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https://www.readbyqxmd.com/read/28087242/brains-for-birds-and-babies-neural-parallels-between-birdsong-and-speech-acquisition
#1
REVIEW
Jonathan Prather, Kazuo Okanoya, Johan J Bolhuis
Language as a computational cognitive mechanism appears to be unique to the human species. However, there are remarkable behavioral similarities between song learning in songbirds and speech acquisition in human infants that are absent in non-human primates. Here we review important neural parallels between birdsong and speech. In both cases there are separate but continually interacting neural networks that underlie vocal production, sensorimotor learning, and auditory perception and memory. As in the case of human speech, neural activity related to birdsong learning is lateralized, and mirror neurons linking perception and performance may contribute to sensorimotor learning...
January 10, 2017: Neuroscience and Biobehavioral Reviews
https://www.readbyqxmd.com/read/28081006/latent-feature-representation-with-depth-directional-long-term-recurrent-learning-for-breast-masses-in-digital-breast-tomosynthesis
#2
Dae Hoe Kim, Seong Tae Kim, Jung Min Chang, Yong Man Ro
Characterization of masses in computer-aided detection systems for digital breast tomosynthesis (DBT) is an important step to reduce false positive (FP) rates. To effectively differentiate masses from FPs in DBT, discriminative mass feature representation is required. In this paper, we propose a new latent feature representation boosted by depth directional long-term recurrent learning for characterizing malignant masses. The proposed network is designed to encode mass characteristics in two parts. First, 2D spatial image characteristics of DBT slices are encoded as a slice feature representation by convolutional neural network (CNN)...
January 12, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28080965/feeling-learning-from-and-being-aware-of-inner-states-interoceptive-dimensions-in-neurodegeneration-and-stroke
#3
Indira García-Cordero, Lucas Sedeño, Laura de la Fuente, Andrea Slachevsky, Gonzalo Forno, Francisco Klein, Patricia Lillo, Jesica Ferrari, Clara Rodriguez, Julian Bustin, Teresa Torralva, Sandra Baez, Adrian Yoris, Sol Esteves, Margherita Melloni, Paula Salamone, David Huepe, Facundo Manes, Adolfo M García, Agustín Ibañez
Interoception is a complex process encompassing multiple dimensions, such as accuracy, learning and awareness. Here, we examined whether each of those dimensions relies on specialized neural regions distributed throughout the vast interoceptive network. To this end, we obtained relevant measures of cardiac interoception in healthy subjects and patients offering contrastive lesion models of neurodegeneration and focal brain damage: behavioural variant fronto-temporal dementia (bvFTD), Alzheimer's disease (AD) and fronto-insular stroke...
November 19, 2016: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
https://www.readbyqxmd.com/read/28080124/statistical-learning-of-parts-and-wholes-a-neural-network-approach
#4
David C Plaut, Anna K Vande Velde
Statistical learning is often considered to be a means of discovering the units of perception, such as words and objects, and representing them as explicit "chunks." However, entities are not undifferentiated wholes but often contain parts that contribute systematically to their meanings. Studies of incidental auditory or visual statistical learning suggest that, as participants learn about wholes they become insensitive to parts embedded within them, but this seems difficult to reconcile with a broad range of findings in which parts and wholes work together to contribute to behavior...
January 12, 2017: Journal of Experimental Psychology. General
https://www.readbyqxmd.com/read/28077714/formation-of-long-term-locomotor-memories-is-associated-with-functional-connectivity-changes-in-the-cerebellar-thalamic-cortical-network
#5
Firas Mawase, Simona Bar-Haim, Lior Shmuelof
: Although motor adaptation is typically rapid, accumulating evidence shows that it is also associated with long-lasting behavioral and neuronal changes. Two processes were suggested to explain the formation of long-term motor memories: recall, reflecting a retrieval of previous motor actions, and faster relearning, reflecting an increased sensitivity to errors. Although these manifestations of motor memories were initially demonstrated in the context of adaptation experiments in reaching, indications of long-term motor memories were also demonstrated recently in other kinds of adaptation such as in locomotor adaptation...
January 11, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28077513/cognitive-control-structures-in-the-imitation-learning-of-spatial-sequences-and-rhythms-an-fmri-study
#6
Katrin Sakreida, Satomi Higuchi, Cinzia Di Dio, Michael Ziessler, Martine Turgeon, Neil Roberts, Stefan Vogt
Imitation learning involves the acquisition of novel motor patterns based on action observation (AO). We used event-related functional magnetic resonance imaging to study the imitation learning of spatial sequences and rhythms during AO, motor imagery (MI), and imitative execution in nonmusicians and musicians. While both tasks engaged the fronto-parietal mirror circuit, the spatial sequence task recruited posterior parietal and dorsal premotor regions more strongly. The rhythm task involved an additional network for auditory working memory...
January 10, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/28077035/nurses-online-behaviour-lessons-for-the-nursing-profession
#7
Janet Green
Social networking has become an extremely popular online activity, however like many activities on the internet, there are some privacy risks and concerns associated with its use. In recent years an increasing number of nurses have been censured or asked to appear before regulatory or registering authorities for unprofessional behaviour on social media sites. Problem behaviours identified include: inappropriate content and postings, crossing professional boundaries and breaching patient privacy and confidentiality...
January 12, 2017: Contemporary Nurse
https://www.readbyqxmd.com/read/28076982/from-structure-to-activity-using-centrality-measures-to-predict-neuronal-activity
#8
Jack McKay Fletcher, Thomas Wennekers
It is clear that the topological structure of a neural network somehow determines the activity of the neurons within it. In the present work, we ask to what extent it is possible to examine the structural features of a network and learn something about its activity? Specifically, we consider how the centrality (the importance of a node in a network) of a neuron correlates with its firing rate. To investigate, we apply an array of centrality measures, including In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, Hyperlink-Induced Topic Search (HITS) and NeuronRank to Leaky-Integrate and Fire neural networks with different connectivity schemes...
November 16, 2016: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28076974/topological-organization-of-whole-brain-white-matter-in-hiv-infection
#9
Laurie M Baker, Sarah Cooley, Ryan P Cabeen, David H Laidlaw, John A Joska, Jacqueline Hoare, Dan J Stein, Jodi Heaps-Woodruff, Lauren E Salminen, Robert H Paul
Infection with human immunodeficiency virus (HIV) is associated with neuroimaging alterations. However, little is known about the topological organization of whole-brain networks and the corresponding association with cognition. As such, we examined structural whole-brain white matter connectivity patterns and cognitive performance in 29 HIV+ young adults (mean age = 25.9) with limited or no HIV treatment history. HIV+ participants and demographically similar HIV- controls (n = 16) residing in South Africa underwent magnetic resonance imaging (MRI) and neuropsychological testing...
January 11, 2017: Brain Connectivity
https://www.readbyqxmd.com/read/28076866/biophysically-motivated-regulatory-network-inference-progress-and-prospects
#10
Tarmo Äijö, Richard Bonneau
Thanks to the confluence of genomic technology and computational developments, the possibility of network inference methods that automatically learn large comprehensive models of cellular regulation is closer than ever. This perspective focuses on enumerating the elements of computational strategies that, when coupled to appropriate experimental designs, can lead to accurate large-scale models of chromatin state and transcriptional regulatory structure and dynamics. We highlight 4 research questions that require further investigation in order to make progress in network inference: (1) using overall constraints on network structure such as sparsity, (2) use of informative priors and data integration to constrain individual model parameters, (3) estimation of latent regulatory factor activity under varying cell conditions, and (4) new methods for learning and modeling regulatory factor interactions...
2016: Human Heredity
https://www.readbyqxmd.com/read/28075373/visual-object-tracking-based-on-cross-modality-gaussian-bernoulli-deep-boltzmann-machines-with-rgb-d-sensors
#11
Mingxin Jiang, Zhigeng Pan, Zhenzhou Tang
Visual object tracking technology is one of the key issues in computer vision. In this paper, we propose a visual object tracking algorithm based on cross-modality featuredeep learning using Gaussian-Bernoulli deep Boltzmann machines (DBM) with RGB-D sensors. First, a cross-modality featurelearning network based on aGaussian-Bernoulli DBM is constructed, which can extract cross-modality features of the samples in RGB-D video data. Second, the cross-modality features of the samples are input into the logistic regression classifier, andthe observation likelihood model is established according to the confidence score of the classifier...
January 10, 2017: Sensors
https://www.readbyqxmd.com/read/28074855/mir-218-targets-mecp2-and-inhibits-heroin-seeking-behavior
#12
Biao Yan, Zhaoyang Hu, Wenqing Yao, Qiumin Le, Bo Xu, Xing Liu, Lan Ma
MicroRNAs (miRNAs) are a class of evolutionarily conserved, 18-25 nucleotide non-coding sequences that post-transcriptionally regulate gene expression. Recent studies implicated their roles in the regulation of neuronal functions, such as learning, cognition and memory formation. Here we report that miR-218 inhibits heroin-induced behavioral plasticity. First, network propagation-based method was used to predict candidate miRNAs that played potential key roles in regulating drug addiction-related genes. Microarray screening was also carried out to identify miRNAs responding to chronic heroin administration in the nucleus accumbens (NAc)...
January 11, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28074633/a-computational-interactome-for-prioritizing-genes-associated-with-complex-agronomic-traits-in-rice
#13
Shiwei Liu, Yihui Liu, Jiawei Zhao, Shitao Cai, Hongmei Qian, Kaijing Zuo, Lingxia Zhao, Lida Zhang
Rice is one of the most important staple foods for more than half of the world's population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome-wide rice protein-protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet/) using machine-learning with structural relationship and functional information...
January 11, 2017: Plant Journal: for Cell and Molecular Biology
https://www.readbyqxmd.com/read/28070484/deep-learning-predictions-of-survival-based-on-mri-in-amyotrophic-lateral-sclerosis
#14
Hannelore K van der Burgh, Ruben Schmidt, Henk-Jan Westeneng, Marcel A de Reus, Leonard H van den Berg, Martijn P van den Heuvel
Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic...
2017: NeuroImage: Clinical
https://www.readbyqxmd.com/read/28070001/neuroimaging-biomarkers-predict-brain-structural-connectivity-change-in-a-mouse-model-of-vascular-cognitive-impairment
#15
Philipp Boehm-Sturm, Martina Füchtemeier, Marco Foddis, Susanne Mueller, Rebecca C Trueman, Marietta Zille, Jan Leo Rinnenthal, Theodore Kypraios, Laurence Shaw, Ulrich Dirnagl, Tracy D Farr
BACKGROUND AND PURPOSE: Chronic hypoperfusion in the mouse brain has been suggested to mimic aspects of vascular cognitive impairment, such as white matter damage. Although this model has attracted attention, our group has struggled to generate a reliable cognitive and pathological phenotype. This study aimed to identify neuroimaging biomarkers of brain pathology in aged, more severely hypoperfused mice. METHODS: We used magnetic resonance imaging to characterize brain degeneration in mice hypoperfused by refining the surgical procedure to use the smallest reported diameter microcoils (160 μm)...
January 9, 2017: Stroke; a Journal of Cerebral Circulation
https://www.readbyqxmd.com/read/28068117/the-neural-representation-of-the-gender-of-faces-in-the-primate-visual-system-a-computer-modeling-study
#16
Thomas Minot, Hannah L Dury, Akihiro Eguchi, Glyn W Humphreys, Simon M Stringer
We use an established neural network model of the primate visual system to show how neurons might learn to encode the gender of faces. The model consists of a hierarchy of 4 competitive neuronal layers with associatively modifiable feedforward synaptic connections between successive layers. During training, the network was presented with many realistic images of male and female faces, during which the synaptic connections are modified using biologically plausible local associative learning rules. After training, we found that different subsets of output neurons have learned to respond exclusively to either male or female faces...
January 9, 2017: Psychological Review
https://www.readbyqxmd.com/read/28067293/drug-response-prediction-as-a-link-prediction-problem
#17
Zachary Stanfield, Mustafa Coşkun, Mehmet Koyutürk
Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models...
January 9, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28067224/updating-temporal-expectancy-of-an-aversive-event-engages-striatal-plasticity-under-amygdala-control
#18
Glenn Dallérac, Michael Graupner, Jeroen Knippenberg, Raquel Chacon Ruiz Martinez, Tatiane Ferreira Tavares, Lucille Tallot, Nicole El Massioui, Anna Verschueren, Sophie Höhn, Julie Boulanger Bertolus, Alex Reyes, Joseph E LeDoux, Glenn E Schafe, Lorenzo Diaz-Mataix, Valérie Doyère
Pavlovian aversive conditioning requires learning of the association between a conditioned stimulus (CS) and an unconditioned, aversive stimulus (US) but also involves encoding the time interval between the two stimuli. The neurobiological bases of this time interval learning are unknown. Here, we show that in rats, the dorsal striatum and basal amygdala belong to a common functional network underlying temporal expectancy and learning of a CS-US interval. Importantly, changes in coherence between striatum and amygdala local field potentials (LFPs) were found to couple these structures during interval estimation within the lower range of the theta rhythm (3-6 Hz)...
January 9, 2017: Nature Communications
https://www.readbyqxmd.com/read/28067221/quantum-chemical-insights-from-deep-tensor-neural-networks
#19
Kristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R Müller, Alexandre Tkatchenko
Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol(-1)) predictions in compositional and configurational chemical space for molecules of intermediate size...
January 9, 2017: Nature Communications
https://www.readbyqxmd.com/read/28066809/deep-feature-transfer-learning-in-combination-with-traditional-features-predicts-survival-among-patients-with-lung-adenocarcinoma
#20
Rahul Paul, Samuel H Hawkins, Yoganand Balagurunathan, Matthew B Schabath, Robert J Gillies, Lawrence O Hall, Dmitry B Goldgof
Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors...
December 2016: Tomography: a Journal for Imaging Research
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