, Group II (scopolamine control animals received scopolamine (0.4 mg/kg i.p, Groups III and IV animals received ethanolic fruit extract of F. carica (200 mg/kg and 400 mg/kg p.o and Group V animals received piracetam (400 mg/kg i.p for 27 days. The rats of Groups III-V were injected with a single dose of scopolamine (0.4 mg/kg i.p on 19 th and 27 th day. Assessment of transfer latency (TL, time taken to reach reward chamber (TRC and swim latency (SL was done on 19 th and 27 th day using EPM, HWM and MWM, respectively. The data was analyzed by one-way Analysis of Variance followed by Dunnett′s test. P ≤ 0.05 was considered to be significant. Results: Ethanolic fruit extract of F. carica decreased TL, TRC and SL in comparison to scopolamine treated rats. Conclusion: The fruit of F. carica enhanced learning and memory activity.

Syntactic learning by mere exposure--an ERP study in adult learners.

Science.gov (United States)

Mueller, Jutta L; Oberecker, Regine; Friederici, Angela D

2009-07-29

Artificial language studies have revealed the remarkable ability of humans to extract syntactic structures from a continuous sound stream by mere exposure. However, it remains unclear whether the processes acquired in such tasks are comparable to those applied during normal language processing. The present study compares the ERPs to auditory processing of simple Italian sentences in native and non-native speakers after brief exposure to Italian sentences of a similar structure. The sentences contained a non-adjacent dependency between an auxiliary and the morphologically marked suffix of the verb. Participants were presented four alternating learning and testing phases. During learning phases only correct sentences were presented while during testing phases 50 percent of the sentences contained a grammatical violation. The non-native speakers successfully learned the dependency and displayed an N400-like negativity and a subsequent anteriorily distributed positivity in response to rule violations. The native Italian group showed an N400 followed by a P600 effect. The presence of the P600 suggests that native speakers applied a grammatical rule. In contrast, non-native speakers appeared to use a lexical form-based processing strategy. Thus, the processing mechanisms acquired in the language learning task were only partly comparable to those applied by competent native speakers.

Syntactic learning by mere exposure - An ERP study in adult learners

Science.gov (United States)

Mueller, Jutta L; Oberecker, Regine; Friederici, Angela D

2009-01-01

Background Artificial language studies have revealed the remarkable ability of humans to extract syntactic structures from a continuous sound stream by mere exposure. However, it remains unclear whether the processes acquired in such tasks are comparable to those applied during normal language processing. The present study compares the ERPs to auditory processing of simple Italian sentences in native and non-native speakers after brief exposure to Italian sentences of a similar structure. The sentences contained a non-adjacent dependency between an auxiliary and the morphologically marked suffix of the verb. Participants were presented four alternating learning and testing phases. During learning phases only correct sentences were presented while during testing phases 50 percent of the sentences contained a grammatical violation. Results The non-native speakers successfully learned the dependency and displayed an N400-like negativity and a subsequent anteriorily distributed positivity in response to rule violations. The native Italian group showed an N400 followed by a P600 effect. Conclusion The presence of the P600 suggests that native speakers applied a grammatical rule. In contrast, non-native speakers appeared to use a lexical form-based processing strategy. Thus, the processing mechanisms acquired in the language learning task were only partly comparable to those applied by competent native speakers. PMID:19640301

Diurnal rhythms in psychological reward functioning in healthy young men: 'Wanting', liking, and learning.

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Byrne, Jamie E M; Murray, Greg

2017-01-01

A range of evidence suggests that human reward functioning is partly driven by the endogenous circadian system, generating 24-hour rhythms in behavioural measures of reward activation. Reward functioning is multifaceted but literature to date is largely limited to measures of self-reported positive mood states. The aim of this study was to advance the field by testing for hypothesised diurnal variation in previously unexplored components of psychological reward: 'wanting', liking, and learning using subjective and behavioural measures. Risky decision making (automatic Balloon Analogue Risk Task), affective responsivity to positive images (International Affective Pictures System), uncued self-reported discrete emotions, and learning-contingent reward (Iowa Gambling Task) were measured at 10.00 hours, 14.00 hours, and 19.00 hours in a counterbalanced repeated measures design with 50 healthy male participants (aged 18-30). As hypothesised, risky decision making (unconscious 'wanting') and ratings of arousal towards positive images (conscious wanting) exhibited a diurnal waveform with indices highest at 14.00 hours. No diurnal rhythm was observed for liking (pleasure ratings to positive images, discrete uncued positive emotions) or in a learning-contingent reward task. Findings reaffirm that diurnal variation in human reward functioning is most pronounced in the motivational 'wanting' components of reward.

Design and Analysis of Decision Rules via Dynamic Programming

KAUST Repository

Amin, Talha M.

2017-04-24

The areas of machine learning, data mining, and knowledge representation have many different formats used to represent information. Decision rules, amongst these formats, are the most expressive and easily-understood by humans. In this thesis, we use dynamic programming to design decision rules and analyze them. The use of dynamic programming allows us to work with decision rules in ways that were previously only possible for brute force methods. Our algorithms allow us to describe the set of all rules for a given decision table. Further, we can perform multi-stage optimization by repeatedly reducing this set to only contain rules that are optimal with respect to selected criteria. One way that we apply this study is to generate small systems with short rules by simulating a greedy algorithm for the set cover problem. We also compare maximum path lengths (depth) of deterministic and non-deterministic decision trees (a non-deterministic decision tree is effectively a complete system of decision rules) with regards to Boolean functions. Another area of advancement is the presentation of algorithms for constructing Pareto optimal points for rules and rule systems. This allows us to study the existence of “totally optimal” decision rules (rules that are simultaneously optimal with regards to multiple criteria). We also utilize Pareto optimal points to compare and rate greedy heuristics with regards to two criteria at once. Another application of Pareto optimal points is the study of trade-offs between cost and uncertainty which allows us to find reasonable systems of decision rules that strike a balance between length and accuracy.

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

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Burbank, Kendra S

2015-12-01

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.

Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

Directory of Open Access Journals (Sweden)

Md. Abdullah-al-mamun

2015-08-01

Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

Role of aging and hippocampus in Time-Place Learning: link to episodic-like memory?

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Cornelis Kees Mulder

2016-01-01

Full Text Available Introduction: with time-place learning (TPL, animals link an event with the spatial location and the time of day. The what-where-when TPL components make the task putatively episodic-like in nature. Animals use an internal sense of time to master TPL, which is circadian system based. Finding indications for a role of the hippocampus and (early aging-sensitivity in TPL would strengthen the episodic-like memory nature of the paradigm. Methods: previously, we used C57Bl/6 mice for our TPL research. Here, we used CD1 mice which are less hippocampal-driven and age faster compared to C57Bl/6 mice. To demonstrate the low degree of hippocampal-driven performance in CD1 mice, a cross maze was used. The spontaneous alternation test was used to score spatial working memory in CD1 mice at four different age categories (young (3-6 months, middle-aged (7-11 months, aged (12-18 months and old (>19 months. TPL performance of middle-aged and aged CD1 mice was tested in a setup with either two or three time points per day (2-arm or 3-arm TPL task. Immunostainings was applied on brains of young and middle-aged C57Bl/6 mice that had successfully mastered the 3-arm TPL task. Results: in contrast to C57Bl/6 mice, middle-aged and aged CD1 mice were less hippocampus-driven and failed to master the 3-arm TPL task. They could, however, master the 2-arm TPL task primarily via an ordinal (non-circadian timing system. c-Fos, CRY2, vasopressin (AVP, and pCREB were investigated. We found no differences at the level of the suprachiasmatic nucleus (SCN; circadian master clock, whereas CRY2 expression was increased in the hippocampal dentate gyrus. The most pronounced difference between TPL trained and control mice was found in c-Fos expression in the paraventricular thalamic nucleus, a circadian system relay station. Conclusions: These results further indicate a key role of CRY proteins in TPL and confirm the limited role of the SCN in TPL. Based on the poor TPL performance of

Implication of Dopaminergic Modulation in Operant Reward Learning and the Induction of Compulsive-Like Feeding Behavior in "Aplysia"

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Bedecarrats, Alexis; Cornet, Charles; Simmers, John; Nargeot, Romuald

2013-01-01

Feeding in "Aplysia" provides an amenable model system for analyzing the neuronal substrates of motivated behavior and its adaptability by associative reward learning and neuromodulation. Among such learning processes, appetitive operant conditioning that leads to a compulsive-like expression of feeding actions is known to be associated…

Learning CAD at University through Summaries of the Rules of Design Intent

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Barbero, Basilio Ramos; Pedrosa, Carlos Melgosa; Samperio, Raúl Zamora

2017-01-01

The ease with which 3D CAD models may be modified and reused are two key aspects that improve the design-intent variable and that can significantly shorten the development timelines of a product. A set of rules are gathered from various authors that take different 3D modelling strategies into account. These rules are then applied to CAD…

A Collaborative Educational Association Rule Mining Tool

Science.gov (United States)

Garcia, Enrique; Romero, Cristobal; Ventura, Sebastian; de Castro, Carlos

2011-01-01

This paper describes a collaborative educational data mining tool based on association rule mining for the ongoing improvement of e-learning courses and allowing teachers with similar course profiles to share and score the discovered information. The mining tool is oriented to be used by non-expert instructors in data mining so its internal…

Optimization of Approximate Inhibitory Rules Relative to Number of Misclassifications

KAUST Repository

Alsolami, Fawaz

2013-10-04

In this work, we consider so-called nonredundant inhibitory rules, containing an expression “attribute:F value” on the right- hand side, for which the number of misclassifications is at most a threshold γ. We study a dynamic programming approach for description of the considered set of rules. This approach allows also the optimization of nonredundant inhibitory rules relative to the length and coverage. The aim of this paper is to investigate an additional possibility of optimization relative to the number of misclassifications. The results of experiments with decision tables from the UCI Machine Learning Repository show this additional optimization achieves a fewer misclassifications. Thus, the proposed optimization procedure is promising.

Anisotropic interaction rules in circular motions of pigeon flocks: An empirical study based on sparse Bayesian learning

Science.gov (United States)

Chen, Duxin; Xu, Bowen; Zhu, Tao; Zhou, Tao; Zhang, Hai-Tao

2017-08-01

Coordination shall be deemed to the result of interindividual interaction among natural gregarious animal groups. However, revealing the underlying interaction rules and decision-making strategies governing highly coordinated motion in bird flocks is still a long-standing challenge. Based on analysis of high spatial-temporal resolution GPS data of three pigeon flocks, we extract the hidden interaction principle by using a newly emerging machine learning method, namely the sparse Bayesian learning. It is observed that the interaction probability has an inflection point at pairwise distance of 3-4 m closer than the average maximum interindividual distance, after which it decays strictly with rising pairwise metric distances. Significantly, the density of spatial neighbor distribution is strongly anisotropic, with an evident lack of interactions along individual velocity. Thus, it is found that in small-sized bird flocks, individuals reciprocally cooperate with a variational number of neighbors in metric space and tend to interact with closer time-varying neighbors, rather than interacting with a fixed number of topological ones. Finally, extensive numerical investigation is conducted to verify both the revealed interaction and decision-making principle during circular flights of pigeon flocks.

Algorithm for detecting violations of traffic rules based on computer vision approaches

Directory of Open Access Journals (Sweden)

Ibadov Samir

2017-01-01

Full Text Available We propose a new algorithm for automatic detect violations of traffic rules for improving the people safety on the unregulated pedestrian crossing. The algorithm uses multi-step proceedings. They are zebra detection, cars detection, and pedestrian detection. For car detection, we use faster R-CNN deep learning tool. The algorithm shows promising results in the detection violations of traffic rules.

Having Linguistic Rules and Knowing Linguistic Facts

Directory of Open Access Journals (Sweden)

Peter Ludlow

2010-11-01

Full Text Available

'Knowledge' doesn't correctly describe our relation to linguistic rules. It is too thick a notion (for example, we don't believe linguistic rules. On the other hand, 'cognize', without further elaboration, is too thin a notion, which is to say that it is too thin to play a role in a competence theory. One advantage of the term 'knowledge'-and presumably Chomsky's original motivation for using it-is that knowledge would play the right kind of role in a competence theory: Our competence would consist in a body of knowledge which we have and which we may or may not act upon-our performance need not conform to the linguistic rules that we know.

Is there a way out of the dilemma? I'm going to make the case that the best way to talk about grammatical rules is simply to say that we have them. That doesn't sound very deep, I know, but saying that we have individual rules leaves room for individual norm guidance in a way that 'cognize' does not. Saying we have a rule like subjacency is also thicker than merely saying we cognize it. Saying I have such a rule invites the interpretation that it is a rule for me-that I am normatively guided by it. The competence theory thus becomes a theory of the rules that we have. Whether we follow those rules is another matter entirely.

Dynamic Programming Approach for Exact Decision Rule Optimization

KAUST Repository

Amin, Talha

2013-01-01

This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from UCI Machine Learning Repository. © Springer-Verlag Berlin Heidelberg 2013.

Rule-violations sensitise towards negative and authority-related stimuli.

Science.gov (United States)

Wirth, Robert; Foerster, Anna; Rendel, Hannah; Kunde, Wilfried; Pfister, Roland

2018-05-01

Rule violations have usually been studied from a third-person perspective, identifying situational factors that render violations more or less likely. A first-person perspective of the agent that actively violates the rules, on the other hand, is only just beginning to emerge. Here we show that committing a rule violation sensitises towards subsequent negative stimuli as well as subsequent authority-related stimuli. In a Prime-Probe design, we used an instructed rule-violation task as the Prime and a word categorisation task as the Probe. Also, we employed a control condition that used a rule inversion task as the Prime (instead of rule violations). Probe targets were categorised faster after a violation relative to after a rule-based response if they related to either, negative valence or authority. Inversions, however, primed only negative stimuli and did not accelerate the categorisation of authority-related stimuli. A heightened sensitivity towards authority-related targets thus seems to be specific to rule violations. A control experiment showed that these effects cannot be explained in terms of semantic priming. Therefore, we propose that rule violations necessarily activate authority-related representations that make rule violations qualitatively different from simple rule inversions.

Dynamic programming approach to optimization of approximate decision rules

KAUST Repository

Amin, Talha

2013-02-01

This paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure R(T) which is the number of unordered pairs of rows with different decisions in the decision table T. For a nonnegative real number β, we consider β-decision rules that localize rows in subtables of T with uncertainty at most β. Our algorithm constructs a directed acyclic graph Δβ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most β. The graph Δβ(T) allows us to describe the whole set of so-called irredundant β-decision rules. We can describe all irredundant β-decision rules with minimum length, and after that among these rules describe all rules with maximum coverage. We can also change the order of optimization. The consideration of irredundant rules only does not change the results of optimization. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository. © 2012 Elsevier Inc. All rights reserved.

Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

Science.gov (United States)

Gardner, Brian; Sporea, Ioana; Grüning, André

2015-12-01

Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

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Qiang Yu

Full Text Available A new learning rule (Precise-Spike-Driven (PSD Synaptic Plasticity is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

Science.gov (United States)

Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Syntactic learning by mere exposure – An ERP study in adult learners

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Friederici Angela D

2009-07-01

Full Text Available Abstract Background Artificial language studies have revealed the remarkable ability of humans to extract syntactic structures from a continuous sound stream by mere exposure. However, it remains unclear whether the processes acquired in such tasks are comparable to those applied during normal language processing. The present study compares the ERPs to auditory processing of simple Italian sentences in native and non-native speakers after brief exposure to Italian sentences of a similar structure. The sentences contained a non-adjacent dependency between an auxiliary and the morphologically marked suffix of the verb. Participants were presented four alternating learning and testing phases. During learning phases only correct sentences were presented while during testing phases 50 percent of the sentences contained a grammatical violation. Results The non-native speakers successfully learned the dependency and displayed an N400-like negativity and a subsequent anteriorily distributed positivity in response to rule violations. The native Italian group showed an N400 followed by a P600 effect. Conclusion The presence of the P600 suggests that native speakers applied a grammatical rule. In contrast, non-native speakers appeared to use a lexical form-based processing strategy. Thus, the processing mechanisms acquired in the language learning task were only partly comparable to those applied by competent native speakers.

The Role of Feedback Contingency in Perceptual Category Learning

Science.gov (United States)

Ashby, F. Gregory; Vucovich, Lauren E.

2016-01-01

Feedback is highly contingent on behavior if it eventually becomes easy to predict, and weakly contingent on behavior if it remains difficult or impossible to predict even after learning is complete. Many studies have demonstrated that humans and nonhuman animals are highly sensitive to feedback contingency, but no known studies have examined how feedback contingency affects category learning, and current theories assign little or no importance to this variable. Two experiments examined the effects of contingency degradation on rule-based and information-integration category learning. In rule-based tasks, optimal accuracy is possible with a simple explicit rule, whereas optimal accuracy in information-integration tasks requires integrating information from two or more incommensurable perceptual dimensions. In both experiments, participants each learned rule-based or information-integration categories under either high or low levels of feedback contingency. The exact same stimuli were used in all four conditions and optimal accuracy was identical in every condition. Learning was good in both high-contingency conditions, but most participants showed little or no evidence of learning in either low-contingency condition. Possible causes of these effects are discussed, as well as their theoretical implications. PMID:27149393

Simulation of skill acquisition in sequential learning of a computer game

DEFF Research Database (Denmark)

Hansen, John Paulin; Nielsen, Finn Ravnsbjerg; Rasmussen, Jens

1995-01-01

The paper presents some theoretical assumptions about the cognitive control mechanisms of subjects learning to play a computer game. A simulation model has been developed to investigate these assumptions. The model is an automaton, reacting to instruction-like cue action rules. The prototypical...... performances of 23 experimental subjects at succeeding levels of training are compared to the performance of the model. The findings are interpreted in terms of a general taxonomy for cognitive task analysis....

A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice

OpenAIRE

Bathellier, Brice; Tee, Sui Poh; Hrovat, Christina; Rumpel, Simon

2013-01-01

Learning speed can strongly differ across individuals. This is seen in humans and animals. Here, we measured learning speed in mice performing a discrimination task and developed a theoretical model based on the reinforcement learning framework to account for differences between individual mice. We found that, when using a multiplicative learning rule, the starting connectivity values of the model strongly determine the shape of learning curves. This is in contrast to current learning models ...

Democracy as the Rule of Nobody. Does It Make Sense Today?

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Tonči Kursar

2012-01-01

Full Text Available I would like to contribute to the ongoing debate on democracy by discussing the notion of the rule of nobody. I first address Rosanvallon's theory of counter-democracy and Keane's concept of monitory democracy. Keane writes about 'monitory democracy' not only as a new phase in the development of democracy on a global scale, but primarily as an abolishment of all domination in human relations. His idea that in a monitory democracy 'no body rules', has been criticized by John Dunn and John Gray. They consider it meaningless to claim that in democracy 'no body rules', since every form of rule needs rulers. I would like to show that both this supposedly realistic criticism and Keane's version of the rule of nobody are too literal and superficial. If we consider democracy to be a kind of sentiment rather than a set of political institutions, we get closer to the puzzling idea that 'no body rules'. This idea, namely, is not about abolishing the rule of men over men, but about being aware of the contingency of all forms of mastery. This was well known to Plato and has been convincingly revived in the works of the French philosopher Jacques Rancière.

Learning invariance from natural images inspired by observations in the primary visual cortex.

Science.gov (United States)

Teichmann, Michael; Wiltschut, Jan; Hamker, Fred

2012-05-01

The human visual system has the remarkable ability to largely recognize objects invariant of their position, rotation, and scale. A good interpretation of neurobiological findings involves a computational model that simulates signal processing of the visual cortex. In part, this is likely achieved step by step from early to late areas of visual perception. While several algorithms have been proposed for learning feature detectors, only few studies at hand cover the issue of biologically plausible learning of such invariance. In this study, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is proposed. Their performance is verified within a simple model of the primary visual cortex to learn so-called complex cells, based on a sequence of static images. As a result, the learned complex-cell responses are largely invariant to phase and position.

A "Sweet 16" of Rules About Teamwork

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Laufer, Alexander (Editor)

2002-01-01

The following "Sweet 16" rules included in this paper derive from a longer paper by APPL Director Dr. Edward Hoffman and myself entitled " 99 Rules for Managing Faster, Better, Cheaper Projects." Our sources consisted mainly of "war stories" told by master project managers in my book Simultaneous Management: Managing Projects in a Dynamic Environment (AMACOM, The American Management Association, 1996). The Simultaneous Management model was a result of 10 years of intensive research and testing conducted with the active participation of master project managers from leading private organizations such as AT&T, DuPont, Exxon, General Motors, IBM, Motorola and Procter & Gamble. In a more recent study, led by Dr. Hoffman, we learned that master project managers in leading public organizations employ most of these rules as well. Both studies, in private and public organizations, found that a dynamic environment calls for dynamic management, and that is especially clear in how successful project managers think about their teams.

Modeling the learning of the English past tense with memory-based learning

NARCIS (Netherlands)

van Noord, Rik; Spenader, Jennifer K.

2015-01-01

Modeling the acquisition and final state of English past tense inflection has been an ongoing challenge since the mid-eighties. A number of rule-based and connectionist models have been proposed over the years, but the former usually have no explanation of how the rules are learned and the latter

What can we learn from sum rules for vertex functions in QCD

International Nuclear Information System (INIS)

Craigie, N.S.; Stern, J.

1982-04-01

We demonstrate that the light cone sum rules for vertex functions based on the operator product expansion and QCD perturbation theory lead to interesting relationships between various non-perturbative parameters associated with hadronic bound states (e.g. vertex couplings and decay constants). We also show that such sum rules provide a valuable means of estimating the matrix elements of the higher spin operators in the meson wave function. (author)

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

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Alexander eHanuschkin

2013-06-01

Full Text Available Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: Random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, they allow for imitating arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions.Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird’s own song

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models.

Science.gov (United States)

Hanuschkin, A; Ganguli, S; Hahnloser, R H R

2013-01-01

Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

Science.gov (United States)

Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

2017-05-01

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

Multimedia Football Viewing: Embedded Rules, Practice, and Video Context in IVD Procedural Learning.

Science.gov (United States)

Kim, Eunsoon; Young, Michael F.

This study investigated the effects of interactive video (IVD) instruction with embedded rules (production system rules) and practice with feedback on learners' academic achievement and perceived self efficacy in the domain of procedural knowledge for watching professional football. Subjects were 71 female volunteers from undergraduate education…

Rule-based Test Generation with Mind Maps

Directory of Open Access Journals (Sweden)

Dimitry Polivaev

2012-02-01

Full Text Available This paper introduces basic concepts of rule based test generation with mind maps, and reports experiences learned from industrial application of this technique in the domain of smart card testing by Giesecke & Devrient GmbH over the last years. It describes the formalization of test selection criteria used by our test generator, our test generation architecture and test generation framework.

Visual Perceptual Learning and its Specificity and Transfer: A New Perspective

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Cong Yu

2011-05-01

Full Text Available Visual perceptual learning is known to be location and orientation specific, and is thus assumed to reflect the neuronal plasticity in the early visual cortex. However, in recent studies we created “Double training” and “TPE” procedures to demonstrate that these “fundamental” specificities of perceptual learning are in some sense artifacts and that learning can completely transfer to a new location or orientation. We proposed a rule-based learning theory to reinterpret perceptual learning and its specificity and transfer: A high-level decision unit learns the rules of performing a visual task through training. However, the learned rules cannot be applied to a new location or orientation automatically because the decision unit cannot functionally connect to new visual inputs with sufficient strength because these inputs are unattended or even suppressed during training. It is double training and TPE training that reactivate these new inputs, so that the functional connections can be strengthened to enable rule application and learning transfer. Currently we are investigating the properties of perceptual learning free from the bogus specificities, and the results provide some preliminary but very interesting insights into how training reshapes the functional connections between the high-level decision units and sensory inputs in the brain.

Priming for performance: valence of emotional primes interact with dissociable prototype learning systems.

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Marissa A Gorlick

Full Text Available Arousal Biased Competition theory suggests that arousal enhances competitive attentional processes, but makes no strong claims about valence effects. Research suggests that the scope of enhanced attention depends on valence with negative arousal narrowing and positive arousal broadening attention. Attentional scope likely affects declarative-memory-mediated and perceptual-representation-mediated learning systems differently, with declarative-memory-mediated learning depending on narrow attention to develop targeted verbalizable rules, and perceptual-representation-mediated learning depending on broad attention to develop a perceptual representation. We hypothesize that negative arousal accentuates declarative-memory-mediated learning and attenuates perceptual-representation-mediated learning, while positive arousal reverses this pattern. Prototype learning provides an ideal test bed as dissociable declarative-memory and perceptual-representation systems mediate two-prototype (AB and one-prototype (AN prototype learning, respectively, and computational models are available that provide powerful insights on cognitive processing. As predicted, we found that negative arousal narrows attentional focus facilitating AB learning and impairing AN learning, while positive arousal broadens attentional focus facilitating AN learning and impairing AB learning.

Priming for performance: valence of emotional primes interact with dissociable prototype learning systems.

Science.gov (United States)

Gorlick, Marissa A; Maddox, W Todd

2013-01-01

Arousal Biased Competition theory suggests that arousal enhances competitive attentional processes, but makes no strong claims about valence effects. Research suggests that the scope of enhanced attention depends on valence with negative arousal narrowing and positive arousal broadening attention. Attentional scope likely affects declarative-memory-mediated and perceptual-representation-mediated learning systems differently, with declarative-memory-mediated learning depending on narrow attention to develop targeted verbalizable rules, and perceptual-representation-mediated learning depending on broad attention to develop a perceptual representation. We hypothesize that negative arousal accentuates declarative-memory-mediated learning and attenuates perceptual-representation-mediated learning, while positive arousal reverses this pattern. Prototype learning provides an ideal test bed as dissociable declarative-memory and perceptual-representation systems mediate two-prototype (AB) and one-prototype (AN) prototype learning, respectively, and computational models are available that provide powerful insights on cognitive processing. As predicted, we found that negative arousal narrows attentional focus facilitating AB learning and impairing AN learning, while positive arousal broadens attentional focus facilitating AN learning and impairing AB learning.

Collaboration rules.

Science.gov (United States)

Evans, Philip; Wolf, Bob

2005-01-01

Corporate leaders seeking to boost growth, learning, and innovation may find the answer in a surprising place: the Linux open-source software community. Linux is developed by an essentially volunteer, self-organizing community of thousands of programmers. Most leaders would sell their grandmothers for workforces that collaborate as efficiently, frictionlessly, and creatively as the self-styled Linux hackers. But Linux is software, and software is hardly a model for mainstream business. The authors have, nonetheless, found surprising parallels between the anarchistic, caffeinated, hirsute world of Linux hackers and the disciplined, tea-sipping, clean-cut world of Toyota engineering. Specifically, Toyota and Linux operate by rules that blend the self-organizing advantages of markets with the low transaction costs of hierarchies. In place of markets' cash and contracts and hierarchies' authority are rules about how individuals and groups work together (with rigorous discipline); how they communicate (widely and with granularity); and how leaders guide them toward a common goal (through example). Those rules, augmented by simple communication technologies and a lack of legal barriers to sharing information, create rich common knowledge, the ability to organize teams modularly, extraordinary motivation, and high levels of trust, which radically lowers transaction costs. Low transaction costs, in turn, make it profitable for organizations to perform more and smaller transactions--and so increase the pace and flexibility typical of high-performance organizations. Once the system achieves critical mass, it feeds on itself. The larger the system, the more broadly shared the knowledge, language, and work style. The greater individuals' reputational capital, the louder the applause and the stronger the motivation. The success of Linux is evidence of the power of that virtuous circle. Toyota's success is evidence that it is also powerful in conventional companies.

Constructing the Syllabus: Devising a Framework for Helping Students Learn to Think like Historians

Science.gov (United States)

Estes, Todd

2007-01-01

In this article, the author describes a syllabus which he designed in his United States history survey courses to help his students learn to think like historians. It contains important information about the way historians work and think, along with descriptions of the reading materials the student will use to further their practice of history.…

Counter-ions at single charged wall: Sum rules.

Science.gov (United States)

Samaj, Ladislav

2013-09-01

For inhomogeneous classical Coulomb fluids in thermal equilibrium, like the jellium or the two-component Coulomb gas, there exists a variety of exact sum rules which relate the particle one-body and two-body densities. The necessary condition for these sum rules is that the Coulomb fluid possesses good screening properties, i.e. the particle correlation functions or the averaged charge inhomogeneity, say close to a wall, exhibit a short-range (usually exponential) decay. In this work, we study equilibrium statistical mechanics of an electric double layer with counter-ions only, i.e. a globally neutral system of equally charged point-like particles in the vicinity of a plain hard wall carrying a fixed uniform surface charge density of opposite sign. At large distances from the wall, the one-body and two-body counter-ion densities go to zero slowly according to the inverse-power law. In spite of the absence of screening, all known sum rules are shown to hold for two exactly solvable cases of the present system: in the weak-coupling Poisson-Boltzmann limit (in any spatial dimension larger than one) and at a special free-fermion coupling constant in two dimensions. This fact indicates an extended validity of the sum rules and provides a consistency check for reasonable theoretical approaches.

Residential energy contracts and the 28 day rule

International Nuclear Information System (INIS)

Littlechild, Stephen

2006-01-01

What measures are needed to protect customers when a utility market is first opened to competition? In the UK, residential (domestic) customers must be able to terminate energy contracts at 28 days' notice. This rule was introduced as a transitional protection for customers and for competition. However, the regulatory justification for the rule seems to have evolved over time. Removing the rule could have a number of advantages, including the development of fixed-price fixed-term contracts. The advantages of retaining the rule are questionable. In other retail sectors there is no regulatory concern or requirement of this kind. UK electricity suppliers have begun to offer capped prices for specified periods of time, suggesting that there is a growing customer demand for this. Fixed-price fixed-term contracts are a common form of competition in Scandinavia. The 28 day rule no longer seems necessary to protect customers and is more likely to distort than to protect competition. In retrospect, it would have been preferable not to introduce the rule in the first place. (author)

A Multistep Maturity Model for the Implementation of Electronic and Computable Diagnostic Clinical Prediction Rules (eCPRs).

Science.gov (United States)

Corrigan, Derek; McDonnell, Ronan; Zarabzadeh, Atieh; Fahey, Tom

2015-01-01

The use of Clinical Prediction Rules (CPRs) has been advocated as one way of implementing actionable evidence-based rules in clinical practice. The current highly manual nature of deriving CPRs makes them difficult to use and maintain. Addressing the known limitations of CPRs requires implementing more flexible and dynamic models of CPR development. We describe the application of Information and Communication Technology (ICT) to provide a platform for the derivation and dissemination of CPRs derived through analysis and continual learning from electronic patient data. We propose a multistep maturity model for constructing electronic and computable CPRs (eCPRs). The model has six levels - from the lowest level of CPR maturity (literaturebased CPRs) to a fully electronic and computable service-oriented model of CPRs that are sensitive to specific demographic patient populations. We describe examples of implementations of the core model components - focusing on CPR representation, interoperability, electronic dissemination, CPR learning, and user interface requirements. The traditional focus on derivation and narrow validation of CPRs has severely limited their wider acceptance. The evolution and maturity model described here outlines a progression toward eCPRs consistent with the vision of a learning health system (LHS) - using central repositories of CPR knowledge, accessible open standards, and generalizable models to avoid repetition of previous work. This is useful for developing more ambitious strategies to address limitations of the traditional CPR development life cycle. The model described here is a starting point for promoting discussion about what a more dynamic CPR development process should look like.

Adaptive WTA with an analog VLSI neuromorphic learning chip.

Science.gov (United States)

Häfliger, Philipp

2007-03-01

In this paper, we demonstrate how a particular spike-based learning rule (where exact temporal relations between input and output spikes of a spiking model neuron determine the changes of the synaptic weights) can be tuned to express rate-based classical Hebbian learning behavior (where the average input and output spike rates are sufficient to describe the synaptic changes). This shift in behavior is controlled by the input statistic and by a single time constant. The learning rule has been implemented in a neuromorphic very large scale integration (VLSI) chip as part of a neurally inspired spike signal image processing system. The latter is the result of the European Union research project Convolution AER Vision Architecture for Real-Time (CAVIAR). Since it is implemented as a spike-based learning rule (which is most convenient in the overall spike-based system), even if it is tuned to show rate behavior, no explicit long-term average signals are computed on the chip. We show the rule's rate-based Hebbian learning ability in a classification task in both simulation and chip experiment, first with artificial stimuli and then with sensor input from the CAVIAR system.

Evaluation of wholesale electric power market rules and financial risk management by agent-based simulations

Science.gov (United States)

Yu, Nanpeng

As U.S. regional electricity markets continue to refine their market structures, designs and rules of operation in various ways, two critical issues are emerging. First, although much experience has been gained and costly and valuable lessons have been learned, there is still a lack of a systematic platform for evaluation of the impact of a new market design from both engineering and economic points of view. Second, the transition from a monopoly paradigm characterized by a guaranteed rate of return to a competitive market created various unfamiliar financial risks for various market participants, especially for the Investor Owned Utilities (IOUs) and Independent Power Producers (IPPs). This dissertation uses agent-based simulation methods to tackle the market rules evaluation and financial risk management problems. The California energy crisis in 2000-01 showed what could happen to an electricity market if it did not go through a comprehensive and rigorous testing before its implementation. Due to the complexity of the market structure, strategic interaction between the participants, and the underlying physics, it is difficult to fully evaluate the implications of potential changes to market rules. This dissertation presents a flexible and integrative method to assess market designs through agent-based simulations. Realistic simulation scenarios on a 225-bus system are constructed for evaluation of the proposed PJM-like market power mitigation rules of the California electricity market. Simulation results show that in the absence of market power mitigation, generation company (GenCo) agents facilitated by Q-learning are able to exploit the market flaws and make significantly higher profits relative to the competitive benchmark. The incorporation of PJM-like local market power mitigation rules is shown to be effective in suppressing the exercise of market power. The importance of financial risk management is exemplified by the recent financial crisis. In this

VALUATION OF THE BLOG LIKE REVITALIZING WAY OF PROCESSES OF EDUCATION-LEARNING IN THE NORMAL EDUCATION

Directory of Open Access Journals (Sweden)

María Isabel Ramírez-Ochoa

2016-07-01

Full Text Available The article shares the results of the intervention Project blog called “Proyecto de lectura y escritura, ENEF” as an educational technological media to transform traditional learning environment of the course "Reading and Writing Workshop", during the 2012-1 school year, the group student educator form second semester. With this educational initiative is intended to 50 students use this technological medium as support in the process of teaching and learning. The investigation began with diagnosed digital skills as much as teachers, like students. Subsequently, the behavior of the teacher and the group blog was analyzed. The results demonstrate the usefulness of this technology as a catalyst for learning processes.

Optimization of approximate decision rules relative to number of misclassifications

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2012-01-01

In the paper, we study an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the number of misclassifications. We introduce an uncertainty measure J(T) which is a difference between the number of rows in a decision table T and the number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules that localize rows in subtables of T with uncertainty at most γ. The presented algorithm constructs a directed acyclic graph Δγ(T). Based on this graph we can describe the whole set of so-called irredundant γ-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 The authors and IOS Press. All rights reserved.

Optimization of approximate decision rules relative to number of misclassifications

KAUST Repository

Amin, Talha

2012-12-01

In the paper, we study an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the number of misclassifications. We introduce an uncertainty measure J(T) which is a difference between the number of rows in a decision table T and the number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules that localize rows in subtables of T with uncertainty at most γ. The presented algorithm constructs a directed acyclic graph Δγ(T). Based on this graph we can describe the whole set of so-called irredundant γ-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 The authors and IOS Press. All rights reserved.

Introduction of a Nonfermented Bread-like Food as a Learning Material in Home Economics Education

OpenAIRE

冨永, 美穂子; 小林, 京子; 森, 敏昭; 佐藤, 一精

1999-01-01

An attempt was made to prepare a nonfermented bread-like food usable as home economics teaching and learning material. The new receipe could be a model for showing the leavening process of bread by use of sodium bicarbonate and yogurt as an acidifying agent. In this study, the nonfermented bread was used in the class by forty second grade high school students on the theme of "Infant foods — focusing on snacks— " The students favorably evaluated the bread-like food as being easy to prepare...

Class Association Rule Pada Metode Associative Classification

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Eka Karyawati

2011-11-01

Full Text Available Frequent patterns (itemsets discovery is an important problem in associative classification rule mining.  Differents approaches have been proposed such as the Apriori-like, Frequent Pattern (FP-growth, and Transaction Data Location (Tid-list Intersection algorithm. This paper focuses on surveying and comparing the state of the art associative classification techniques with regards to the rule generation phase of associative classification algorithms.  This phase includes frequent itemsets discovery and rules mining/extracting methods to generate the set of class association rules (CARs.  There are some techniques proposed to improve the rule generation method.  A technique by utilizing the concepts of discriminative power of itemsets can reduce the size of frequent itemset.  It can prune the useless frequent itemsets. The closed frequent itemset concept can be utilized to compress the rules to be compact rules.  This technique may reduce the size of generated rules.  Other technique is in determining the support threshold value of the itemset. Specifying not single but multiple support threshold values with regard to the class label frequencies can give more appropriate support threshold value.  This technique may generate more accurate rules. Alternative technique to generate rule is utilizing the vertical layout to represent dataset.  This method is very effective because it only needs one scan over dataset, compare with other techniques that need multiple scan over dataset.   However, one problem with these approaches is that the initial set of tid-lists may be too large to fit into main memory. It requires more sophisticated techniques to compress the tid-lists.

From rule to response: neuronal processes in the premotor and prefrontal cortex.

Science.gov (United States)

Wallis, Jonathan D; Miller, Earl K

2003-09-01

The ability to use abstract rules or principles allows behavior to generalize from specific circumstances (e.g., rules learned in a specific restaurant can subsequently be applied to any dining experience). Neurons in the prefrontal cortex (PFC) encode such rules. However, to guide behavior, rules must be linked to motor responses. We investigated the neuronal mechanisms underlying this process by recording from the PFC and the premotor cortex (PMC) of monkeys trained to use two abstract rules: "same" or "different." The monkeys had to either hold or release a lever, depending on whether two successively presented pictures were the same or different, and depending on which rule was in effect. The abstract rules were represented in both regions, although they were more prevalent and were encoded earlier and more strongly in the PMC. There was a perceptual bias in the PFC, relative to the PMC, with more PFC neurons encoding the presented pictures. In contrast, neurons encoding the behavioral response were more prevalent in the PMC, and the selectivity was stronger and appeared earlier in the PMC than in the PFC.

Input and Age-Dependent Variation in Second Language Learning: A Connectionist Account.

Science.gov (United States)

Janciauskas, Marius; Chang, Franklin

2017-07-26

Language learning requires linguistic input, but several studies have found that knowledge of second language (L2) rules does not seem to improve with more language exposure (e.g., Johnson & Newport, 1989). One reason for this is that previous studies did not factor out variation due to the different rules tested. To examine this issue, we reanalyzed grammaticality judgment scores in Flege, Yeni-Komshian, and Liu's (1999) study of L2 learners using rule-related predictors and found that, in addition to the overall drop in performance due to a sensitive period, L2 knowledge increased with years of input. Knowledge of different grammar rules was negatively associated with input frequency of those rules. To better understand these effects, we modeled the results using a connectionist model that was trained using Korean as a first language (L1) and then English as an L2. To explain the sensitive period in L2 learning, the model's learning rate was reduced in an age-related manner. By assigning different learning rates for syntax and lexical learning, we were able to model the difference between early and late L2 learners in input sensitivity. The model's learning mechanism allowed transfer between the L1 and L2, and this helped to explain the differences between different rules in the grammaticality judgment task. This work demonstrates that an L1 model of learning and processing can be adapted to provide an explicit account of how the input and the sensitive period interact in L2 learning. © 2017 The Authors. Cognitive Science - A Multidisciplinary Journal published by Wiley Periodicals, Inc.

Machine learning with quantum relative entropy

International Nuclear Information System (INIS)

Tsuda, Koji

2009-01-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Machine learning with quantum relative entropy

Energy Technology Data Exchange (ETDEWEB)

Tsuda, Koji [Max Planck Institute for Biological Cybernetics, Spemannstr. 38, Tuebingen, 72076 (Germany)], E-mail: koji.tsuda@tuebingen.mpg.de

2009-12-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Blue light filtered white light induces depression-like responses and temporary spatial learning deficits in rats.

Science.gov (United States)

Meng, Qinghe; Lian, Yuzheng; Jiang, Jianjun; Wang, Wei; Hou, Xiaohong; Pan, Yao; Chu, Hongqian; Shang, Lanqin; Wei, Xuetao; Hao, Weidong

2018-04-18

Ambient light has a vital impact on mood and cognitive functions. Blue light has been previously reported to play a salient role in the antidepressant effect via melanopsin. Whether blue light filtered white light (BFW) affects mood and cognitive functions remains unclear. The present study aimed to investigate whether BFW led to depression-like symptoms and cognitive deficits including spatial learning and memory abilities in rats, and whether they were associated with the light-responsive function in retinal explants. Male Sprague-Dawley albino rats were randomly divided into 2 groups (n = 10) and treated with a white light-emitting diode (LED) light source and BFW light source, respectively, under a standard 12 : 12 h L/D condition over 30 days. The sucrose consumption test, forced swim test (FST) and the level of plasma corticosterone (CORT) were employed to evaluate depression-like symptoms in rats. Cognitive functions were assessed by the Morris water maze (MWM) test. A multi-electrode array (MEA) system was utilized to measure electro-retinogram (ERG) responses induced by white or BFW flashes. The effect of BFW over 30 days on depression-like responses in rats was indicated by decreased sucrose consumption in the sucrose consumption test, an increased immobility time in the FST and an elevated level of plasma CORT. BFW led to temporary spatial learning deficits in rats, which was evidenced by prolonged escape latency and swimming distances in the spatial navigation test. However, no changes were observed in the short memory ability of rats treated with BFW. The micro-ERG results showed a delayed implicit time and reduced amplitudes evoked by BFW flashes compared to the white flash group. BFW induces depression-like symptoms and temporary spatial learning deficits in rats, which might be closely related to the impairment of light-evoked output signals in the retina.

Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality

Science.gov (United States)

Grytskyy, Dmytro; Diesmann, Markus; Helias, Moritz

2016-06-01

Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic feed-forward networks of nonlinear rate-based model neurons with a correlation sensitive learning rule inspired by and being qualitatively similar to STDP. After obtaining equations that describe the change of the spatial shape of the signal from layer to layer, we derive a criterion for the nonlinearity necessary to obtain stable dynamics for arbitrary input. We classify the possible scenarios of signal evolution and find that close to the transition to the unstable regime metastable solutions appear. The form of these dissipative solitons is determined analytically and the evolution and interaction of several such coexistent objects is investigated.

The relationship between the structural mere exposure effect and the implicit learning process.

Science.gov (United States)

Newell, B R; Bright, J E

2001-11-01

Three experiments are reported that investigate the relationship between the structural mere exposure effect (SMEE) and implicit learning in an artificial grammar task. Subjects were presented with stimuli generated from a finite-state grammar and were asked to memorize them. In a subsequent test phase subjects were required first to rate how much they liked novel items, and second whether or not they thought items conformed to the rules of the grammar. A small but consistent effect of grammaticality was found on subjects' liking ratings (a "structural mere exposure effect") in all three experiments, but only when encoding and testing conditions were consistent. A change in the surface representation of stimuli between encoding and test (Experiment 1), memorizing fragments of items and being tested on whole items (Experiment 2), and a mismatch of processing operations between encoding and test (Experiment 3) all removed the SMEE. In contrast, the effect of grammaticality on rule judgements remained intact in the face of all three manipulations. It is suggested that rule judgements reflect attempts to explicitly recall information about training items, whereas the SMEE can be explained in terms of an attribution of processing fluency.

Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning.

Science.gov (United States)

Oudeyer, Pierre-Yves

2017-01-01

Autonomous lifelong development and learning are fundamental capabilities of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives.

A rule-based stemmer for Arabic Gulf dialect

Directory of Open Access Journals (Sweden)

Belal Abuata

2015-04-01

Full Text Available Arabic dialects arewidely used from many years ago instead of Modern Standard Arabic language in many fields. The presence of dialects in any language is a big challenge. Dialects add a new set of variational dimensions in some fields like natural language processing, information retrieval and even in Arabic chatting between different Arab nationals. Spoken dialects have no standard morphological, phonological and lexical like Modern Standard Arabic. Hence, the objective of this paper is to describe a procedure or algorithm by which a stem for the Arabian Gulf dialect can be defined. The algorithm is rule based. Special rules are created to remove the suffixes and prefixes of the dialect words. Also, the algorithm applies rules related to the word size and the relation between adjacent letters. The algorithm was tested for a number of words and given a good correct stem ratio. The algorithm is also compared with two Modern Standard Arabic algorithms. The results showed that Modern Standard Arabic stemmers performed poorly with Arabic Gulf dialect and our algorithm performed poorly when applied for Modern Standard Arabic words.

Accurate crop classification using hierarchical genetic fuzzy rule-based systems

Science.gov (United States)

Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

2014-10-01

This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

Knowledge of display rules in prelingually deaf and hearing children.

Science.gov (United States)

Hosie, J A; Russell, P A; Gray, C D; Scott, C; Hunter, N; Banks, J S; Macaulay, M C

2000-03-01

Deaf children of elementary and secondary school age participated in a study designed to examine their understanding of display rules, the principles governing the expression and concealment of emotion in social situations. The results showed that deaf children's knowledge of display rules, as measured by their reported concealment of emotion, was comparable to that of hearing children of the same age. However, deaf children were less likely to report that they would conceal happiness and anger. They were also less likely to produce reasons for concealing emotion and a smaller proportion of their reasons were prosocial, that is, relating to the feelings of others. The results suggest that the understanding of display rules (which function to protect the feelings of other people) may develop more gradually in deaf children raised in a spoken language environment than it does in hearing children.

Cutkosky rules for superstring field theory

International Nuclear Information System (INIS)

Pius, Roji; Sen, Ashoke

2016-01-01

Superstring field theory expresses the perturbative S-matrix of superstring theory as a sum of Feynman diagrams each of which is manifestly free from ultraviolet divergences. The interaction vertices fall off exponentially for large space-like external momenta making the ultraviolet finiteness property manifest, but blow up exponentially for large time-like external momenta making it impossible to take the integration contours for loop energies to lie along the real axis. This forces us to carry out the integrals over the loop energies by choosing appropriate contours in the complex plane whose ends go to infinity along the imaginary axis but which take complicated form in the interior navigating around the various poles of the propagators. We consider the general class of quantum field theories with this property and prove Cutkosky rules for the amplitudes to all orders in perturbation theory. Besides having applications to string field theory, these results also give an alternative derivation of Cutkosky rules in ordinary quantum field theories.

Research on Fault Diagnosis Method Based on Rule Base Neural Network

Directory of Open Access Journals (Sweden)

Zheng Ni

2017-01-01

Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

Memory formation in reversal learning of the honeybee

Directory of Open Access Journals (Sweden)

Ravit Hadar

2010-12-01

Full Text Available In reversal learning animals are first trained with a differential learning protocol, where they learn to respond to a reinforced odor (CS+ and not to respond to a nonreinforced odor (CS-. Once they respond correctly to this rule, the contingencies of the conditioned stimuli are reversed, and animals learn to adjust their response to the new rule. This study investigated the effect of a protein synthesis inhibitor (emetine on the memory formed after reversal learning in the honeybee Apis mellifera. Two groups of bees were studied: summer bees and winter bees, each yielded different results. Blocking protein synthesis in summer bees inhibits consolidation of the excitatory learning following reversal learning whereas it blocked the consolidation of the inhibitory learning in winter bees. These findings suggest that excitatory and inhibitory learning may involve different molecular processes in bees, which are seasonally dependent.

Inductive learning of thyroid functional states using the ID3 algorithm. The effect of poor examples on the learning result.

Science.gov (United States)

Forsström, J

1992-01-01

The ID3 algorithm for inductive learning was tested using preclassified material for patients suspected to have a thyroid illness. Classification followed a rule-based expert system for the diagnosis of thyroid function. Thus, the knowledge to be learned was limited to the rules existing in the knowledge base of that expert system. The learning capability of the ID3 algorithm was tested with an unselected learning material (with some inherent missing data) and with a selected learning material (no missing data). The selected learning material was a subgroup which formed a part of the unselected learning material. When the number of learning cases was increased, the accuracy of the program improved. When the learning material was large enough, an increase in the learning material did not improve the results further. A better learning result was achieved with the selected learning material not including missing data as compared to unselected learning material. With this material we demonstrate a weakness in the ID3 algorithm: it can not find available information from good example cases if we add poor examples to the data.

Iranian EFL Teachers’ Perceptions of Learning Accent

Directory of Open Access Journals (Sweden)

Hassan Galbat

2018-05-01

Full Text Available Since the appearance of “Audio-lingual Method”, the issue of foreign accent has been the focus of many researchers and many teachers attempted to sound as native like as possible to better teach native English accent. The present study attempted to uncover the Iranian EFL teachers’ perceptions on accent, the way they viewed their own accent, and how English accent can be improved. Totally 50 male and female teachers with different age range, qualifications, teaching and learning experiences participated in the study. The data of the study were collected using Teachers’ Perceptions of Accent Questionnaire developed by the researcher and semi-structured interviews. Based on the analysis performed on the data collected through questionnaires and interviews, it was found that teachers care about learning accent and they considered it valuable and important. They admitted that they have foreign accent to some degree and they did not seem to be happy with foreign accent and were more in favour of native like accent. Regarding the strategies to improve English accent, they mentioned techniques like listening to authentic language, understanding pronunciation rules, and comparing people’s accent with their own accent, watching English movies, noticing stress, and pronunciation patterns, imitating, speaking with native people, using books, and recording and monitoring their own speeches.

Evaluation of probabilistic forecasts with the scoringRules package

Science.gov (United States)

Jordan, Alexander; Krüger, Fabian; Lerch, Sebastian

2017-04-01

Over the last decades probabilistic forecasts in the form of predictive distributions have become popular in many scientific disciplines. With the proliferation of probabilistic models arises the need for decision-theoretically principled tools to evaluate the appropriateness of models and forecasts in a generalized way in order to better understand sources of prediction errors and to improve the models. Proper scoring rules are functions S(F,y) which evaluate the accuracy of a forecast distribution F , given that an outcome y was observed. In coherence with decision-theoretical principles they allow to compare alternative models, a crucial ability given the variety of theories, data sources and statistical specifications that is available in many situations. This contribution presents the software package scoringRules for the statistical programming language R, which provides functions to compute popular scoring rules such as the continuous ranked probability score for a variety of distributions F that come up in applied work. For univariate variables, two main classes are parametric distributions like normal, t, or gamma distributions, and distributions that are not known analytically, but are indirectly described through a sample of simulation draws. For example, ensemble weather forecasts take this form. The scoringRules package aims to be a convenient dictionary-like reference for computing scoring rules. We offer state of the art implementations of several known (but not routinely applied) formulas, and implement closed-form expressions that were previously unavailable. Whenever more than one implementation variant exists, we offer statistically principled default choices. Recent developments include the addition of scoring rules to evaluate multivariate forecast distributions. The use of the scoringRules package is illustrated in an example on post-processing ensemble forecasts of temperature.

Business rules for creating process flexibility : Mapping RIF rules and BDI rules

NARCIS (Netherlands)

Gong, Y.; Overbeek, S.J.; Janssen, M.

2011-01-01

Business rules and software agents can be used for creating flexible business processes. The Rule Interchange Format (RIF) is a new W3C recommendation standard for exchanging rules among disparate systems. Yet, the impact that the introduction of RIF has on the design of flexible business processes

Teaching versus enforcing game rules in preschoolers' peer interactions.

Science.gov (United States)

Köymen, Bahar; Schmidt, Marco F H; Rost, Loreen; Lieven, Elena; Tomasello, Michael

2015-07-01

Children use normative language in two key contexts: when teaching others and when enforcing social norms. We presented pairs of 3- and 5-year-old peers (N=192) with a sorting game in two experimental conditions (in addition to a third baseline condition). In the teaching condition, one child was knowledgeable, whereas the other child was ignorant and so in need of instruction. In the enforcement condition, children learned conflicting rules so that each child was making mistakes from the other's point of view. When teaching rules to an ignorant partner, both age groups used generic normative language ("Bunnies go here"). When enforcing rules on a rule-breaking partner, 3-year-olds used normative utterances that were not generic and aimed at correcting individual behavior ("No, this goes there"), whereas 5-year-olds again used generic normative language, perhaps because they discerned that instruction was needed in this case as well. Young children normatively correct peers differently depending on their assessment of what their wayward partners need to bring them back into line. Copyright © 2015 Elsevier Inc. All rights reserved.

The EU Rules on Labelling of Genetically Modified Foods: Mission accomplished?

DEFF Research Database (Denmark)

Andersen, Lars Bracht

2010-01-01

of GMO related content in a food product is in fact an appropriate measure to protect consumer interests. Furthermore, the EU labelling rules may prove to be a trade obstacle causing conflict in the EU’s trade relations with third countries. The labelling rules will most likely be considered a trade......In 2003 the EU adopted new rules regulating all aspects of placing genetically modified foods on the market. The rules significantly enhance the scope of the labelling requirement in an attempt to accommodate consumer demand, but it is debatable whether or not a labelling requirement in the case...

Rule-Based Event Processing and Reaction Rules

Science.gov (United States)

Paschke, Adrian; Kozlenkov, Alexander

Reaction rules and event processing technologies play a key role in making business and IT / Internet infrastructures more agile and active. While event processing is concerned with detecting events from large event clouds or streams in almost real-time, reaction rules are concerned with the invocation of actions in response to events and actionable situations. They state the conditions under which actions must be taken. In the last decades various reaction rule and event processing approaches have been developed, which for the most part have been advanced separately. In this paper we survey reaction rule approaches and rule-based event processing systems and languages.

Learning in the machine: The symmetries of the deep learning channel.

Science.gov (United States)

Baldi, Pierre; Sadowski, Peter; Lu, Zhiqin

2017-11-01

In a physical neural system, learning rules must be local both in space and time. In order for learning to occur, non-local information must be communicated to the deep synapses through a communication channel, the deep learning channel. We identify several possible architectures for this learning channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges: (1) symmetry of architectures; (2) symmetry of weights; (3) symmetry of neurons; (4) symmetry of derivatives; (5) symmetry of processing; and (6) symmetry of learning rules. Random backpropagation (RBP) addresses the second and third symmetry, and some of its variations, such as skipped RBP (SRBP) address the first and the fourth symmetry. Here we address the last two desirable symmetries showing through simulations that they can be achieved and that the learning channel is particularly robust to symmetry variations. Specifically, random backpropagation and its variations can be performed with the same non-linear neurons used in the main input-output forward channel, and the connections in the learning channel can be adapted using the same algorithm used in the forward channel, removing the need for any specialized hardware in the learning channel. Finally, we provide mathematical results in simple cases showing that the learning equations in the forward and backward channels converge to fixed points, for almost any initial conditions. In symmetric architectures, if the weights in both channels are small at initialization, adaptation in both channels leads to weights that are essentially symmetric during and after learning. Biological connections are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Debate as Encapsulated Conflict: Ruled Controversy as an Approach to Learning Conflict Management Skills.

Science.gov (United States)

Lee, David G.; Hensley, Carl Wayne

Debate can provide a format for the development of communication skills to aid students in managing conflicts, because an understanding of rule-governed communication in conflict situations is invaluable in constructive conflict management. Since in debate procedural rules restrict discussion primarily to substantive and procedural topics, debate…

The OZI rule and nucleons

International Nuclear Information System (INIS)

Lipkin, H.J.

1991-11-01

The title of this lecture series raises two questions: (1) what is the OZI rule? (2) what is a nucleon. In the lectures both questions were addressed in parallel and the material moved back and forth between them. In a written version it seems more appropriate to treat the two question separately, begining with trying to understand the structure of the nucleon. Experimental evidence for the symmetry and quark structure of hadrons is reviewed with a historical introduction and updated by presenting constituent quark model relation for hadron masses and magnetic moments.Three definitions of the OZI rule are presented, all which forbid decay like φ->ρπ but making different selection rules for more complicate reactions. All suffer from the higer order paradox that a forbidden process can take place via two-step transition in which each step is allowed; e.g. φ-> KK-bar -> ρπ. No prescription is given for estimating the strength of forbidden processes. The role of cancellations between different higer order diagrams is discussed. (author)

Neuronal avalanches and learning

Energy Technology Data Exchange (ETDEWEB)

Arcangelis, Lucilla de, E-mail: dearcangelis@na.infn.it [Department of Information Engineering and CNISM, Second University of Naples, 81031 Aversa (Italy)

2011-05-01

Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

Neuronal avalanches and learning

International Nuclear Information System (INIS)

Arcangelis, Lucilla de

2011-01-01

Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

Fronto-parietal contributions to phonological processes in successful artificial grammar learning

Directory of Open Access Journals (Sweden)

Dariya Goranskaya

2016-11-01

Full Text Available Sensitivity to regularities plays a crucial role in the acquisition of various linguistic features from spoken language input. Artificial grammar (AG learning paradigms explore pattern recognition abilities in a set of structured sequences (i.e. of syllables or letters. In the present study, we investigated the functional underpinnings of learning phonological regularities in auditorily presented syllable sequences. While previous neuroimaging studies either focused on functional differences between the processing of correct vs. incorrect sequences or between different levels of sequence complexity, here the focus is on the neural foundation of the actual learning success. During functional magnetic resonance imaging (fMRI, participants were exposed to a set of syllable sequences with an underlying phonological rule system, known to ensure performance differences between participants. We expected that successful learning and rule application would require phonological segmentation and phoneme comparison. As an outcome of four alternating learning and test fMRI sessions, participants split into successful learners and non-learners. Relative to non-learners, successful learners showed increased task-related activity in a fronto-parietal network of brain areas encompassing the left lateral premotor cortex as well as bilateral superior and inferior parietal cortices during both learning and rule application. These areas were previously associated with phonological segmentation, phoneme comparison and verbal working memory. Based on these activity patterns and the phonological strategies for rule acquisition and application, we argue that successful learning and processing of complex phonological rules in our paradigm is mediated via a fronto-parietal network for phonological processes.

Mechanisms of rule acquisition and rule following in inductive reasoning.

Science.gov (United States)

Crescentini, Cristiano; Seyed-Allaei, Shima; De Pisapia, Nicola; Jovicich, Jorge; Amati, Daniele; Shallice, Tim

2011-05-25

Despite the recent interest in the neuroanatomy of inductive reasoning processes, the regional specificity within prefrontal cortex (PFC) for the different mechanisms involved in induction tasks remains to be determined. In this study, we used fMRI to investigate the contribution of PFC regions to rule acquisition (rule search and rule discovery) and rule following. Twenty-six healthy young adult participants were presented with a series of images of cards, each consisting of a set of circles numbered in sequence with one colored blue. Participants had to predict the position of the blue circle on the next card. The rules that had to be acquired pertained to the relationship among succeeding stimuli. Responses given by subjects were categorized in a series of phases either tapping rule acquisition (responses given up to and including rule discovery) or rule following (correct responses after rule acquisition). Mid-dorsolateral PFC (mid-DLPFC) was active during rule search and remained active until successful rule acquisition. By contrast, rule following was associated with activation in temporal, motor, and medial/anterior prefrontal cortex. Moreover, frontopolar cortex (FPC) was active throughout the rule acquisition and rule following phases before a rule became familiar. We attributed activation in mid-DLPFC to hypothesis generation and in FPC to integration of multiple separate inferences. The present study provides evidence that brain activation during inductive reasoning involves a complex network of frontal processes and that different subregions respond during rule acquisition and rule following phases.

Proposed Rule: Strengthening Transparency in Regulatory Science

Science.gov (United States)

April 24, 2018 proposed rule providing that when EPA develops regulations, including regulations for which the public is likely to bear the cost of compliance, with regard to those scientific studies that are pivotal to the action being taken, EPA should e

Simulation-based learning: Just like the real thing.

Science.gov (United States)

Lateef, Fatimah

2010-10-01

Simulation is a technique for practice and learning that can be applied to many different disciplines and trainees. It is a technique (not a technology) to replace and amplify real experiences with guided ones, often "immersive" in nature, that evoke or replicate substantial aspects of the real world in a fully interactive fashion. Simulation-based learning can be the way to develop health professionals' knowledge, skills, and attitudes, whilst protecting patients from unnecessary risks. Simulation-based medical education can be a platform which provides a valuable tool in learning to mitigate ethical tensions and resolve practical dilemmas. Simulation-based training techniques, tools, and strategies can be applied in designing structured learning experiences, as well as be used as a measurement tool linked to targeted teamwork competencies and learning objectives. It has been widely applied in fields such aviation and the military. In medicine, simulation offers good scope for training of interdisciplinary medical teams. The realistic scenarios and equipment allows for retraining and practice till one can master the procedure or skill. An increasing number of health care institutions and medical schools are now turning to simulation-based learning. Teamwork training conducted in the simulated environment may offer an additive benefit to the traditional didactic instruction, enhance performance, and possibly also help reduce errors.

Learning Display Rules: The Socialization of Emotion Expression in Infancy.

Science.gov (United States)

Malatesta, Carol Zander; Haviland, Jeannette M.

1982-01-01

Develops a methodology for studying emotion socialization and examines the synchrony of mother and infant expressions to determine whether "instruction" in display rules is underway in early infancy and what the short-term effects of such instruction on infant expression might be. Sixty dyads were videotaped during play and reunion after brief…

The Okubo-Zweig-Iizuka rule and unitarity

International Nuclear Information System (INIS)

Ruuskanen, V.; Toernqvist, N.A.

1977-01-01

Using an explicitly unitary framework the Okubo-Zweig-Iizuka (OZI) rule is discussed and in particular how this rule is compatible with unitarity. For the phi-ω case unitarity effects (phi→KantiK→ω) contribute a (nearly) imaginary mixing of 0.6 +- 0.1 % and thus cannot account for the whole mixing of 6-10 %. For the f'-f mixing unitarity effects give a much larger value (>6.8%). In order to understand the small experimental f'→2π coupling the process f'→f→2π must be cancelled by another process e.g. f'→fsup((2))→2π, where fsup((2)) is a heavy f-like meson. For the psions above the first important charm threshold unitarity effects are likely to be crucial. At very high energies cancellations can suppress the unitarity effects. But in a transient energy interval (+p particular between the Dsup(*)antiDsup(*) and the Dsup(*)antiDsup(**) thresholds) these cancellations cannot work everywhere because mass differences are important. Therefore (if charm annihilation amplitudes near threshold are not negligibly small), it is expected that in this interval unitarity effects should be the dominant mechanism for the breaking of the OZI rule. Results from a conventional mass matrix mixing analysis are also given in the appendix. (author)

Phonological reduplication in sign language: rules rule

Directory of Open Access Journals (Sweden)

Iris eBerent

2014-06-01

Full Text Available Productivity—the hallmark of linguistic competence—is typically attributed to algebraic rules that support broad generalizations. Past research on spoken language has documented such generalizations in both adults and infants. But whether algebraic rules form part of the linguistic competence of signers remains unknown. To address this question, here we gauge the generalization afforded by American Sign Language (ASL. As a case study, we examine reduplication (X→XX—a rule that, inter alia, generates ASL nouns from verbs. If signers encode this rule, then they should freely extend it to novel syllables, including ones with features that are unattested in ASL. And since reduplicated disyllables are preferred in ASL, such rule should favor novel reduplicated signs. Novel reduplicated signs should thus be preferred to nonreduplicative controls (in rating, and consequently, such stimuli should also be harder to classify as nonsigns (in the lexical decision task. The results of four experiments support this prediction. These findings suggest that the phonological knowledge of signers includes powerful algebraic rules. The convergence between these conclusions and previous evidence for phonological rules in spoken language suggests that the architecture of the phonological mind is partly amodal.

Adaptive structured dictionary learning for image fusion based on group-sparse-representation

Science.gov (United States)

Yang, Jiajie; Sun, Bin; Luo, Chengwei; Wu, Yuzhong; Xu, Limei

2018-04-01

Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a l1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

Directory of Open Access Journals (Sweden)

Christian Albers

Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

Science.gov (United States)

Albers, Christian; Westkott, Maren; Pawelzik, Klaus

2016-01-01

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

Like father, like son: periventricular nodular heterotopia and nonverbal learning disorder.

Science.gov (United States)

McCann, Marcia V; Pongonis, Stephen J; Golomb, Meredith R; Edwards-Brown, Mary; Christensen, Celanie K; Sokol, Deborah K

2008-08-01

Periventricular nodular heterotopia is a common malformation of cortical development in which the migration of developing neurons destined for the cerebral cortex is abbreviated. Bilateral periventricular nodular heterotopia is most commonly an X-linked disorder that involves mutations in the filamin A (FLNA) gene, but an autosomal recessive form and sporadic forms have been identified. To our knowledge, autosomal dominant transmission of isolated periventricular nodular heterotopia has not been reported. Periventricular nodular heterotopia has a heterogeneous phenotype, associated commonly with seizure disorder, and more recently with reading deficits and visual-spatial deficits in some patients. We present a father and son with bilateral periventricular nodular heterotopia and similar visual-spatial learning deficits, consistent with nonverbal learning disability.

Transient Response Analysis of Metropolis Learning in Games

KAUST Repository

Jaleel, Hassan

2017-10-19

The objective of this work is to provide a qualitative description of the transient properties of stochastic learning dynamics like adaptive play, log-linear learning, and Metropolis learning. The solution concept used in these learning dynamics for potential games is that of stochastic stability, which is based on the stationary distribution of the reversible Markov chain representing the learning process. However, time to converge to a stochastically stable state is exponential in the inverse of noise, which limits the use of stochastic stability as an effective solution concept for these dynamics. We propose a complete solution concept that qualitatively describes the state of the system at all times. The proposed concept is prevalent in control systems literature where a solution to a linear or a non-linear system has two parts, transient response and steady state response. Stochastic stability provides the steady state response of stochastic learning rules. In this work, we study its transient properties. Starting from an initial condition, we identify the subsets of the state space called cycles that have small hitting times and long exit times. Over the long time scales, we provide a description of how the distributions over joint action profiles transition from one cycle to another till it reaches the globally optimal state.

Transient Response Analysis of Metropolis Learning in Games

KAUST Repository

Jaleel, Hassan; Shamma, Jeff S.

2017-01-01

The objective of this work is to provide a qualitative description of the transient properties of stochastic learning dynamics like adaptive play, log-linear learning, and Metropolis learning. The solution concept used in these learning dynamics for potential games is that of stochastic stability, which is based on the stationary distribution of the reversible Markov chain representing the learning process. However, time to converge to a stochastically stable state is exponential in the inverse of noise, which limits the use of stochastic stability as an effective solution concept for these dynamics. We propose a complete solution concept that qualitatively describes the state of the system at all times. The proposed concept is prevalent in control systems literature where a solution to a linear or a non-linear system has two parts, transient response and steady state response. Stochastic stability provides the steady state response of stochastic learning rules. In this work, we study its transient properties. Starting from an initial condition, we identify the subsets of the state space called cycles that have small hitting times and long exit times. Over the long time scales, we provide a description of how the distributions over joint action profiles transition from one cycle to another till it reaches the globally optimal state.

New set of Chemical Safety rules

CERN Multimedia

HSE Unit

2011-01-01

A new set of four Safety Rules was issued on 28 March 2011: Safety Regulation SR-C ver. 2, Chemical Agents (en); General Safety Instruction GSI-C1, Prevention and Protection Measures (en); General Safety Instruction GSI-C2, Explosive Atmospheres (en); General Safety Instruction GSI-C3, Monitoring of Exposure to Hazardous Chemical Agents in Workplace Atmospheres (en). These documents form part of the CERN Safety Rules and are issued in application of the “Staff Rules and Regulations” and of document SAPOCO 42. These documents set out the minimum requirements for the protection of persons from risks to their occupational safety and health arising, or likely to arise, from the effects of hazardous chemical agents that are present in the workplace or used in any CERN activity. Simultaneously, the HSE Unit has published seven Safety Guidelines and six Safety Forms. These documents are available from the dedicated Web page “Chemical, Cryogenic and Biological Safety&...

A generalization of Hamilton’s rule for the evolution of microbial cooperation

OpenAIRE

smith, jeff; Van Dyken, J. David; Zee, Peter

2010-01-01

Hamilton’s rule states that cooperation will evolve if the fitness cost to actors is less than the benefit to recipients multiplied by their genetic relatedness. This rule makes many simplifying assumptions, however, and does not accurately describe social evolution in organisms like microbes where selection is both strong and nonadditive. We derived a generalization of Hamilton’s rule and measured its parameters in Myxococcus xanthus bacteria. Nonadditivity made cooperative sporulation surpr...

Competitive STDP Learning of Overlapping Spatial Patterns.

Science.gov (United States)

Krunglevicius, Dalius

2015-08-01

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron's responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron's forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.

18 CFR 385.104 - Rule of construction (Rule 104).

Science.gov (United States)

2010-04-01

... Definitions § 385.104 Rule of construction (Rule 104). To the extent that the text of a rule is inconsistent with its caption, the text of the rule controls. [Order 376, 49 FR 21705, May 23, 1984] ...

Transfer between local and global processing levels by pigeons (Columba livia) and humans (Homo sapiens) in exemplar- and rule-based categorization tasks.

Science.gov (United States)

Aust, Ulrike; Braunöder, Elisabeth

2015-02-01

The present experiment investigated pigeons' and humans' processing styles-local or global-in an exemplar-based visual categorization task in which category membership of every stimulus had to be learned individually, and in a rule-based task in which category membership was defined by a perceptual rule. Group Intact was trained with the original pictures (providing both intact local and global information), Group Scrambled was trained with scrambled versions of the same pictures (impairing global information), and Group Blurred was trained with blurred versions (impairing local information). Subsequently, all subjects were tested for transfer to the 2 untrained presentation modes. Humans outperformed pigeons regarding learning speed and accuracy as well as transfer performance and showed good learning irrespective of group assignment, whereas the pigeons of Group Blurred needed longer to learn the training tasks than the pigeons of Groups Intact and Scrambled. Also, whereas humans generalized equally well to any novel presentation mode, pigeons' transfer from and to blurred stimuli was impaired. Both species showed faster learning and, for the most part, better transfer in the rule-based than in the exemplar-based task, but there was no evidence of the used processing mode depending on the type of task (exemplar- or rule-based). Whereas pigeons relied on local information throughout, humans did not show a preference for either processing level. Additional tests with grayscale versions of the training stimuli, with versions that were both blurred and scrambled, and with novel instances of the rule-based task confirmed and further extended these findings. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Knowledge Representation and Reasoning in Personalized Web-Based e-Learning Applications

DEFF Research Database (Denmark)

Dolog, Peter

2006-01-01

a user inferred from user interactions with the eLeanrning systems is used to adapt o®ered learning resources and guide a learner through them. This keynote gives an overview about knowledge and rules taken into account in current adaptive eLearning prototypes when adapting learning instructions....... Adaptation is usually based on knowledge about learning esources and users. Rules are used for heuristics to match the learning resources with learners and infer adaptation decisions.......Adaptation that is so natural for teaching by humans is a challenging issue for electronic learning tools. Adaptation in classic teaching is based on observations made about students during teaching. Similar idea was employed in user-adapted (personalized) eLearning applications. Knowledge about...

Simulation-based learning: Just like the real thing

Directory of Open Access Journals (Sweden)

Lateef Fatimah

2010-01-01

Full Text Available Simulation is a technique for practice and learning that can be applied to many different disciplines and trainees. It is a technique (not a technology to replace and amplify real experiences with guided ones, often "immersive" in nature, that evoke or replicate substantial aspects of the real world in a fully interactive fashion. Simulation-based learning can be the way to develop health professionals′ knowledge, skills, and attitudes, whilst protecting patients from unnecessary risks. Simulation-based medical education can be a platform which provides a valuable tool in learning to mitigate ethical tensions and resolve practical dilemmas. Simulation-based training techniques, tools, and strategies can be applied in designing structured learning experiences, as well as be used as a measurement tool linked to targeted teamwork competencies and learning objectives. It has been widely applied in fields such aviation and the military. In medicine, simulation offers good scope for training of interdisciplinary medical teams. The realistic scenarios and equipment allows for retraining and practice till one can master the procedure or skill. An increasing number of health care institutions and medical schools are now turning to simulation-based learning. Teamwork training conducted in the simulated environment may offer an additive benefit to the traditional didactic instruction, enhance performance, and possibly also help reduce errors.

The (kinetic) theory of active particles applied to learning dynamics. Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

Science.gov (United States)

Nieto, J.

2016-03-01

The learning phenomena, their complexity, concepts, structure, suitable theories and models, have been extensively treated in the mathematical literature in the last century, and [4] contains a very good introduction to the literature describing the many approaches and lines of research developed about them. Two main schools have to be pointed out [5] in order to understand the two -not exclusive- kinds of existing models: the stimulus sampling models and the stochastic learning models. Also [6] should be mentioned as a survey where two methods of learning are pointed out, the cognitive and the social, and where the knowledge looks like a mathematical unknown. Finally, as the authors do, we refer to the works [9,10], where the concept of population thinking was introduced and which motivate the game theory rules as a tool (both included in [4] to develop their theory) and [7], where the ideas of developing a mathematical kinetic theory of perception and learning were proposed.

FeynRules - Feynman rules made easy

OpenAIRE

Christensen, Neil D.; Duhr, Claude

2008-01-01

In this paper we present FeynRules, a new Mathematica package that facilitates the implementation of new particle physics models. After the user implements the basic model information (e.g. particle content, parameters and Lagrangian), FeynRules derives the Feynman rules and stores them in a generic form suitable for translation to any Feynman diagram calculation program. The model can then be translated to the format specific to a particular Feynman diagram calculator via F...

Rule-based conversion of closely-related languages: a Dutch-to-Afrikaans convertor

CSIR Research Space (South Africa)

Van Huyssteen, GB

2009-11-01

Full Text Available and performance of a rule-based Dutch-to-Afrikaans converter, with the aim to transform Dutch text so that it looks more like an Afrikaans text (even though it might not even be a good Dutch translation). The rules we used is based on systematic orthographic...

The Rule of Metaphor commented.

Directory of Open Access Journals (Sweden)

Marie-France Begué

2015-04-01

Full Text Available This paper presents the exposure provided by Marie-France Begué to SIPLET (Permanent Interdisciplinary Seminar Literature, Aesthetics and Theology around The Rule of Methaphor of Paul Ricoeur. In it, after a general introduction, are addressed in detail four of the studies in the book: the first, “Between Rhetoric and Poetics: Aristotle,”; the sixth, “The work of the likeness,”; the seventh, “Metaphor and reference”; and the eighth,” Metaphor and philosophical discourse”. The main objective of the paper was to provide an introduction to the thought of Ricoeur in this book, to the seminar participants according to the work they have been doing on the dialogue between poetry and mysticism.Key words: Paul Ricoeur, Rule Methaphor, Theology and Literature, Philosophy of Language.

Geometrical methods in learning theory

International Nuclear Information System (INIS)

Burdet, G.; Combe, Ph.; Nencka, H.

2001-01-01

The methods of information theory provide natural approaches to learning algorithms in the case of stochastic formal neural networks. Most of the classical techniques are based on some extremization principle. A geometrical interpretation of the associated algorithms provides a powerful tool for understanding the learning process and its stability and offers a framework for discussing possible new learning rules. An illustration is given using sequential and parallel learning in the Boltzmann machine

What does good look like? A guide for observing in services for people\\ud with learning disabilities and/or autism

OpenAIRE

Beadle-Brown, Julie; Murphy, Bev; Positive Behaviour Support Academy; Mencap

2016-01-01

This resource provides an overview of what good support looks like in services for people with learning disabilities and/or autism. The definition of “good” is based on both research and good practice and emphasises the nature and quality of the support needed to ensure good quality of life outcomes for people with learning disabilities and/or autism.

Human embryo research and the 14-day rule.

Science.gov (United States)

Pera, Martin F

2017-06-01

In many jurisdictions, restrictions prohibit the culture of human embryos beyond 14 days of development. However, recent reports describing the successful maintenance of embryos in vitro to this stage have prompted many in the field to question whether the rule is still appropriate. This Spotlight article looks at the original rationale behind the 14-day rule and its relevance today in light of advances in human embryo culture and in the derivation of embryonic-like structures from human pluripotent stem cells. © 2017. Published by The Company of Biologists Ltd.

Optimization of decision rules based on dynamic programming approach

KAUST Repository

Zielosko, Beata

2014-01-14

This chapter is devoted to the study of an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure that is the difference between number of rows in a given decision table and the number of rows labeled with the most common decision for this table divided by the number of rows in the decision table. We fix a threshold γ, such that 0 ≤ γ < 1, and study so-called γ-decision rules (approximate decision rules) that localize rows in subtables which uncertainty is at most γ. Presented algorithm constructs a directed acyclic graph Δ γ T which nodes are subtables of the decision table T given by pairs "attribute = value". The algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The chapter contains also results of experiments with decision tables from UCI Machine Learning Repository. © 2014 Springer International Publishing Switzerland.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

Science.gov (United States)

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

Directory of Open Access Journals (Sweden)

Emre O. Neftci

2017-06-01

Full Text Available An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Hebbian learning and predictive mirror neurons for actions, sensations and emotions.

Science.gov (United States)

Keysers, Christian; Gazzola, Valeria

2014-01-01

Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse how mirror neurons become a dynamic system that performs active inferences about the actions of others and allows joint actions despite sensorimotor delays. We explore how this system performs a projection of the self onto others, with egocentric biases to contribute to mind-reading. Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system.

Physiotherapy clinical educators' perceptions and experiences of clinical prediction rules.

Science.gov (United States)

Knox, Grahame M; Snodgrass, Suzanne J; Rivett, Darren A

2015-12-01

Clinical prediction rules (CPRs) are widely used in medicine, but their application to physiotherapy practice is more recent and less widespread, and their implementation in physiotherapy clinical education has not been investigated. This study aimed to determine the experiences and perceptions of physiotherapy clinical educators regarding CPRs, and whether they are teaching CPRs to students on clinical placement. Cross-sectional observational survey using a modified Dillman method. Clinical educators (n=211, response rate 81%) supervising physiotherapy students from 10 universities across 5 states and territories in Australia. Half (48%) of respondents had never heard of CPRs, and a further 25% had never used CPRs. Only 27% reported using CPRs, and of these half (51%) were rarely if ever teaching CPRs to students in the clinical setting. However most respondents (81%) believed CPRs assisted in the development of clinical reasoning skills and few (9%) were opposed to teaching CPRs to students. Users of CPRs were more likely to be male (pphysiotherapy (pStudents are unlikely to be learning about CPRs on clinical placement, as few clinical educators use them. Clinical educators will require training in CPRs and assistance in teaching them if students are to better learn about implementing CPRs in physiotherapy clinical practice. Copyright © 2015 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Neuromorphic Deep Learning Machines

OpenAIRE

Neftci, E; Augustine, C; Paul, S; Detorakis, G

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Back Propagation (BP) rule, often relies on the immediate availability of network-wide...

Use of a Recursive-Rule eXtraction algorithm with J48graft to achieve highly accurate and concise rule extraction from a large breast cancer dataset

Directory of Open Access Journals (Sweden)

Yoichi Hayashi

Full Text Available To assist physicians in the diagnosis of breast cancer and thereby improve survival, a highly accurate computer-aided diagnostic system is necessary. Although various machine learning and data mining approaches have been devised to increase diagnostic accuracy, most current methods are inadequate. The recently developed Recursive-Rule eXtraction (Re-RX algorithm provides a hierarchical, recursive consideration of discrete variables prior to analysis of continuous data, and can generate classification rules that have been trained on the basis of both discrete and continuous attributes. The objective of this study was to extract highly accurate, concise, and interpretable classification rules for diagnosis using the Re-RX algorithm with J48graft, a class for generating a grafted C4.5 decision tree. We used the Wisconsin Breast Cancer Dataset (WBCD. Nine research groups provided 10 kinds of highly accurate concrete classification rules for the WBCD. We compared the accuracy and characteristics of the rule set for the WBCD generated using the Re-RX algorithm with J48graft with five rule sets obtained using 10-fold cross validation (CV. We trained the WBCD using the Re-RX algorithm with J48graft and the average classification accuracies of 10 runs of 10-fold CV for the training and test datasets, the number of extracted rules, and the average number of antecedents for the WBCD. Compared with other rule extraction algorithms, the Re-RX algorithm with J48graft resulted in a lower average number of rules for diagnosing breast cancer, which is a substantial advantage. It also provided the lowest average number of antecedents per rule. These features are expected to greatly aid physicians in making accurate and concise diagnoses for patients with breast cancer. Keywords: Breast cancer diagnosis, Rule extraction, Re-RX algorithm, J48graft, C4.5

Native-likeness in second language lexical categorization reflects individual language history and linguistic community norms.

Science.gov (United States)

Zinszer, Benjamin D; Malt, Barbara C; Ameel, Eef; Li, Ping

2014-01-01

SECOND LANGUAGE LEARNERS FACE A DUAL CHALLENGE IN VOCABULARY LEARNING: First, they must learn new names for the 100s of common objects that they encounter every day. Second, after some time, they discover that these names do not generalize according to the same rules used in their first language. Lexical categories frequently differ between languages (Malt et al., 1999), and successful language learning requires that bilinguals learn not just new words but new patterns for labeling objects. In the present study, Chinese learners of English with varying language histories and resident in two different language settings (Beijing, China and State College, PA, USA) named 67 photographs of common serving dishes (e.g., cups, plates, and bowls) in both Chinese and English. Participants' response patterns were quantified in terms of similarity to the responses of functionally monolingual native speakers of Chinese and English and showed the cross-language convergence previously observed in simultaneous bilinguals (Ameel et al., 2005). For English, bilinguals' names for each individual stimulus were also compared to the dominant name generated by the native speakers for the object. Using two statistical models, we disentangle the effects of several highly interactive variables from bilinguals' language histories and the naming norms of the native speaker community to predict inter-personal and inter-item variation in L2 (English) native-likeness. We find only a modest age of earliest exposure effect on L2 category native-likeness, but importantly, we find that classroom instruction in L2 negatively impacts L2 category native-likeness, even after significant immersion experience. We also identify a significant role of both L1 and L2 norms in bilinguals' L2 picture naming responses.

Native-Likeness in Second Language Lexical Categorization Reflects Individual Language History and Linguistic Community Norms

Directory of Open Access Journals (Sweden)

Benjamin D Zinszer

2014-10-01

Full Text Available Second language learners face a dual challenge in vocabulary learning: First, they must learn new names for the hundreds of common objects that they encounter every day. Second, after some time, they discover that these names do not generalize according to the same rules used in their first language. Lexical categories frequently differ between languages (Malt et al., 1999, and successful language learning requires that bilinguals learn not just new words but new patterns for labeling objects. In the present study, Chinese learners of English with varying language histories and resident in two different language settings (Beijing, China and State College, PA, USA named 67 photographs of common serving dishes (e.g., cups, plates, and bowls in both Chinese and English. Participants’ response patterns were quantified in terms of similarity to the responses of functionally monolingual native speakers of Chinese and English and showed the cross-language convergence previously observed in simultaneous bilinguals (Ameel et al., 2005. For English, bilinguals’ names for each individual stimulus were also compared to the dominant name generated by the native speakers for the object. Using two statistical models, we disentangle the effects of several highly interactive variables from bilinguals' language histories and the naming norms of the native speaker community to predict inter-personal and inter-item variation in L2 (English native-likeness. We find only a modest age of earliest exposure effect on L2 category native-likeness, but importantly, we find that classroom instruction in L2 negatively impacts L2 category native-likeness, even after significant immersion experience. We also identify a significant role of both L1 and L2 norms in bilinguals’ L2 picture naming responses.

What Does Design and Technology Learning Really Look Like?

Science.gov (United States)

Southall, Mary

2016-01-01

This paper presents findings from a research study investigating the relationship between "intended" learning and "actual" learning in Design and Technology lessons (Southall, 2015). The research focused upon the "pre active" phase of the teaching-learning process, that is the teacher's planning processes and…

Ontogeny of collective behavior reveals a simple attraction rule.

Science.gov (United States)

Hinz, Robert C; de Polavieja, Gonzalo G

2017-02-28

The striking patterns of collective animal behavior, including ant trails, bird flocks, and fish schools, can result from local interactions among animals without centralized control. Several of these rules of interaction have been proposed, but it has proven difficult to discriminate which ones are implemented in nature. As a method to better discriminate among interaction rules, we propose to follow the slow birth of a rule of interaction during animal development. Specifically, we followed the development of zebrafish, Danio rerio , and found that larvae turn toward each other from 7 days postfertilization and increase the intensity of interactions until 3 weeks. This developmental dataset allows testing the parameter-free predictions of a simple rule in which animals attract each other part of the time, with attraction defined as turning toward another animal chosen at random. This rule makes each individual likely move to a high density of conspecifics, and moving groups naturally emerge. Development of attraction strength corresponds to an increase in the time spent in attraction behavior. Adults were found to follow the same attraction rule, suggesting a potential significance for adults of other species.

Learning science and science education in a new era.

Science.gov (United States)

Aysan, Erhan

2015-06-01

Today, it takes only a few months for the amount of knowledge to double. The volume of information available has grown so much that it cannot be fully encompassed by the human mind. For this reason, science, learning, and education have to change in the third millennium. The question is thus: what is it that needs to be done? The answer may be found through three basic stages. The first stage is persuading scientists of the necessity to change science education. The second stage is more difficult, in that scientists must be told that they should not place an exaggerated importance on their own academic field and that they should see their field as being on an equal basis with other fields. In the last stage, scientists need to condense the bulk of information on their hands to a manageable size. "Change" is the magic word of our time. Change brings about new rules, and this process happens very quickly in a global world. If we scientists do not rapidly change our scientific learning and education, we will find our students and ourselves caught up in an irreversibly destructive and fatal change that sets its own rules, just like the Arab spring.

Learning science and science education in a new era

Directory of Open Access Journals (Sweden)

Erhan Aysan

2015-06-01

Full Text Available Today, it takes only a few months for the amount of knowledge to double. The volume of information available has grown so much that it cannot be fully encompassed by the human mind. For this reason, science, learning, and education have to change in the third millennium. The question is thus: what is it that needs to be done? The answer may be found through three basic stages. The first stage is persuading scientists of the necessity to change science education. The second stage is more difficult, in that scientists must be told that they should not place an exaggerated importance on their own academic field and that they should see their field as being on an equal basis with other fields. In the last stage, scientists need to condense the bulk of information on their hands to a manageable size. “Change” is the magic word of our time. Change brings about new rules, and this process happens very quickly in a global world. If we scientists do not rapidly change our scientific learning and education, we will find our students and ourselves caught up in an irreversibly destructive and fatal change that sets its own rules, just like the Arab spring.

An agent architecture with on-line learning of both procedural and declarative knowledge

Energy Technology Data Exchange (ETDEWEB)

Sun, R.; Peterson, T.; Merrill, E. [Univ. of Alabama, Tuscaloosa, AL (United States)

1996-12-31

In order to develop versatile cognitive agents that learn in situated contexts and generalize resulting knowledge to different environments, we explore the possibility of learning both declarative and procedural knowledge in a hybrid connectionist architecture. The architecture is based on the two-level idea proposed earlier by the author. Declarative knowledge is represented symbolically, while procedural knowledge is represented subsymbolically. The architecture integrates reactive procedures, rules, learning, and decision-making in a unified framework, and structures different learning components (including Q-learning and rule induction) in a synergistic way to perform on-line and integrated learning.

Learning and Chaining of Motor Primitives for Goal-directed Locomotion of a Snake-Like Robot with Screw-Drive Units

DEFF Research Database (Denmark)

Chatterjee, Sromona; Nachstedt, Timo; Tamosiunaite, Minija

2015-01-01

-directed locomotion for the robot. The behavioural primitives of the robot are generated using a reinforcement learning approach called "Policy Improvement with Path Integrals" (PI2). PI2 is numerically simple and has the ability to deal with high-dimensional systems. Here, PI2 is used to learn the robot’s motor...... controls by finding proper locomotion control parameters, like joint angles and screw-drive unit velocities, in a coordinated manner for different goals. Thus, it is able to generate a large repertoire of motor primitives, which are selectively stored to form a primitive library. The learning process...

Brain signatures of early lexical and morphological learning of a new language.

Science.gov (United States)

Havas, Viktória; Laine, Matti; Rodríguez Fornells, Antoni

2017-07-01

Morphology is an important part of language processing but little is known about how adult second language learners acquire morphological rules. Using a word-picture associative learning task, we have previously shown that a brief exposure to novel words with embedded morphological structure (suffix for natural gender) is enough for language learners to acquire the hidden morphological rule. Here we used this paradigm to study the brain signatures of early morphological learning in a novel language in adults. Behavioural measures indicated successful lexical (word stem) and morphological (gender suffix) learning. A day after the learning phase, event-related brain potentials registered during a recognition memory task revealed enhanced N400 and P600 components for stem and suffix violations, respectively. An additional effect observed with combined suffix and stem violations was an enhancement of an early N2 component, most probably related to conflict-detection processes. Successful morphological learning was also evident in the ERP responses to the subsequent rule-generalization task with new stems, where violation of the morphological rule was associated with an early (250-400ms) and late positivity (750-900ms). Overall, these findings tend to converge with lexical and morphosyntactic violation effects observed in L1 processing, suggesting that even after a short exposure, adult language learners can acquire both novel words and novel morphological rules. Copyright © 2017 Elsevier Ltd. All rights reserved.

New Safety rules

CERN Multimedia

Safety Commission

2008-01-01

The revision of CERN Safety rules is in progress and the following new Safety rules have been issued on 15-04-2008: Safety Procedure SP-R1 Establishing, Updating and Publishing CERN Safety rules: http://cern.ch/safety-rules/SP-R1.htm; Safety Regulation SR-S Smoking at CERN: http://cern.ch/safety-rules/SR-S.htm; Safety Regulation SR-M Mechanical Equipment: http://cern.ch/safety-rules/SR-M.htm; General Safety Instruction GSI-M1 Standard Lifting Equipment: http://cern.ch/safety-rules/GSI-M1.htm; General Safety Instruction GSI-M2 Standard Pressure Equipment: http://cern.ch/safety-rules/GSI-M2.htm; General Safety Instruction GSI-M3 Special Mechanical Equipment: http://cern.ch/safety-rules/GSI-M3.htm. These documents apply to all persons under the Director General’s authority. All Safety rules are available at the web page: http://www.cern.ch/safety-rules The Safety Commission

Ruling Allowing Induced Abortion in Colombia: a Case Study

OpenAIRE

Martinez Orozco, Camilo Eduardo

2007-01-01

The aim of this work is to present and examine the ruling on which the Colombian Constitutional Court declared the blanket criminalization of induced abortion to be unconstitutional: ruling C-355/061; all of this based in the understanding I have achieved of the Courts’ reasoning. In the first section I will present the norms that constituted the blanket prohibition of abortion, as well as the likely situation of its practice, both by the time the Constitutional Court took up the analysis of ...

The drift diffusion model as the choice rule in reinforcement learning.