An integrated neuro-mechanical model of C. elegans forward locomotion

One of the most tractable organisms for the study of nervous systems is the nematode Caenorhabditis elegans, whose locomotion in particular has been the subject of a number of models. In this paper we present a first integrated neuro-mechanical model of forward locomotion. We find that a previous neural model is robust to the addition of a body with mechanical properties, and that the integrated model produces oscillations with a more realistic frequency and waveform than the neural model alone. We conclude that the body and environment are likely to be important components of the worm’s locomotion subsystem

An integrated neuro-mechanical model of C. elegans forward locomotion

One of the most tractable organisms for the study of nervous systems is the nematode Caenorhabditis elegans, whose locomotion in particular has been the subject of a number of models. In this paper we present a first integrated neuro-mechanical model of forward locomotion. We find that a previous neural model is robust to the addition of a body with mechanical properties, and that the integrated model produces oscillations with a more realistic frequency and waveform than the neural model alone. We conclude that the body and environment are likely to be important components of the worm’s locomotion subsystem

Support Vector Machine Histogram: New Analysis and Architecture Design Method of Deep Convolutional Neural Network

Deep convolutional neural network (DCNN) is a kind of hierarchical neural network models and attracts attention in recent years since it has shown high classification performance. DCNN can acquire the feature representation which is a parameter indicating the feature of the input by learning. However, its internal analysis and the design of the network architecture have many unclear points and it cannot be said that it has been sufficiently elucidated. We propose the novel DCNN analysis method “Support vector machine (SVM) histogram” as a prescription to deal with these problems. This is a method that examines the spatial distribution of DCNN extracted feature representation by using the decision boundary of linear SVM. We show that we can interpret DCNN hierarchical processing using this method. In addition, by using the result of SVM histogram, DCNN architecture design becomes possible. In this study, we designed the architecture of the application to large scale natural image dataset. In the result, we succeeded in showing higher accuracy than the original DCNN

Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations

Efficient inference for genetic association studies with multiple outcomes

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modelling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson et al. (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes

Methodologies for time series prediction and missing value imputation

The amount of collected data is increasing all the time in the world. More sophisticated measuring instruments and increase in the computer processing power produce more and more data, which requires more capacity from the collection, transmission and storage. Even though computers are faster, large databases need also good and accurate methodologies for them to be useful in practice. Some techniques are not feasible to be applied to very large databases or are not able to provide the necessary accuracy. As the title proclaims, this thesis focuses on two aspects encountered with databases, time series prediction and missing value imputation. The first one is a function approximation and regression problem, but can, in some cases, be formulated also as a classification task. Accurate prediction of future values is heavily dependent not only on a good model, which is well trained and validated, but also preprocessing, input variable selection or projection and output approximation strategy selection. The importance of all these choices made in the approximation process increases when the prediction horizon is extended further into the future. The second focus area deals with missing values in a database. The missing values can be a nuisance, but can be also be a prohibiting factor in the use of certain methodologies and degrade the performance of others. Hence, missing value imputation is a very necessary part of the preprocessing of a database. This imputation has to be done carefully in order to retain the integrity of the database and not to insert any unwanted artifacts to aggravate the job of the final data analysis methodology. Furthermore, even though the accuracy is always the main requisite for a good methodology, computational time has to be considered alongside the precision. In this thesis, a large variety of different strategies for output approximation and variable processing for time series prediction are presented. There is also a detailed presentation of new methodologies and tools for solving the problem of missing values. The strategies and methodologies are compared against the state-of-the-art ones and shown to be accurate and useful in practice.Maailmassa tuotetaan koko ajan enemmän ja enemmän tietoa. Kehittyneemmät mittalaitteet, nopeammat tietokoneet sekä kasvaneet siirto- ja tallennuskapasiteetit mahdollistavat suurien tietomassojen keräämisen, siirtämisen ja varastoinnin. Vaikka tietokoneiden laskentateho kasvaa jatkuvasti, suurten tietoaineistojen käsittelyssä tarvitaan edelleen hyviä ja tarkkoja menetelmiä. Kaikki menetelmät eivät sovellu valtavien aineistojen käsittelyyn tai eivät tuota tarpeeksi tarkkoja tuloksia. Tässä työssä keskitytään kahteen tärkeään osa-alueeseen tietokantojen käsittelyssä: aikasarjaennustamiseen ja puuttuvien arvojen täydentämiseen. Ensimmäinen näistä alueista on regressio-ongelma, jossa pyritään arvioimaan aikasarjan tulevaisuutta edeltävien näytteiden pohjalta. Joissain tapauksissa regressio-ongelma voidaan muotoilla myös luokitteluongelmaksi. Tarkka aikasarjan ennustaminen on riippuvainen hyvästä ja luotettavasta ennustusmallista. Malli on opetettava oikein ja sen oikeellisuus ja tarkkuus on varmistettava. Lisäksi aikasarjan esikäsittely, syötemuuttujien valinta- tai projektiotapa sekä ennustusstrategia täytyy valita huolella ja niiden soveltuvuus mallin yhteyteen on varmistettava huolellisesti. Tehtyjen valintojen tärkeys kasvaa entisestään mitä pidemmälle tulevaisuuteen ennustetaan. Toinen tämän työn osa-alue käsittelee puuttuvien arvojen ongelmaa. Tietokannasta puuttuvat arvot voivat heikentää data-analyysimenetelmän tuottamia tuloksia tai jopa estää joidenkin menetelmien käytön, joten puuttuvien arvojen arviointi ja täydentäminen esikäsittelyn osana on suositeltavaa. Täydentäminen on kuitenkin tehtävä harkiten, sillä puutteellinen täydentäminen johtaa hyvin todennäköisesti epätarkkuuksiin lopullisessa käyttökohteessa ja ei-toivottuihin rakenteisiin tietokannan sisällä. Koska kyseessä on esikäsittely, eikä varsinainen datan hyötykäyttö, puuttuvien arvojen täydentämiseen käytetty laskenta-aika tulisi minimoida säilyttäen laskentatarkkuus. Tässä väitöskirjassa on esitelty erilaisia tapoja ennustaa pitkän ajan päähän tulevaisuuteen ja keinoja syötemuuttujien valintaan. Lisäksi uusia menetelmiä puuttuvien arvojen täydentämiseen on kehitetty ja niitä on vertailtu olemassa oleviin menetelmiin

Inferring relevance from eye movements with wrong models

Statistical inference forms the backbone of modern science. It is often viewed as giving an objective validation for hypotheses or models. Perhaps for this reason the theory of statistical inference is often derived with the assumption that the "truth" is within the model family. However, in many real-world applications the applied statistical models are incorrect. A more appropriate probabilistic model may be computationally too complex, or the problem to be modelled may be so new that there is little prior information to be incorporated. However, in statistical theory the theoretical and practical implications of the incorrectness of the model family are to a large extent unexplored. This thesis focusses on conditional statistical inference, that is, modeling of classes of future observations given observed data, under the assumption that the model is incorrect. Conditional inference or prediction is one of the main application areas of statistical models which is still lacking a conclusive theoretical justification of Bayesian inference. The main result of the thesis is an axiomatic derivation where, given an incorrect model and assuming that the utility is conditional likelihood, a discriminative posterior yields a distribution on model parameters which best agrees with the utility. The devised discriminative posterior outperforms the classical Bayesian joint likelihood-based approach in conditional inference. Additionally, a theoretically justified expectation maximization-type algorithm is presented for obtaining conditional maximum likelihood point estimates for conditional inference tasks. The convergence of the algorithm is shown to be more stable than in earlier partly heuristic variants. The practical application field of the thesis is inference of relevance from eye movement signals in an information retrieval setup. It is shown that relevance can be predicted to some extent, and that this information can be exploited in a new kind of task, proactive information retrieval. Besides making it possible to design new kinds of engineering applications, statistical modeling of eye tracking data can also be applied in basic psychological research to make hypotheses of cognitive processes affecting eye movements, which is the second application area of the thesis

A perceptual learning model to discover the hierarchical latent structure of image collections

Biology has been an unparalleled source of inspiration for the work of researchers in several scientific and engineering fields including computer vision. The starting point of this thesis is the neurophysiological properties of the human early visual system, in particular, the cortical mechanism that mediates learning by exploiting information about stimuli repetition. Repetition has long been considered a fundamental correlate of skill acquisition andmemory formation in biological aswell as computational learning models. However, recent studies have shown that biological neural networks have differentways of exploiting repetition in forming memory maps. The thesis focuses on a perceptual learning mechanism called repetition suppression, which exploits the temporal distribution of neural activations to drive an efficient neural allocation for a set of stimuli. This explores the neurophysiological hypothesis that repetition suppression serves as an unsupervised perceptual learning mechanism that can drive efficient memory formation by reducing the overall size of stimuli representation while strengthening the responses of the most selective neurons. This interpretation of repetition is different from its traditional role in computational learning models mainly to induce convergence and reach training stability, without using this information to provide focus for the neural representations of the data. The first part of the thesis introduces a novel computational model with repetition suppression, which forms an unsupervised competitive systemtermed CoRe, for Competitive Repetition-suppression learning. The model is applied to generalproblems in the fields of computational intelligence and machine learning. Particular emphasis is placed on validating the model as an effective tool for the unsupervised exploration of bio-medical data. In particular, it is shown that the repetition suppression mechanism efficiently addresses the issues of automatically estimating the number of clusters within the data, as well as filtering noise and irrelevant input components in highly dimensional data, e.g. gene expression levels from DNA Microarrays. The CoRe model produces relevance estimates for the each covariate which is useful, for instance, to discover the best discriminating bio-markers. The description of the model includes a theoretical analysis using Huber’s robust statistics to show that the model is robust to outliers and noise in the data. The convergence properties of themodel also studied. It is shown that, besides its biological underpinning, the CoRe model has useful properties in terms of asymptotic behavior. By exploiting a kernel-based formulation for the CoRe learning error, a theoretically sound motivation is provided for the model’s ability to avoid local minima of its loss function. To do this a necessary and sufficient condition for global error minimization in vector quantization is generalized by extending it to distance metrics in generic Hilbert spaces. This leads to the derivation of a family of kernel-based algorithms that address the local minima issue of unsupervised vector quantization in a principled way. The experimental results show that the algorithm can achieve a consistent performance gain compared with state-of-the-art learning vector quantizers, while retaining a lower computational complexity (linear with respect to the dataset size). Bridging the gap between the low level representation of the visual content and the underlying high-level semantics is a major research issue of current interest. The second part of the thesis focuses on this problem by introducing a hierarchical and multi-resolution approach to visual content understanding. On a spatial level, CoRe learning is used to pool together the local visual patches by organizing them into perceptually meaningful intermediate structures. On the semantical level, it provides an extension of the probabilistic Latent Semantic Analysis (pLSA) model that allows discovery and organization of the visual topics into a hierarchy of aspects. The proposed hierarchical pLSA model is shown to effectively address the unsupervised discovery of relevant visual classes from pictorial collections, at the same time learning to segment the image regions containing the discovered classes. Furthermore, by drawing on a recent pLSA-based image annotation system, the hierarchical pLSA model is extended to process and representmulti-modal collections comprising textual and visual data. The results of the experimental evaluation show that the proposed model learns to attach textual labels (available only at the level of the whole image) to the discovered image regions, while increasing the precision/ recall performance with respect to flat, pLSA annotation model