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representational learning

Laura M Getz, Elke R Nordeen, Sarah C Vrabic, Joseph C Toscano
Adult speech perception is generally enhanced when information is provided from multiple modalities. In contrast, infants do not appear to benefit from combining auditory and visual speech information early in development. This is true despite the fact that both modalities are important to speech comprehension even at early stages of language acquisition. How then do listeners learn how to process auditory and visual information as part of a unified signal? In the auditory domain, statistical learning processes provide an excellent mechanism for acquiring phonological categories...
March 21, 2017: Brain Sciences
Sebastian M Frank, Mark W Greenlee, Peter U Tse
Here, we report on the long-term stability of changes in behavior and brain activity following perceptual learning of conjunctions of simple motion features. Participants were trained for 3 weeks on a visual search task involving the detection of a dot moving in a "v"-shaped target trajectory among inverted "v"-shaped distractor trajectories. The first and last training sessions were carried out during functional magnetic resonance imaging (fMRI). Learning stability was again examined behaviorally and using fMRI 3 years after the end of training...
February 23, 2017: Cerebral Cortex
Xinhang Song, Shuqiang Jiang, Luis Herranz
Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, etc). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own cooccurrence patterns that are consistent across the images in that category...
March 22, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Greg Jensen, Yelda Alkan, Fabian Muñoz, Vincent P Ferrera, Herbert S Terrace
Transitive inference (TI) is a classic learning paradigm for which the relative contributions of experienced rewards and representation-based inference have been debated vigorously, particularly regarding the notion that animals are capable of logic and reasoning. Rhesus macaque subjects and human participants performed a TI task in which, prior to learning a 7-item list (ABCDEFG), a block of trials presented exclusively the pair FG. Contrary to the expectation of associative models, the high prior rate of reward for F did not disrupt subsequent learning of the entire list...
March 23, 2017: Journal of Comparative Psychology
Jie Guo, Tingfa Xu, Guokai Shi, Zhitao Rao, Xiangmin Li
In this paper, we propose a multi-view structural local subspace tracking algorithm based on sparse representation. We approximate the optimal state from three views: (1) the template view; (2) the PCA (principal component analysis) basis view; and (3) the target candidate view. Then we propose a unified objective function to integrate these three view problems together. The proposed model not only exploits the intrinsic relationship among target candidates and their local patches, but also takes advantages of both sparse representation and incremental subspace learning...
March 23, 2017: Sensors
Jonathan Lyle Lustgarten, Jeya Balaji Balasubramanian, Shyam Visweswaran, Vanathi Gopalakrishnan
The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules...
March 2017: Data (Basel)
Jin-Hyung Cho, Sam D Rendall, Jesse M Gray
Fos induction during learning labels neuronal ensembles in the hippocampus that encode a specific physical environment, revealing a memory trace. In the cortex and other regions, the extent to which Fos induction during learning reveals specific sensory representations is unknown. Here we generate high-quality brain-wide maps of Fos mRNA expression during auditory fear conditioning and recall in the setting of the home cage. These maps reveal a brain-wide pattern of Fos induction that is remarkably similar among fear conditioning, shock-only, tone-only, and fear recall conditions, casting doubt on the idea that Fos reveals auditory-specific sensory representations...
April 2017: Learning & Memory
Karagh Murphy, Logan S James, Jon T Sakata, Jonathan F Prather
Sensorimotor integration is the process through which the nervous system creates a link between motor commands and associated sensory feedback. This process allows for the acquisition and refinement of many behaviors, including learned communication behaviors like speech and birdsong. Consequently, it is important to understand fundamental mechanisms of sensorimotor integration, and comparative analyses of this process can provide vital insight. Songbirds offer a powerful comparative model system to study how the nervous system links motor and sensory information for learning and control...
March 22, 2017: Journal of Neurophysiology
Benjamin F Grewe, Jan Gründemann, Lacey J Kitch, Jerome A Lecoq, Jones G Parker, Jesse D Marshall, Margaret C Larkin, Pablo E Jercog, Francois Grenier, Jin Zhong Li, Andreas Lüthi, Mark J Schnitzer
The brain's ability to associate different stimuli is vital for long-term memory, but how neural ensembles encode associative memories is unknown. Here we studied how cell ensembles in the basal and lateral amygdala encode associations between conditioned and unconditioned stimuli (CS and US, respectively). Using a miniature fluorescence microscope, we tracked the Ca(2+) dynamics of ensembles of amygdalar neurons during fear learning and extinction over 6 days in behaving mice. Fear conditioning induced both up- and down-regulation of individual cells' CS-evoked responses...
March 22, 2017: Nature
Xiantong Zhen, Mengyang Yu, Feng Zheng, Ilanit Ben Nachum, Mousumi Bhaduri, David Laidley, Shuo Li
Multitarget regression has recently generated intensive popularity due to its ability to simultaneously solve multiple regression tasks with improved performance, while great challenges stem from jointly exploring inter-target correlations and input-output relationships. In this paper, we propose multitarget sparse latent regression (MSLR) to simultaneously model intrinsic intertarget correlations and complex nonlinear input-output relationships in one single framework. By deploying a structure matrix, the MSLR accomplishes a latent variable model which is able to explicitly encode intertarget correlations via ℓ2,1-norm-based sparse learning; the MSLR naturally admits a representer theorem for kernel extension, which enables it to flexibly handle highly complex nonlinear input-output relationships; the MSLR can be solved efficiently by an alternating optimization algorithm with guaranteed convergence, which ensures efficient multitarget regression...
March 16, 2017: IEEE Transactions on Neural Networks and Learning Systems
Lifu Huang, Jonathan May, Xiaoman Pan, Heng Ji, Xiang Ren, Jiawei Han, Lin Zhao, James A Hendler
The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases...
March 2017: Big Data
Katharina Scheiter, Katrin Schleinschok, Shaaron Ainsworth
The goal of this study was to explore two accounts for why sketching during learning from text is helpful: (1) sketching acts like other constructive strategies such as self-explanation because it helps learners to identify relevant information and generate inferences; or (2) that in addition to these general effects, sketching has more specific benefits due to the pictorial representation that is constructed. Seventy-three seventh-graders (32 girls, M = 12.82 years) were first taught how to either create sketches or self-explain while studying science texts...
March 22, 2017: Topics in Cognitive Science
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
Laura J Batterink, Ken A Paller
The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated...
February 24, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
Jing Han, Jiang Yue, Yi Zhang, Lianfa Bai
Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images...
2017: PloS One
Angeliki Lazaridou, Marco Marelli, Marco Baroni
By the time they reach early adulthood, English speakers are familiar with the meaning of thousands of words. In the last decades, computational simulations known as distributional semantic models (DSMs) have demonstrated that it is possible to induce word meaning representations solely from word co-occurrence statistics extracted from a large amount of text. However, while these models learn in batch mode from large corpora, human word learning proceeds incrementally after minimal exposure to new words. In this study, we run a set of experiments investigating whether minimal distributional evidence from very short passages suffices to trigger successful word learning in subjects, testing their linguistic and visual intuitions about the concepts associated with new words...
March 21, 2017: Cognitive Science
Arndt R Finkelmann, Andreas H Göller, Gisbert Schneider
Machine learning models for site of metabolism (SoM) prediction offer the ability to identify metabolic soft spots in low molecular weight drug molecules at low computational cost and enable data-based reactivity prediction. SoM prediction is an atom classification problem. Successful construction of machine learning models requires atom representations that capture the reactivity-determining features of a potential reaction site. We have developed a descriptor scheme that characterizes an atom's steric and electronic environment and its relative location in the molecular structure...
March 21, 2017: ChemMedChem
Mahdi Tabassian, Martino Alessandrini, Lieven Herbots, Oana Mirea, Efstathios D Pagourelias, Ruta Jasaityte, Jan Engvall, Luca De Marchi, Guido Masetti, Jan D'hooge
The aim of this study was to analyze the whole temporal profiles of the segmental deformation curves of the left ventricle (LV) and describe their interrelations to obtain more detailed information concerning global LV function in order to be able to identify abnormal changes in LV mechanics. The temporal characteristics of the segmental LV deformation curves were compactly described using an efficient decomposition into major patterns of variation through a statistical method, called Principal Component Analysis (PCA)...
March 20, 2017: International Journal of Cardiovascular Imaging
Ryan E Harvey, Shannon M Thompson, Lilliana M Sanchez, Ryan M Yoder, Benjamin J Clark
The limbic thalamus, specifically the anterior thalamic nuclei (ATN), contains brain signals including that of head direction cells, which fire as a function of an animal's directional orientation in an environment. Recent work has suggested that this directional orientation information stemming from the ATN contributes to the generation of hippocampal and parahippocampal spatial representations, and may contribute to the establishment of unique spatial representations in radially oriented tasks such as the radial arm maze...
2017: Frontiers in Neuroscience
Mark J Wagner, Tony Hyun Kim, Joan Savall, Mark J Schnitzer, Liqun Luo
The human brain contains approximately 60 billion cerebellar granule cells, which outnumber all other brain neurons combined. Classical theories posit that a large, diverse population of granule cells allows for highly detailed representations of sensorimotor context, enabling downstream Purkinje cells to sense fine contextual changes. Although evidence suggests a role for the cerebellum in cognition, granule cells are known to encode only sensory and motor context. Here, using two-photon calcium imaging in behaving mice, we show that granule cells convey information about the expectation of reward...
March 20, 2017: Nature
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