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Spaced learning

Kan Li, José C Príncipe
This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning...
2018: Frontiers in Neuroscience
Fang Ren, Logan Ward, Travis Williams, Kevin J Laws, Christopher Wolverton, Jason Hattrick-Simpers, Apurva Mehta
With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method-dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary...
April 2018: Science Advances
David J Herzfeld, Yoshiko Kojima, Robijanto Soetedjo, Reza Shadmehr
The primary output cells of the cerebellar cortex, Purkinje cells, make kinematic predictions about ongoing movements via high-frequency simple spikes, but receive sensory error information about that movement via low-frequency complex spikes (CS). How is the vector space of sensory errors encoded by this low-frequency signal? Here we measured Purkinje cell activity in the oculomotor vermis of animals during saccades, then followed the chain of events from experience of visual error, generation of CS, modulation of simple spikes, and ultimately change in motor output...
April 16, 2018: Nature Neuroscience
Tolib Mirzoev, Sumit Kane
BACKGROUND: Information from patient complaints - a widely accepted measure of patient satisfaction with services - can inform improvements in service quality, and contribute towards overall health systems performance. While analyses of data from patient complaints received much emphasis, there is limited published literature on key interventions to improve complaint management systems. OBJECTIVES: The objectives are two-fold: first, to synthesise existing evidence and provide practical options to inform future policy and practice and, second, to identify key outstanding gaps in the existing literature to inform agenda for future research...
2018: Global Health Action
Martin Vogt
Activity landscapes (ALs) are representations and models of compound data sets annotated with a target-specific activity. In contrast to quantitative structure-activity relationship (QSAR) models, ALs aim at characterizing structure-activity relationships (SARs) on a large-scale level encompassing all active compounds for specific targets. The popularity of AL modeling has grown substantially with the public availability of large activity-annotated compound data sets. AL modeling crucially depends on molecular representations and similarity metrics used to assess structural similarity...
April 14, 2018: Expert Opinion on Drug Discovery
Francesco Bagattini, Fabio Schoen, Luca Tigli
In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space...
April 14, 2018: Journal of Chemical Physics
Tomer D Ullman, Andreas Stuhlmüller, Noah D Goodman, Joshua B Tenenbaum
Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum, Kemp, Griffiths, & Goodman, 2011), we work with more expressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time...
April 10, 2018: Cognitive Psychology
A E Khizhnikova, A S Klochkov, A M Kotov-Smolensky, L A Chernikova, N A Suponeva, M A Piradov
BACKGROUND: the relevance of this study arises from the high prevalence of upper limb motor impairment and pathological synergy in the post-stroke patients; these conditions are very difficult to correct with the use of the traditional rehabilitation methods. A promising but insufficiently studied approaches are the virtual reality (VR) technology as well as its combination with other techniques. AIM: The objective of the present study was to evaluate the influence of the training making use of the mechanotherapeutic system on the motor function of the paretic hand...
April 9, 2018: Voprosy Kurortologii, Fizioterapii, i Lechebnoĭ Fizicheskoĭ Kultury
Yibing Ma, Zhiguo Jiang, Haopeng Zhang, Fengying Xie, Yushan Zheng, Huaqiang Shi, Yu Zhao, Jun Shi
BACKGROUND AND OBJECTIVE: Content-based image retrieval is an effective method for histopathological image analysis. However, given a database of huge whole slide images (WSIs), acquiring appropriate region-of-interests (ROIs) for training is significant and difficult. Moreover, histopathological images can only be annotated by pathologists, resulting in the lack of labeling information. Therefore, it is an important and challenging task to generate ROIs from WSI and retrieve image with few labels...
June 2018: Computer Methods and Programs in Biomedicine
John Schwoebel, Acasia K Depperman, Jessica L Scott
Spaced retrieval practice results in better long-term retention than massed retrieval practice. The episodic context account of this effect suggests that updated representations of the more distinct temporal contexts associated with spaced retrievals facilitate later recall. We examined whether environmental context, in addition to temporal context, may also play a role in retrieval-based learning. Participants studied and then attempted to retrieve the English translations of Swahili words during four acquisition blocks of trials...
April 12, 2018: Memory
Jon B Prince, Catherine J Stevens, Mari Riess Jones, Barbara Tillmann
Despite the empirical evidence for the power of the cognitive capacity of implicit learning of structures and regularities in several modalities and materials, it remains controversial whether implicit learning extends to the learning of temporal structures and regularities. We investigated whether (a) an artificial grammar can be learned equally well when expressed in duration sequences as when expressed in pitch sequences, (b) learning of the artificial grammar in either duration or pitch (as the primary dimension) sequences can be influenced by the properties of the secondary dimension (invariant vs...
April 12, 2018: Journal of Experimental Psychology. Learning, Memory, and Cognition
Caroline Bushdid, Claire A de March, Sebastien Fiorucci, Hiroaki Matsunami, Jérôme Golebiowski
Predicting the activity of chemicals for a given odorant receptor is a longstanding challenge. Here, the activity of 258 chemicals on the human G protein-coupled odorant receptor (OR)51E1, also known as prostate-specific G protein-coupled receptor 2 (PSGR2) was virtually screened by machine learning using 4884 chemical descriptors as input. A systematic control by functional in vitro assays revealed that a support vector machine algorithm accurately predicted the activity of a screened library. It allowed identifying two novel agonists in vitro for OR51E1...
April 12, 2018: Journal of Physical Chemistry Letters
Fang-Bo Lin, Xin Liu, Jing-Wen Xie, Jing Luo, Xia-Lu Feng, De-Ren Hou
OBJECTIVE: To compare the behavioral and pathological features of SORL1 gene knockout mice with those of normal mice and APP/PSE1 mice to verify the feasibility of using SORL1 knockout mice as a model of sporadic Alzheimer disease. METHODS: SORL1 gene of fertilized mouse eggs were edited using Crispr/Case9 technique. SORL1-/- mice were screened and identified by detecting the DNA sequence, and Western blotting was used to detect the expression of SORL1. SORL1-/- mice, control mice and APP/PSE1 mice all underwent Morris water maze test to assess their learning and memory abilities with positioning navigation and space exploration experiments...
March 20, 2018: Nan Fang Yi Ke da Xue Xue Bao, Journal of Southern Medical University
Yikang Zhou, Gang Li, Junkai Dong, Xin-Hui Xing, Junbiao Dai, Chong Zhang
Facing boosting ability to construct combinatorial metabolic pathways, how to search the metabolic sweet spot has become the rate-limiting step. We here reported an efficient Machine-learning workflow in conjunction with YeastFab Assembly strategy (MiYA) for combinatorial optimizing the large biosynthetic genotypic space of heterologous metabolic pathways in Saccharomyces cerevisiae. Using β-carotene biosynthetic pathway as example, we first demonstrated that MiYA has the power to search only a small fraction (2~5%) of combinatorial space to precisely tune the expression level of each gene with a machine-learning algorithm of ANN ensemble to avoid over-fitting problem when dealing with a small number of training samples...
April 5, 2018: Metabolic Engineering
Steve D M Brown, Chris C Holmes, Ann-Marie Mallon, Terrence F Meehan, Damian Smedley, Sara Wells
We are entering a new era of mouse phenomics, driven by large-scale and economical generation of mouse mutants coupled with increasingly sophisticated and comprehensive phenotyping. These studies are generating large, multidimensional gene-phenotype data sets, which are shedding new light on the mammalian genome landscape and revealing many hitherto unknown features of mammalian gene function. Moreover, these phenome resources provide a wealth of disease models and can be integrated with human genomics data as a powerful approach for the interpretation of human genetic variation and its relationship to disease...
April 6, 2018: Nature Reviews. Genetics
Xiao-Lei Zhang
Multilayer bootstrap network builds a gradually narrowed multilayer nonlinear network from bottom up for unsupervised nonlinear dimensionality reduction. Each layer of the network is a nonparametric density estimator. It consists of a group of k-centroids clusterings. Each clustering randomly selects data points with randomly selected features as its centroids, and learns a one-hot encoder by one-nearest-neighbor optimization. Geometrically, the nonparametric density estimator at each layer projects the input data space to a uniformly-distributed discrete feature space, where the similarity of two data points in the discrete feature space is measured by the number of the nearest centroids they share in common...
March 20, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Yang Wang, Lin Wu
Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts. In this paper we revisit it with a fundamentally different perspective by discovering LRR as essentially a latent clustered orthogonal projection based representation winged with an optimized local graph structure for spectral clustering; each column of the representation is fundamentally a cluster basis orthogonal to others to indicate its members, which intuitively projects the view-specific feature representation to be the one spanned by all orthogonal basis to characterize the cluster structures...
March 20, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Yuxing Fang, Xiaosha Wang, Suyu Zhong, Luping Song, Zaizhu Han, Gaolang Gong, Yanchao Bi
Object conceptual processing has been localized to distributed cortical regions that represent specific attributes. A challenging question is how object semantic space is formed. We tested a novel framework of representing semantic space in the pattern of white matter (WM) connections by extending the representational similarity analysis (RSA) to structural lesion pattern and behavioral data in 80 brain-damaged patients. For each WM connection, a neural representational dissimilarity matrix (RDM) was computed by first building machine-learning models with the voxel-wise WM lesion patterns as features to predict naming performance of a particular item and then computing the correlation between the predicted naming score and the actual naming score of another item in the testing patients...
April 6, 2018: PLoS Biology
Margherita Francescatto, Marco Chierici, Setareh Rezvan Dezfooli, Alessandro Zandonà, Giuseppe Jurman, Cesare Furlanello
BACKGROUND: High-throughput methodologies such as microarrays and next-generation sequencing are routinely used in cancer research, generating complex data at different omics layers. The effective integration of omics data could provide a broader insight into the mechanisms of cancer biology, helping researchers and clinicians to develop personalized therapies. RESULTS: In the context of CAMDA 2017 Neuroblastoma Data Integration challenge, we explore the use of Integrative Network Fusion (INF), a bioinformatics framework combining a similarity network fusion with machine learning for the integration of multiple omics data...
April 3, 2018: Biology Direct
Massimiliano Conson, Filippo Bianchini, Mario Quarantelli, Maddalena Boccia, Sara Salzano, Antonella Di Vita, Cecilia Guariglia
INTRODUCTION: Developmental topographical disorientation (DTD) is a lifelong condition in which affected individuals are selectively impaired in navigating space. Although it seems that DTD is widespread in the population, only a few cases have been studied from both a behavioral and a neuroimaging point of view. Here, we report a new case of DTD, never described previously, of a young woman (C.F.) showing a specific deficit in translating allocentrically coded information into egocentrically guided navigation, in presence of spared ability of constructing such representations...
April 4, 2018: Journal of Clinical and Experimental Neuropsychology
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