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https://www.readbyqxmd.com/read/27918886/computational-principles-and-models-of-multisensory-integration
#1
REVIEW
Chandramouli Chandrasekaran
Combining information from multiple senses creates robust percepts, speeds up responses, enhances learning, and improves detection, discrimination, and recognition. In this review, I discuss computational models and principles that provide insight into how this process of multisensory integration occurs at the behavioral and neural level. My initial focus is on drift-diffusion and Bayesian models that can predict behavior in multisensory contexts. I then highlight how recent neurophysiological and perturbation experiments provide evidence for a distributed redundant network for multisensory integration...
December 2, 2016: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/27914262/a-new-switching-control-for-finite-time-synchronization-of-memristor-based-recurrent-neural-networks
#2
Jie Gao, Peiyong Zhu, Ahmed Alsaedi, Fuad E Alsaadi, Tasawar Hayat
In this paper, finite-time synchronization (FTS) of memristor-based recurrent neural networks (MNNs) with time-varying delays is investigated by designing a new switching controller. First, by using the differential inclusions theory and set-valued maps, sufficient conditions to ensure FTS of MNNs are obtained under the two cases of 0<α<1 and α=0, and it is derived that α=0 is the critical value of 0<α<1. Next, it is discussed deeply on the relation between the parameter α and the synchronization time...
November 4, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27913368/content-driven-associative-memories-for-color-image-patterns
#3
Mingming Li, Shuzhi Sam Ge, Tong Heng Lee
This paper presents a novel content-driven associative memory (CDAM) to associate large-scale color images based on the subjects that represent the images' content. Compared to traditional associative memories, CDAM inherits their tolerance to random noise in images and possesses greater robustness against correlated noise that distorts an image's spatial contextual structure. A three-layer recurrent neural tensor network (RNTN) is designed as the network model of CDAM. Multiple salient objects detection algorithm and partial radial basis function (PRBF) kernel are proposed for subject determination and content-driven association, respectively...
November 29, 2016: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/27913360/real-time-decentralized-neural-control-via-backstepping-for-a-robotic-arm-powered-by-industrial-servomotors
#4
Luis A Vazquez, Francisco Jurado, Carlos E Castaneda, Victor Santibanez
This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, a local neural controller is derived via backstepping approach. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints...
December 1, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/27909395/reversal-learning-in-humans-and-gerbils-dynamic-control-network-facilitates-learning
#5
Christian Jarvers, Tobias Brosch, André Brechmann, Marie L Woldeit, Andreas L Schulz, Frank W Ohl, Marcel Lommerzheim, Heiko Neumann
Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears...
2016: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/27896980/deep-motif-dashboard-visualizing-and-understanding-genomic-sequences-using-deep-neural-networks
#6
Jack Lanchantin, Ritambhara Singh, Beilun Wang, Yanjun Qi
Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification...
2016: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/27889134/neural-adaptive-observer-based-sensor-and-actuator-fault-detection-in-nonlinear-systems-application-in-uav
#7
Alireza Abbaspour, Payam Aboutalebi, Kang K Yen, Arman Sargolzaei
A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies...
November 23, 2016: ISA Transactions
https://www.readbyqxmd.com/read/27885364/bidirectional-rnn-for-medical-event-detection-in-electronic-health-records
#8
Abhyuday N Jagannatha, Hong Yu
Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervised machine learning models in this domain are based on Conditional Random Fields (CRFs) with features calculated from fixed context windows. In this application, we explored recurrent neural network frameworks and show that they significantly out-performed the CRF models...
June 2016: Proceedings of the Conference
https://www.readbyqxmd.com/read/27872368/complementary-learning-systems-within-the-hippocampus-a-neural-network-modelling-approach-to-reconciling-episodic-memory-with-statistical-learning
#9
Anna C Schapiro, Nicholas B Turk-Browne, Matthew M Botvinick, Kenneth A Norman
A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences...
January 5, 2017: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
https://www.readbyqxmd.com/read/27850132/492-recurrent-neural-networks-on-electronic-medical-records-to-assess-picu-mortality-risk
#10
David Ledbetter, Melissa Aczon, Long Ho, Alec Gunny, Randall Wetzel
No abstract text is available yet for this article.
December 2016: Critical Care Medicine
https://www.readbyqxmd.com/read/27821193/a-novel-resting-state-functional-magnetic-resonance-imaging-signature-of-resilience-to-recurrent-depression
#11
C I Workman, K E Lythe, S McKie, J Moll, J A Gethin, J F W Deakin, R Elliott, R Zahn
BACKGROUND: A high proportion of patients with remitted major depressive disorder (MDD) will experience recurring episodes, whilst some develop resilience and remain in recovery. The neural basis of resilience to recurrence is elusive. Abnormal resting-state connectivity of the subgenual cingulate cortex (sgACC) was previously found in cross-sectional studies of MDD, suggesting its potential pathophysiological importance. The current study aimed to investigate whether resting-state connectivity to a left sgACC seed region distinguishes resilient patients from those developing recurring episodes...
November 8, 2016: Psychological Medicine
https://www.readbyqxmd.com/read/27815386/pten-signaling-in-the-postnatal-perivascular-progenitor-niche-drives-medulloblastoma-formation
#12
Guo Zhu, Sherri L Rankin, Jon D Larson, Xiaoyan Zhu, Lionel Ml Chow, Chunxu Qu, Jinghui Zhang, David W Ellison, Suzanne J Baker
Loss of the tumor suppressor gene PTEN exerts diverse outcomes on cancer in different developmental contexts. To gain insight into the effect of its loss on outcomes in the brain, we conditionally inactivated the murine Pten gene in neonatal neural stem/progenitor cells. Pten inactivation created an abnormal perivascular proliferative niche in the cerebellum that persisted in adult animals but did not progress to malignancy. Proliferating cells showed undifferentiated morphology and expressed the progenitor marker Nestin but not Math1, a marker of committed granule neuron progenitors...
November 4, 2016: Cancer Research
https://www.readbyqxmd.com/read/27815231/sleep-quality-prediction-from-wearable-data-using-deep-learning
#13
Aarti Sathyanarayana, Shafiq Joty, Luis Fernandez-Luque, Ferda Ofli, Jaideep Srivastava, Ahmed Elmagarmid, Teresa Arora, Shahrad Taheri
BACKGROUND: The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics...
November 4, 2016: JMIR MHealth and UHealth
https://www.readbyqxmd.com/read/27814468/a-modular-architecture-for-transparent-computation-in-recurrent-neural-networks
#14
Giovanni S Carmantini, Peter Beim Graben, Mathieu Desroches, Serafim Rodrigues
Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata...
September 24, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814464/complete-stability-of-delayed-recurrent-neural-networks-with-gaussian-activation-functions
#15
Peng Liu, Zhigang Zeng, Jun Wang
This paper addresses the complete stability of delayed recurrent neural networks with Gaussian activation functions. By means of the geometrical properties of Gaussian function and algebraic properties of nonsingular M-matrix, some sufficient conditions are obtained to ensure that for an n-neuron neural network, there are exactly 3(k) equilibrium points with 0≤k≤n, among which 2(k) and 3(k)-2(k) equilibrium points are locally exponentially stable and unstable, respectively. Moreover, it concludes that all the states converge to one of the equilibrium points; i...
October 8, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814462/echo-state-networks-for-data-driven-downhole-pressure-estimation-in-gas-lift-oil-wells
#16
Eric A Antonelo, Eduardo Camponogara, Bjarne Foss
Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesirable phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement. Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principle model simulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements...
October 4, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814352/neural-population-dynamics-during-reaching-are-better-explained-by-a-dynamical-system-than-representational-tuning
#17
Jonathan A Michaels, Benjamin Dann, Hansjörg Scherberger
Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex...
November 2016: PLoS Computational Biology
https://www.readbyqxmd.com/read/27812835/anti-correlations-in-the-degree-distribution-increase-stimulus-detection-performance-in-noisy-spiking-neural-networks
#18
Marijn B Martens, Arthur R Houweling, Paul H E Tiesinga
Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks...
November 4, 2016: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/27790093/synaptic-conductances-during-interictal-discharges-in-pyramidal-neurons-of-rat-entorhinal-cortex
#19
Dmitry V Amakhin, Julia L Ergina, Anton V Chizhov, Aleksey V Zaitsev
In epilepsy, the balance of excitation and inhibition underlying the basis of neural network activity shifts, resulting in neuronal network hyperexcitability and recurrent seizure-associated discharges. Mechanisms involved in ictal and interictal events are not fully understood, in particular, because of controversial data regarding the dynamics of excitatory and inhibitory synaptic conductances. In the present study, we estimated AMPAR-, NMDAR-, and GABAA R-mediated conductances during two distinct types of interictal discharge (IID) in pyramidal neurons of rat entorhinal cortex in cortico-hippocampal slices...
2016: Frontiers in Cellular Neuroscience
https://www.readbyqxmd.com/read/27783690/persistent-memory-in-single-node-delay-coupled-reservoir-computing
#20
André David Kovac, Maximilian Koall, Gordon Pipa, Hazem Toutounji
Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic conductances, to predator/pray population interactions. The evidence is mounting, not only to the presence of delays as physical constraints in signal propagation speed, but also to their functional role in providing dynamical diversity to the systems that comprise them. The latter observation in biological systems inspired the recent development of a computational architecture that harnesses this dynamical diversity, by delay-coupling a single nonlinear element to itself...
2016: PloS One
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