Cognitive Tomography Reveals Complex, Task-Independent Mental Representations

Humans develop rich mental representations that guide their behavior in a variety of everyday tasks. However, it is unknown whether these representations, often formalized as priors in Bayesian inference, are specific for each task or subserve multiple tasks. Current approaches cannot distinguish between these two possibilities because they cannot extract comparable representations across different tasks [1-10]. Here, we develop a novel method, termed cognitive tomography, that can extract complex, multidimensional priors across tasks. We apply this method to human judgments in two qualitatively different tasks, "familiarity" and "odd one out," involving an ecologically relevant set of stimuli, human faces. We show that priors over faces are structurally complex and vary dramatically across subjects, but are invariant across the tasks within each subject. The priors we extract from each task allow us to predict with high precision the behavior of subjects for novel stimuli both in the same task as well as in the other task. Our results provide the first evidence for a single high-dimensional structured representation of a naturalistic stimulus set that guides behavior in multiple tasks. Moreover, the representations estimated by cognitive tomography can provide independent, behavior-based regressors for elucidating the neural correlates of complex naturalistic priors. © 2013 The Authors.

What is the weakest topology in which feedback stability is robust?

Mathematical theorems in control theory are only of interest in so far as their assumptions relate to practical situations. The space of systems with transfer functions in ℋ∞, for example, has many advantages mathematically, but includes large classes of non-physical systems, and one must be careful in drawing inferences from results in that setting. Similarly, the graph topology has long been known to be the weakest, or coarsest, topology in which (1) feedback stability is a robust property (i.e. preserved in small neighbourhoods) and (2) the map from open-to-closed-loop transfer functions is continuous. However, it is not known whether continuity is a necessary part of this statement, or only required for the existing proofs. It is entirely possible that the answer depends on the underlying classes of systems used. The class of systems we concern ourselves with here is the set of systems that can be approximated, in the graph topology, by real rational transfer function matrices. That is, lumped parameter models, or those distributed systems for which it makes sense to use finite element methods. This is precisely the set of systems that have continuous frequency responses in the extended complex plane. For this class, we show that there is indeed a weaker topology; in which feedback stability is robust but for which the maps from open-to-closed-loop transfer functions are not necessarily continuous. © 2013 Copyright Taylor and Francis Group, LLC.

Shock amplification, curve veering and the role of damping

The circumstances are investigated under which high peak acceleration can occur in the internal parts of a system when subjected to impulsive driving on the outside. Previous work using a coupled beam model has highlighted the importance of veering pairs of modes. Such a veering pair can be approximated by a lumped system with two degrees of freedom. The worst case of acceleration amplification is shown to occur when the two oscillators are tuned to the same frequency, and for this case closed-form expressions are derived to show the parameter dependence of the acceleration ratio on the mass ratio and coupling strength. Sensitivity analysis of the eigenvalues and eigenvectors indicates that mass ratio is the most sensitive parameter for altering the veering behaviour in an undamped system. Non-proportional damping is also shown to have a strong influence on the veering behaviour. The study gives design guidelines to allow permissible acceleration levels to be achieved by the choice of the effective mass and damping of the indirectly driven subsystem relative to the directly driven subsystem. © 2013 Elsevier Ltd.

Systematic misregistration and the statistical analysis of surface data.

Spatial normalisation is a key element of statistical parametric mapping and related techniques for analysing cohort statistics on voxel arrays and surfaces. The normalisation process involves aligning each individual specimen to a template using some sort of registration algorithm. Any misregistration will result in data being mapped onto the template at the wrong location. At best, this will introduce spatial imprecision into the subsequent statistical analysis. At worst, when the misregistration varies systematically with a covariate of interest, it may lead to false statistical inference. Since misregistration generally depends on the specimen's shape, we investigate here the effect of allowing for shape as a confound in the statistical analysis, with shape represented by the dominant modes of variation observed in the cohort. In a series of experiments on synthetic surface data, we demonstrate how allowing for shape can reveal true effects that were previously masked by systematic misregistration, and also guard against misinterpreting systematic misregistration as a true effect. We introduce some heuristics for disentangling misregistration effects from true effects, and demonstrate the approach's practical utility in a case study of the cortical bone distribution in 268 human femurs.

Comparative phylogeography of the Yellow River schizothoracine fishes (Cyprinidae): Vicariance, expansion, and recent coalescence in response to the Quaternary environmental upheaval in the Tibetan Plateau

National Natural Science Foundation of China (NSFC) [30225008, 30300036, 30530120]; Key Innovation Plan [KSCX2-SW-106]; National Basic Research Project in China [2005cb422005]; National Natural Science Foundation of China [30600062]

Electrostatic force microscopy and potentiometry of realistic nanostructured systems

We investigate the dependency of electrostatic interaction forces on applied potentials in electrostatic force microscopy (EFM) as well as in related local potentiometry techniques such as Kelvin probe microscopy (KPM). The approximated expression of electrostatic interaction between two conductors, usually employed in EFM and KPM, may loose its validity when probe-sample distance is not very small, as often realized when realistic nanostructured systems with complex topography are investigated. In such conditions, electrostatic interaction does not depend solely on the potential difference between probe and sample, but instead it may depend on the bias applied to each conductor. For instance, electrostatic force can change from repulsive to attractive for certain ranges of applied potentials and probe-sample distances, and this fact cannot be accounted for by approximated models. We propose a general capacitance model, even applicable to more than two conductors, considering values of potentials applied to each of the conductors to determine the resulting forces and force gradients, being able to account for the above phenomenon as well as to describe interactions at larger distances. Results from numerical simulations and experiments on metal stripe electrodes and semiconductor nanowires supporting such scenario in typical regimes of EFM investigations are presented, evidencing the importance of a more rigorous modeling for EFM data interpretation. Furthermore, physical meaning of Kelvin potential as used in KPM applications can also be clarified by means of the reported formalism. © 2009 American Institute of Physics.

Bayesian parameter identification for vibro-acoustic models with nonparametric uncertainty

Vibration and acoustic analysis at higher frequencies faces two challenges: computing the response without using an excessive number of degrees of freedom, and quantifying its uncertainty due to small spatial variations in geometry, material properties and boundary conditions. Efficient models make use of the observation that when the response of a decoupled vibro-acoustic subsystem is sufficiently sensitive to uncertainty in such spatial variations, the local statistics of its natural frequencies and mode shapes saturate to universal probability distributions. This holds irrespective of the causes that underly these spatial variations and thus leads to a nonparametric description of uncertainty. This work deals with the identification of uncertain parameters in such models by using experimental data. One of the difficulties is that both experimental errors and modeling errors, due to the nonparametric uncertainty that is inherent to the model type, are present. This is tackled by employing a Bayesian inference strategy. The prior probability distribution of the uncertain parameters is constructed using the maximum entropy principle. The likelihood function that is subsequently computed takes the experimental information, the experimental errors and the modeling errors into account. The posterior probability distribution, which is computed with the Markov Chain Monte Carlo method, provides a full uncertainty quantification of the identified parameters, and indicates how well their uncertainty is reduced, with respect to the prior information, by the experimental data. © 2013 Taylor & Francis Group, London.

Molecular evidence for diphyly of the Asian catfish family Amblycipitidae (Teleostei: Siluriformes) and exclusion of the South American Aspredinidae from Sisoroidea

Amblycipitidae Day, 1873 is an Asian family of catfishes (Siluriformes) usually considered to contain 28 species placed in three genera: Amblyceps (14 spp.), Liobagrus (12 spp.) and Xiurenbagrus (2 spp.). Morphology-based systematics has supported the monophyly of this family, with some authors placing Amblycipitidae within a larger group including Akysidae, Sisoridae and Aspredinidae, termed the Sisoroidea. Here we investigate the phylogenetic relationships among four species of Amblyceps, six species of Liobagrus and the two species of Xiurenbagrus with respect to other sisoroid taxa as well as other catfish groups using 6100 aligned base pairs of DNA sequence data from the rag1 and rag2 genes of the nuclear genome and from three regions (cyt b, COL ND4 plus tRNA-His and tRNA-Ser) of the mitochondrial genome. Parsimony and Bayesian analyses of the data indicate strong support for a diphyletic Amblycipitidae in which the genus Amblyceps is the sister group to the Sisoridae and a clade formed by genera Liobagrus and Xiurenbagrus is the sister group to Akysidae. These taxa together form a well supported monophyletic group that assembles all Asian sisoroid taxa, but excludes the South American Aspredinidae. Results for aspredinids are consistent with previous molecular studies that indicate these catfishes are not sisoroids, but the sister group to the South American doradoid catfishes (Auchenipteridae + Doradidae). The redefined sisoroid clade plus Bagridae, Horabagridae and (Ailia + Laides) make up a larger monophyletic group informally termed "Big Asia." Likelihood-based SH tests and Bayes Factor comparisons of the rag and the mitochondrial data partitions considered separately and combined reject both the hypothesis of amblycipitid monophyly and the hypothesis of aspredinid inclusion within Sisoroidea. This result for amblycipitids conflicts with a number of well documented morphological synapomorphies that we briefly review. Possible nomenclatural changes for amblycipitid taxa are noted.

The Random Forest Kernel and other kernels for big data from random partitions

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.

Gaussian process volatility model

The prediction of time-changing variances is an important task in the modeling of financial data. Standard econometric models are often limited as they assume rigid functional relationships for the evolution of the variance. Moreover, functional parameters are usually learned by maximum likelihood, which can lead to over-fitting. To address these problems we introduce GP-Vol, a novel non-parametric model for time-changing variances based on Gaussian Processes. This new model can capture highly flexible functional relationships for the variances. Furthermore, we introduce a new online algorithm for fast inference in GP-Vol. This method is much faster than current offline inference procedures and it avoids overfitting problems by following a fully Bayesian approach. Experiments with financial data show that GP-Vol performs significantly better than current standard alternatives.

Variation patterns of the mitochondrial 16S rRNA gene with secondary structure constraints and their application to phylogeny of cyprinine fishes (Teleostei : Cypriniformes)

The mitochondrial 16S ribosomal RNA (rRNA) gene sequences from 93 cyprinid fishes were examined to reconstruct the phylogenetic relationships within the diverse and economically important subfamily Cyprininae. Within the subfamily a biased nucleotide composition (A > T, C > G) was observed in the loop regions of the gene, and in stem regions apparent selective pressures of base pairing showed a bias in favor of G over C and T over A. The bias may be associated with transition-transversion bias. Rates of nucleotide substitution were lower in stems than in loops. Analysis of compensatory substitutions across these taxa demonstrates 68% covariation in the gene and a logical weighting factor to account for dependence in mutations for phylogenetic inference should be 0.66. Comparisons of varied stem-loop weighting schemes indicate that the down-weightings for stem regions could improve the phylogenetic analysis and the degree of non-independence of stem substitutions was not as important as expected. Bayesian inference under four models of nucleotide substitution indicated that likelihood-based phylogenetic analyses were more effective in improving the phylogenetic performance than was weighted parsimony analysis. In Bayesian analyses, the resolution of phylogenies under the 16-state models for paired regions, incorporating GTR + G + I models for unpaired regions was better than those under other models. The subfamily Cyprininae was resolved as a monophyletic group, as well as tribe Labein and several genera. However, the monophyly of the currently recognized tribes, such as Schizothoracin, Barbin, Cyprinion + Onychostoma lineages, and some genera was rejected. Furthermore, comparisons of the parsimony and Bayesian analyses and results of variable length bootstrap analysis indicates that the mitochondrial 16S rRNA gene should contain important character variation to recover well-supported phylogeny of cyprinid taxa whose divergences occurred within the recent 8 MY, but could not provide resolution power for deep phylogenies spanning 10-19 MYA. (c) 2008 Published by Elsevier Inc.

Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3.

A venerable history of classical work on autoassociative memory has significantly shaped our understanding of several features of the hippocampus, and most prominently of its CA3 area, in relation to memory storage and retrieval. However, existing theories of hippocampal memory processing ignore a key biological constraint affecting memory storage in neural circuits: the bounded dynamical range of synapses. Recent treatments based on the notion of metaplasticity provide a powerful model for individual bounded synapses; however, their implications for the ability of the hippocampus to retrieve memories well and the dynamics of neurons associated with that retrieval are both unknown. Here, we develop a theoretical framework for memory storage and recall with bounded synapses. We formulate the recall of a previously stored pattern from a noisy recall cue and limited-capacity (and therefore lossy) synapses as a probabilistic inference problem, and derive neural dynamics that implement approximate inference algorithms to solve this problem efficiently. In particular, for binary synapses with metaplastic states, we demonstrate for the first time that memories can be efficiently read out with biologically plausible network dynamics that are completely constrained by the synaptic plasticity rule, and the statistics of the stored patterns and of the recall cue. Our theory organises into a coherent framework a wide range of existing data about the regulation of excitability, feedback inhibition, and network oscillations in area CA3, and makes novel and directly testable predictions that can guide future experiments.

A reversible infinite HMM using normalised random measures

Copyright 2014 by the author(s). We present a nonparametric prior over reversible Markov chains. We use completely random measures, specifically gamma processes, to construct a countably infinite graph with weighted edges. By enforcing symmetry to make the edges undirected we define a prior over random walks on graphs that results in a reversible Markov chain. The resulting prior over infinite transition matrices is closely related to the hierarchical Dirichlet process but enforces reversibility. A reinforcement scheme has recently been proposed with similar properties, but the de Finetti measure is not well characterised. We take the alternative approach of explicitly constructing the mixing measure, which allows more straightforward and efficient inference at the cost of no longer having a closed form predictive distribution. We use our process to construct a reversible infinite HMM which we apply to two real datasets, one from epigenomics and one ion channel recording.

Phylogeny of freshwater parasitic copepods in the Ergasilidae (Copepoda : Poecilostomatoida) based on 18S and 28S rDNA sequences

The phylogenetic relationships among the Ergasilidae genera are poorly understood. In this study, 14 species from four genera in the Ergasilidae including Sinergasilus, Ergasilus, Pseudergasilus, and Paraergasilus were collected in China, and their phylogenetic relationships were examined using neighbor-joining, maximum parsimony, maximum likelihood, and Bayesian inference methods based on partial sequences of 18S and 28S ribosomal deoxyribonucleic acid, respectively. All the analyses suggest that the Sinergasilus and Paraergasilus are both monophyletic, but the Ergasilus is polyphyletic rather than monophyletic. Considering the relationships among the four genera, the phylogenetic analyses and subsequent hypothesis tests all suggest that Pseudergasilus clustered with some Ergasilus species may have a closer relationship with Sinergasilus rather than with Paraergasilus. It is proposed that the Sinergasilus and the Pseudergasilus species might have evolved from Ergasilus species.

Phylogenetic relationships of Danio within the order cypriniformes: A framework for comparative and evolutionary studies of a model species

The evolutionary relationships of species of Danio and the monophyly and phylogenetic placement of the genus within the family Cyprinidae and subfamily Rasborinae provide fundamentally important phyloinformatics necessary for direct evaluations of an array of pertinent questions in modern comparative biology. Although the genus Danio is not one of the most diverse within the family, Danio rerio is one of the most important model species in biology. Many investigations have used this species or presumed close relatives to address specific questions that have lasting impact on the hypothesis and theory of development in vertebrates. Largely lacking from this approach has been a holistic picture of the exact phylogenetic or evolutionary relationships of this species and its close relatives. One thing that has been learned over the previous century is that many organismal attributes (e.g., developmental pathways, ecologies, behaviors, speciation) are historically constrained and their origins and functions are best explained via a phylogenetic approach. Herein, we provide a molecular evaluation of the phylogenetic placement of the model species Danio rerio within the genus Danio and among hypothesized closely related species and genera. Our analysis is derived from data using two nuclear genes (RAG1, rhodopsin) and five mitochondrial genes (ND4, ND4L, ND5, COI, cyt b) evaluated using parsimony, maximum likelihood, and Bayesian analyses. The family Cyprinidae is resolved as monophyletic but the subfamily Rasborinae (priority over Danioinae) is an unnatural assemblage. Danio is identified as a monophyletic group sister to a clade inclusive of the genera Chela, Microrasbora, Devario, and Inlecypris, not Devario nor Esomus as hypothesized in previous studies. Danio rerio is sister to D. kyathit among the species of Danio evaluated in this analysis. Microrasbora and Rasbora are non-monophyletic assemblages; however, Boraras is monophyletic.