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https://www.readbyqxmd.com/read/30524218/organizing-sequential-memory-in-a-neuromorphic-device-using-dynamic-neural-fields
#1
Raphaela Kreiser, Dora Aathmani, Ning Qiao, Giacomo Indiveri, Yulia Sandamirskaya
Neuromorphic Very Large Scale Integration (VLSI) devices emulate the activation dynamics of biological neuronal networks using either mixed-signal analog/digital or purely digital electronic circuits. Using analog circuits in silicon to physically emulate the functionality of biological neurons and synapses enables faithful modeling of neural and synaptic dynamics at ultra low power consumption in real-time, and thus may serve as computational substrate for a new generation of efficient neural controllers for artificial intelligent systems...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/30513692/online-prediction-of-ship-behavior-with-automatic-identification-system-sensor-data-using-bidirectional-long-short-term-memory-recurrent-neural-network
#2
Miao Gao, Guoyou Shi, Shuang Li
The real-time prediction of ship behavior plays an important role in navigation and intelligent collision avoidance systems. This study developed an online real-time ship behavior prediction model by constructing a bidirectional long short-term memory recurrent neural network (BI-LSTM-RNN) that is suitable for automatic identification system (AIS) date and time sequential characteristics, and for online parameter adjustment. The bidirectional structure enhanced the relevance between historical and future data, thus improving the prediction accuracy...
November 30, 2018: Sensors
https://www.readbyqxmd.com/read/30504276/perceptual-decision-making-biases-in-post-error-reaction-times-explained-by-attractor-network-dynamics
#3
Kevin Berlemont, Jean-Pierre Nadal
Perceptual decision-making is the subject of many experimental and theoretical studies. Most modeling analyses are based on statistical processes of accumulation of evidence. In contrast, very few works confront attractor network models' predictions with empirical data from continuous sequences of trials. Recently however, numerical simulations of a biophysical competitive attractor network model have shown that such network can describe sequences of decision trials and reproduce repetition biases observed in perceptual decision experiments...
November 30, 2018: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/30498472/identification-of-mild-cognitive-impairment-from-speech-in-swedish-using-deep-sequential-neural-networks
#4
Charalambos Themistocleous, Marie Eckerström, Dimitrios Kokkinakis
While people with mild cognitive impairment (MCI) portray noticeably incipient memory difficulty in remembering events and situations along with problems in decision making, planning, and finding their way in familiar environments, detailed neuropsychological assessments also indicate deficits in language performance. To this day, there is no cure for dementia but early-stage treatment can delay the progression of MCI; thus, the development of valid tools for identifying early cognitive changes is of great importance...
2018: Frontiers in Neurology
https://www.readbyqxmd.com/read/30496914/concept-learning-through-deep-reinforcement-learning-with-memory-augmented-neural-networks
#5
Jing Shi, Jiaming Xu, Yiqun Yao, Bo Xu
Deep neural networks have shown superior performance in many regimes to remember familiar patterns with large amounts of data. However, the standard supervised deep learning paradigm is still limited when facing the need to learn new concepts efficiently from scarce data. In this paper, we present a memory-augmented neural network which is motivated by the process of human concept learning. The training procedure, imitating the concept formation course of human, learns how to distinguish samples from different classes and aggregate samples of the same kind...
November 12, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/30474053/infant-cognition-includes-the-potentially-human-unique-ability-to-encode-embedding
#6
M Winkler, J L Mueller, A D Friederici, C Männel
Human cognition relies on the ability to encode complex regularities in the input. Regularities above a certain complexity level can involve the feature of embedding, defined by nested relations between sequential elements. While comparative studies suggest the cognitive processing of embedding to be human specific, evidence of its ontogenesis is lacking. To assess infants' ability to process embedding, we implemented nested relations in tone sequences, minimizing perceptual and memory requirements. We measured 5-month-olds' brain responses in two auditory oddball paradigms, presenting standard sequences with one or two levels of embedding, interspersed with infrequent deviant sequences violating the established embedding rules...
November 2018: Science Advances
https://www.readbyqxmd.com/read/30471306/carbofuran-hampers-oligodendrocytes-development-leading-to-impaired-myelination-in-the-hippocampus-of-rat-brain
#7
Brashket Seth, Anuradha Yadav, Ankit Tandon, Jai Shankar, Rajnish Kumar Chaturvedi
During the mammalian brain development, oligodendrocyte progenitor cells (OPCs) are generated from neuroepithelium and migrate throughout the brain. Myelination is a tightly regulated process which involves time framed sequential events of OPCs proliferation, migration, differentiation and interaction with axons for functional insulated sheath formation. Myelin is essential for efficient and rapid conduction of electric impulses and its loss in the hippocampus of the brain may result in impaired memory and long-term neurological deficits...
November 21, 2018: Neurotoxicology
https://www.readbyqxmd.com/read/30469355/hierarchical-clustering-of-dna-k-mer-counts-in-rnaseq-fastq-files-identifies-sample-heterogeneities
#8
Wolfgang Kaisers, Holger Schwender, Heiner Schaal
We apply hierarchical clustering (HC) of DNA k-mer counts on multiple Fastq files. The tree structures produced by HC may reflect experimental groups and thereby indicate experimental effects, but clustering of preparation groups indicates the presence of batch effects. Hence, HC of DNA k-mer counts may serve as a diagnostic device. In order to provide a simple applicable tool we implemented sequential analysis of Fastq reads with low memory usage in an R package (seqTools) available on Bioconductor. The approach is validated by analysis of Fastq file batches containing RNAseq data...
November 21, 2018: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/30459310/retroactive-and-graded-prioritization-of-memory-by-reward
#9
Erin Kendall Braun, G Elliott Wimmer, Daphna Shohamy
Many decisions are based on an internal model of the world. Yet, how such a model is constructed from experience and represented in memory remains unknown. We test the hypothesis that reward shapes memory for sequences of events by retroactively prioritizing memory for objects as a function of their distance from reward. Human participants encountered neutral objects while exploring a series of mazes for reward. Across six data sets, we find that reward systematically modulates memory for neutral objects, retroactively prioritizing memory for objects closest to the reward...
November 20, 2018: Nature Communications
https://www.readbyqxmd.com/read/30458398/sequential-prediction-of-quantitative-health-risk-assessment-for-the-fine-particulate-matter-in-an-underground-facility-using-deep-recurrent-neural-networks
#10
Jorge Loy-Benitez, Paulina Vilela, Qian Li, ChangKyoo Yoo
Particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5 ) in indoor public spaces such as subway stations, has represented a major public health concern; however, forecasting future sequences of quantitative health risk is an effective method for protecting commuters' health, and an important tool for developing early warning systems. Despite the existence of several predicting methods, some tend to fail to forecast long-term dependencies in an effective way. This paper aims to implement a multiple sequences prediction of a comprehensive indoor air quality index (CIAI) traced by indoor PM2...
November 17, 2018: Ecotoxicology and Environmental Safety
https://www.readbyqxmd.com/read/30452352/deep-sequential-segmentation-of-organs-in-volumetric-medical-scans
#11
Alexey A Novikov, David Major, Maria Wimmer, Dimitrios Lenis, Katja Buhler
Segmentation in 3D scans is playing an increasingly important role in current clinical practice supporting diagnosis, tissue quantification, or treatment planning. The current 3D approaches based on Convolutional Neural Networks (CNN) usually suffer from at least three main issues caused predominantly by implementation constraints - first, they require resizing the volume to the lower-resolutional reference dimensions, second, the capacity of such approaches is very limited due to memory restrictions, and third, all slices of volumes have to be available at any given training or testing time...
November 16, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30451107/reaction-time-of-erroneous-responses-in-the-go-no-go-paradigm
#12
Yanick Leblanc-Sirois, Claude M J Braun, Jonathan Elie-Fortier
Reaction time (RT) of erroneous responses in go/no-go tasks tends to be shorter than RT of correct responses. An opposite difference has been reported ( Halperin, Wolf, Greenblatt, & Young, 1991 ) which could be attributed to differences in go trial probability, or to high memory demand. Two experiments aimed here to test these two explanations, a simultaneous matching task with low memory load (Experiment 1), and a sequential matching task with high memory load (Experiment 2). Go trial probability was also manipulated...
September 2018: Experimental Psychology
https://www.readbyqxmd.com/read/30450798/adult-bone-marrow-three-dimensional-phenotypic-landscape-of-b-cell-differentiation
#13
Claire Carrion, Estelle Guérin, Nathalie Gachard, Alexandre le Guyader, Stéphane Giraut, Jean Feuillard
BACKGROUND: The different B-cell subsets in human bone marrow result from a dynamic equilibrium between endogenous production, B-cell bone marrow reentry and terminal plasma cell differentiation. Our aim was to define and quantify the different medullary B-cell subsets. METHODS: A series of 32 normal adult bone marrows plus 15 normal adult blood samples was studied by nine color flow cytometry (CD10, CD19, CD24, CD27, CD34, CD38, CD45, IgM, and IgD). With the Kaluza software radar plots, two 2D triple parametric histograms (CD10/CD34/CD45 and CD27/IgM/IgD) were set-up to identify six progenitor and five mature B-cell subsets...
November 18, 2018: Cytometry. Part B, Clinical Cytometry
https://www.readbyqxmd.com/read/30441867/study-on-the-preferred-application-oriented-index-for-mental-fatigue-detection
#14
Tianhong Duan, Nong Zhang, Kaiway Li, Xuelin Hou, Jun Pei
Most of the research on mental fatigue evaluation has mainly concentrated on some indexes that require sophisticated and large instruments that make the detection of mental fatigue cumbersome, time-consuming, and difficult to apply on a large scale. A quick and sensitive mental fatigue detection index is necessary so that mentally fatigued workers can be alerted in time and take corresponding countermeasures. However, to date, no studies have compared the sensitivity of common objective evaluation indexes. To solve these problems, this study recruited 56 human subjects...
November 14, 2018: International Journal of Environmental Research and Public Health
https://www.readbyqxmd.com/read/30441177/predict-in-hospital-code-blue-events-using-monitor-alarms-through-deep-learning-approach
#15
Ran Xiao, Johnathan King, Andrea Villaroman, Duc H Do, Noel G Boyle, Xiao Hu
Bedside monitors in hospital intensive care units (ICUs) are known to produce excessive false alarms that could desensitize caregivers, resulting in delayed or even missed clinical interventions to life-threatening events. Our previous studies proposed a framework aggregating information in monitor alarm data by mining frequent alarm combinations (i.e., SuperAlarm) that are predictive to clinical endpoints, such as code blue events, in an effort to address this critical issue. In the present pilot study, we hypothesize that sequential deep learning models, specifically long-short term memory (LSTM), could capture time-depend features in continuous alarm sequences preceding code blue events and these features may be predictive of these endpoints...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30440769/improving-eeg-based-motor-imagery-classification-via-spatial-and-temporal-recurrent-neural-networks
#16
Xuelin Ma, Shuang Qiu, Changde Du, Jiezhen Xing, Huiguang He
Motor imagery (MI) based Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing movement intention of severely disabled persons. There are extensive studies about MI-based intention recognition, most of which heavily rely on staged handcrafted EEG feature extraction and classifier design. For end-to-end deep learning methods, researchers encode spatial information with convolution neural networks (CNNs) from raw EEG data. Compared with CNNs, recurrent neural networks (RNNs) allow for long-range lateral interactions between features...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30440405/deep-classification-of-epileptic-signals
#17
David Ahmedt-Aristizabal, Clinton Fookes, Kien Nguyen, Sridha Sridharan
Electrophysiological observation plays a major role in epilepsy evaluation. However, human interpretation of brain signals is subjective and prone to misdiagnosis. Automating this process, especially seizure detection relying on scalpbased Electroencephalography (EEG) and intracranial EEG, has been the focus of research over recent decades. Nevertheless, its numerous challenges have inhibited a definitive solution. Inspired by recent advances in deep learning, here we describe a new classification approach for EEG time series based on Recurrent Neural Networks (RNNs) via the use of Long- Short Term Memory (LSTM) networks...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30440327/multiple-brain-activities-during-sequential-memory-encoding-meg-study-of-modulation-of-alpha-band-rhythm
#18
Koichi Yokosawa, Ryoken Takase, Ryota Chitose, Keisuke Kimura
It is known that alpha-band rhythm during memory maintenance is enhanced by increasing memory load. This enhancement is generally thought to be caused by active inhibition of task-irrelevant visual inputs. During sequential memory processing, we previously found that alpha-band activity increases from beginning to midterm during memory encoding, and conversely decreases from midterm to ending. In the present study, we conducted two experiments to determine the spatial and functional role of alpha-band rhythm during sequential memory processing...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30426926/decoding-hierarchical-control-of-sequential-behavior-in-oscillatory-eeg-activity
#19
Atsushi Kikumoto, Ulrich Mayr
Despite strong theoretical reasons for assuming that abstract representations organize complex action sequences in terms of subplans (chunks) and sequential positions, we lack methods to directly track such content-independent, hierarchical representations in humans. We applied time-resolved, multivariate decoding analysis to the pattern of rhythmic EEG activity that was registered while participants planned and executed individual elements from pre-learned, structured sequences. Across three experiments, the theta and alpha-band activity coded basic elements and abstract control representations, in particular, the ordinal position of basic elements, but also the identity and position of chunks...
November 14, 2018: ELife
https://www.readbyqxmd.com/read/30418449/neuromorphic-computation-with-spiking-memristors-habituation-experimental-instantiation-of-logic-gates-and-a-novel-sequence-sensitive-perceptron-model
#20
Ella M Gale
Memristors have been compared to neurons and synapses, suggesting they would be good for neuromorphic computing. A change in voltage across a memristor causes a current spike which imparts a short-term memory to a memristor, allowing for through-time computation, which can do arithmetical operations and sequential logic, or model short-time habituation to a stimulus. Using simple physical rules, simple logic gates such as XOR, and novel, more complex, gates such as the arithmetic full adder (AFA) can be instantiated in sol-gel TiO2 plastic memristors...
November 12, 2018: Faraday Discussions
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