Optimal association with partly missing key vectors

A new association scheme which can still recall appropriate data when some key elements are missing (blank) is presented. The traditional associative memory models are designed to deal with complete (memorized) keys, but in the real world, key elements are often missing due to error, equipment failure, observation difficulty, etc. The traditional models, in this case, can not have an optimal association except for special cases. When an incomplete key containing blanks is given, we wish to get the same data, as nearly as possible, as would be obtained with the complete key. In this paper, the optimal associative memory model which operates with partly missing keys is proposed. The model is constructed on the basis of the theory of the pseudoinverse of matrices. Even from the incomplete keys which contain a large percentage of blanks, the model recalls the appropriate data optimally under the MSE criterion. From the results of computer simulations, we can show that the model has the expected ability.     

A model of distributed type associative memory with quantized Hadamard transform

This paper proposes a new correlation matrix network model of associative memory in brain. Each memorized pattern which consists of binary (+1 or-1) elements is preprocessed by a quantized Hadamard transform to increase selectivity. The association ability of a correlation matrix network model depends on the orthogonality between key patterns by which the corresponding memorized patterns are associatively recalled. In a brain model, however, it is rare that the key patterns are mutually orthogonal since they are memorized patterns themselves. The quantized Hadamard transform, presented in this paper, renders the memorized patterns approximately orthogonal. The model is tested by computer simulation.     

Learning and recall in a dynamic theory of coordination patterns

A dynamic theory of learning and recall of coordination patterns is developed in the context of relative timing skills. Characterizing the coordination patterns in such skills by the collective variable, relative phase, we choose a model system in which the intrinsic pattern dynamics as well as the influence of environmental and memorized information are well understood from previous experimental and theoretical work. To describe learning we endow memorized information with dynamics which is determined by a phenomenological strategy. Similarly, additional degrees of freedom must be introduced to understand recall. As such recall variables we choose the relative strengths with which each memorized pattern acts on the pattern dynamics and model their dynamics phenomenologically. The resulting dynamical system that resembles models used in pattern recognition theory is shown to adequately describe the learning and recall processes. Moreover, due to the operational character of the theory, several predictions emerge that are open to experimental test. In particular, we show under which conditions phase transitions occur in the dynamics of the coordination patterns during learning and during recall. Considering different time scales and their relations we demonstrate how these phase transitions can be identified and observed. Other predictions include the influence of the intrinsic pattern dynamics on the recall process and the existence of history and hysteresis effects in recall. We discuss different forms of forgetting and differentiation of memorized information. The results show how a new theoretical view of learning and recall as change of behavioral dynamics can lead to a different understanding of these processes by providing testable predictions.     

A model of orthogonal auto-associative networks

Two types of auto-associative networks are well known, one based upon the correlation matrix and the other based upon the orthogonal projection matrix, both of which are calculated from the pattern vectors to be memorized. Although the latter type of networks have a desirable associative property compared to the former ones, they require, in conventional models, nonlocal calculation of the pattern vectors (i.e., pseudoinverse of a matrix) or some learning procedure based on the error correction paradigm. This paper proposes a new model of auto-associative networks in which the orthogonal projection is implemented in a special relation between the connections linking neuron-like elements. The connection weights can be determined by a Hebbian local learning, requiring no pseudoinverse calculation nor the error correction learning.     

干旱半干旱区斑块状植被格局形成模拟研究进展

斑块状植被格局是世界上干旱半干旱区常见的景观类型,它们的形成、组成结构和演替过程研究,对于揭示区域生态系统变化的关键过程具有重要意义。鉴于基于地面调查和遥感技术的方法难以全面刻画斑块状植被格局的形成过程及机制,借助于模型模拟成为解决这一问题的有效方法。自20世纪90年代初至今,斑块状植被格局形成的连续和离散模拟研究不断涌现,然而,连续模拟侧重于植被格局形成的一般机理,缺乏与现实格局的对比和验证,离散模拟单元选择与规则制定等仍需不断研究。在简要回顾斑块状格局形成的反馈机制基础上,重点综述了斑块状植被格局形成的连续和离散模拟的最新研究进展,并指出了现有研究的不足。干旱半干旱区小尺度上植物和水的反馈作用决定了大尺度的斑块状植被格局,充分揭示植被-土壤水分相互作用机理是模型模拟研究的关键,放牧强度和降水格局等外部环境对干旱半干旱区斑块状植被格局特征具有重要影响。在未来研究中,应加强模型模拟结果与实际观测的植被格局比较和验证,重视局域环境条件、生态系统功能在模型中的表达,构建综合连续和离散模型各自优点的混合模型,注重斑块状植被格局形成过程中的标准子模型及模型开发和集成平台的研发,同时强调面向格局...     

Configural Frequency Analysis (CFA) Revisited — a New Look at an Old Approach

Configural frequency analysis (CFA) is a widely used method for the identification of types and syndromes in contingency tables. However, the type model of CFA shows some major deficiencies. In this paper, we propose an alternative modeling of types eliminating the shortcomings of CFA. Basically, a type is modeled as a combination of traits or symptoms that deviates from the pattern of association holding true for the complementary configurations of the contingency table. The new approach is formulated in terms of a log-linear model. It is shown that parameter estimation can be performed with methods known from the analysis of incomplete contingency tables. Test procedures for confirmatory analysis and methods for exploratory search for type configurations are developed. We illustrate the methodology with two practical examples.     

Estimating Haplotype Effects for Survival Data

Summary Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness‐of‐fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF‐receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm.     

Allelic penetrance approach as a tool to model two-locus interaction in complex binary traits

Many binary phenotypes do not follow a classical Mendelian inheritance pattern. Interaction between genetic and environmental factors is thought to contribute to the incomplete penetrance phenomena often observed in these complex binary traits. Several two-locus models for penetrance have been proposed to aid the genetic dissection of binary traits. Such models assume linear genetic effects of both loci in different mathematical scales of penetrance, resembling the analytical framework of quantitative traits. However, changes in phenotypic scale are difficult to envisage in binary traits and limited genetic interpretation is extractable from current modeling of penetrance. To overcome this limitation, we derived an allelic penetrance approach that attributes incomplete penetrance to the stochastic expression of the alleles controlling the phenotype, the genetic background and environmental factors. We applied this approach to formulate dominance and recessiveness in a single diallelic locus and to model different genetic mechanisms for the joint action of two diallelic loci. We fit the models to data on the genetic susceptibility of mice following infections with Listeria monocytogenes and Plasmodium berghei. These models gain in genetic interpretation, because they specify the alleles that are responsible for the genetic (inter)action and their genetic nature (dominant or recessive), and predict genotypic combinations determining the phenotype. Further, we show via computer simulations that the proposed models produce penetrance patterns not captured by traditional two-locus models. This approach provides a new analysis framework for dissecting mechanisms of interlocus joint action in binary traits using genetic crosses.     

Reparameterizing the pattern mixture model for sensitivity analyses under informative dropout

Pattern mixture models are frequently used to analyze longitudinal data where missingness is induced by dropout. For measured responses, it is typical to model the complete data as a mixture of multivariate normal distributions, where mixing is done over the dropout distribution. Fully parameterized pattern mixture models are not identified by incomplete data; Little (1993, Journal of the American Statistical Association 88, 125-134) has characterized several identifying restrictions that can be used for model fitting. We propose a reparameterization of the pattern mixture model that allows investigation of sensitivity to assumptions about nonidentified parameters in both the mean and variance, allows consideration of a wide range of nonignorable missing-data mechanisms, and has intuitive appeal for eliciting plausible missing-data mechanisms. The parameterization makes clear an advantage of pattern mixture models over parametric selection models, namely that the missing-data mechanism can be varied without affecting the marginal distribution of the observed data. To illustrate the utility of the new parameterization, we analyze data from a recent clinical trial of growth hormone for maintaining muscle strength in the elderly. Dropout occurs at a high rate and is potentially informative. We undertake a detailed sensitivity analysis to understand the impact of missing-data assumptions on the inference about the effects of growth hormone on muscle strength.     

A two-part mixed-effects pattern-mixture model to handle zero-inflation and incompleteness in a longitudinal setting

Two-part regression models are frequently used to analyze longitudinal count data with excess zeros, where the same set of subjects is repeatedly observed over time. In this context, several sources of heterogeneity may arise at individual level that affect the observed process. Further, longitudinal studies often suffer from missing values: individuals dropout of the study before its completion, and thus present incomplete data records. In this paper, we propose a finite mixture of hurdle models to face the heterogeneity problem, which is handled by introducing random effects with a discrete distribution; a pattern-mixture approach is specified to deal with non-ignorable missing values. This approach helps us to consider overdispersed counts, while allowing for association between the two parts of the model, and for non-ignorable dropouts. The effectiveness of the proposal is tested through a simulation study. Finally, an application to real data on skin cancer is provided.     

Computational dynamics of gradient bistable networks

We describe a neural-like, homogeneous network consisting of coupled bistable elements and we study its abilities of learning, pattern recognition and computation. The technique allows new possibilities of pattern recognition, including the memorization and perfect recall of several memory patterns, without interference from spurious states. When the coupling strength between elements exceeds a critical value, the network readily converges to a unique attractor. Below this critical value one could perfectly recall all memorized patterns.     

Epigenetic learning in non-neural organisms

Learning involves a usually adaptive response to an input (an external stimulus or the organism℉s own behaviour) in which the input-response relation is memorized; some physical traces of the relation persist and can later be the basis of a more effective response. Using toy models we show that this characterization applies not only to the paradigmatic case of neural learning, but also to cellular responses that are based on epigenetic mechanisms of cell memory. The models suggest that the research agenda of epigenetics needs to be expanded.     

Recollection ability of three-dimensional correlation matrix associative memory

This paper presents the approximate expression of the recollection probability of a three-dimensional correlation matrix autoassociative memory, in which each memorized pattern consists of binary (+1 or-1) elements, and discusses the recollection ability in comparison with that of the conventional two-dimensional correlation matrix associative memory. An associative memory using the correlation properties between memorized patterns desires the condition that the memorized patterns are mutually orthogonal or approximately orthogonal. The three-dimensional correlation matrix associative memory that inscribes the third order correlation of a memorized pattern heightens the recollection ability even though the above condition is not satisfied.     

Mutant mice: Experimental organisms as materialised models in biomedicine

Animal models have received particular attention as key examples of material models. In this paper, we argue that the specificities of establishing animal models—acknowledging their status as living beings and as epistemological tools—necessitate a more complex account of animal models as materialised models. This becomes particularly evident in animal-based models of diseases that only occur in humans: in these cases, the representational relation between animal model and human patient needs to be generated and validated. The first part of this paper presents an account of how disease-specific animal models are established by drawing on the example of transgenic mice models for Alzheimer’s disease. We will introduce an account of validation that involves a three-fold process including (1) from human being to experimental organism; (2) from experimental organism to animal model; and (3) from animal model to human patient. This process draws upon clinical relevance as much as scientific practices and results in disease-specific, yet incomplete, animal models. The second part of this paper argues that the incompleteness of models can be described in terms of multi-level abstractions. We qualify this notion by pointing to different experimental techniques and targets of modelling, which give rise to a plurality of models for a specific disease.     

Evolution of an avian pigmentation gene correlates with a measure of sexual selection

The extravagant plumage traits of male birds are a favourite example of sexual selection. However, to date the units that selection is acting upon, the genes themselves have been a 'black box'. Here, we report evidence of change driven by sexual selection at a pigmentation gene locus in the galliform birds. Across species, we find a correlation between the rate of amino acid change (dN/dS) at this locus (MC1R) and the degree of sexual dichromatism, which we use as a measure of the strength of sexual selection. There is no evidence for a similar pattern in any of five other loci (four candidate and one control locus). This is consistent with previous work on colour polymorphisms and suggests that MC1R may be a key target for selection acting on plumage colour. The pattern of selection at MC1R seems to be consistent with the continuous or cyclical evolution of traits and preferences that is the outcome of several Fisherian and good-genes models of sexual selection. In contrast, we found no support for models of sexual selection that predict an increase in purifying selection as a result of purging of deleterious mutations or for models that predict an increased rate of mutation in association with stronger sexual selection.     

Representations of the human circulatory system

There is no agreement about the robustness of intuitive representations of the circulatory system and their susceptibility to change by instruction. In this paper, we analyse to what extent students with varying degrees of biology instruction and different ages (High School Health Science and Social Science students and first and final year University Psychology students) have been able to change their intuitive beliefs into scientific representations of the workings of the human circulatory system. We analysed two aspects of the representation of the circulatory system: models of the circulatory system (the relationship between the heart and lungs in the blood pathway) and the circulatory system pattern (the blood pathway between organs). In relation to the models of the circulatory system, we found differences among the different groups of students according to the amount of biological instruction they had. The majority of participants (High School Social Science students and both University Psychology student groups) had an incomplete model, which did not include the lungs while the High School Health Science students group had a scientific representation. As for the circulatory system pattern, we did not find differences among groups. However, we cannot conclude that the representations of the two aspects of the circulatory system (model and pattern) are independent. The data in this study show that compulsory human physiology instruction did not enhance the acquisition of a scientific representation of the human circulatory system.     

Primate disease ecology in comparative and theoretical perspective

Infectious disease plays a major role in the lives of wild primates, and the past decade has witnessed significant strides in our understanding of primate disease ecology. In this review, I briefly describe some key findings from phylogenetic comparative approaches, focusing on analyses of parasite richness that use the Global Mammal Parasite Database. While these studies have provided new answers to fundamental questions, new questions have arisen, including questions about the underlying epidemiological mechanisms that produce the broader phylogenetic patterns. I discuss two examples in which theoretical models have given us new traction on these comparative questions. First, drawing on findings of a positive association between range use intensity and the richness of helminth parasites, we developed a spatially explicit agent-based model to investigate the underlying drivers of this pattern. From this model, we are gaining deeper understanding of how range use intensity results in greater exposure to parasites, thus producing higher prevalence in the simulated populations-and, plausibly, higher parasite richness in comparative analyses. Second, I show how a model of disease spread on social networks provides solid theoretical foundations for understanding the effects of sociality and group size on parasitism across primate species. This study further revealed that larger social groups are more subdivided, which should slow the spread of infectious diseases. This effect could offset the increased disease risk expected in larger social groups, which has yet to receive strong empirical support in our comparative analyses. In addition to these examples, I discuss the need for more meta-analyses of individual-level phenomena documented in the field, and for greater linkage between theoretical modeling and field research.