Beyond Word for Word: Fact Guided Training for Neural Data-to-Document Generation

2019 ◽  
pp. 525-538
Author(s):  
Feng Nie ◽  
Hailin Chen ◽  
Jinpeng Wang ◽  
Rong Pan ◽  
Chin-Yew Lin
Keyword(s):  
Neural Data ◽  
Document Generation

Automated Test Requirement Document Generation

1987 ◽  
Author(s):  
Robert Haas ◽  
Fred Suzuki
Keyword(s):  
Requirement Document ◽  
Automated Test ◽  
Document Generation

scholarly journals Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

2021 ◽  
Author(s):  
Parikshat Sirpal ◽  
Rafat Damseh ◽  
Ke Peng ◽  
Dang Khoa Nguyen ◽  
Frédéric Lesage
Keyword(s):  
Near Infrared ◽  
Short Term Memory ◽  
A Priori ◽  
Functional Near Infrared Spectroscopy ◽  
Neural Data ◽  
Frequency Bands ◽  
Epileptic Patients ◽  
Brain Hemodynamics ◽  
Long Short Term Memory ◽  
Trained Neural Network

AbstractIn this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model’s fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.

scholarly journals Development of scenario management and requirements tool (SMaRT): towards supporting scenario-based requirements engineering methodology

2018 ◽  
Vol 7 (2.14) ◽  
pp. 62
Author(s):  
Azlin Nordin ◽  
Amirul Ikhwan Omar ◽  
Megat Usamah Megat Mohamed Amin ◽  
Norsaremah Salleh
Keyword(s):  
Requirements Engineering ◽  
Real World ◽  
Management Process ◽  
Requirements Management ◽  
Software Environment ◽  
Document Generation

Scenario-based methodology has been applied in analyzing real world experience into representation in software environment. Nonetheless, capturing, documenting and managing scenarios are extremely labor-intensive. Hence, the generic aims of this project are to investigate, explore and analyze requirements management process towards supporting scenario-based requirements approach. Based on our analysis of the existing RE or Requirements Management (RM) tools, most of the tools had less focus or missing some significant RE features to facilitate scenario-based   methodology. We analyzed all the relevant features and developed a prototype i.e.  Scenario-based Requirements Tool (SMaRT) to demonstrate how scenario-based approach can be implemented to further supports scenario-based RE  methodology and  covers most of the RE process  i.e. elicitation,  analysis  and  negotiation, documentation, validation,   and  management. SMaRT improves the RE tool features i.e. project and requirements management, document generation and validation function to further supports scenario-based RE methodology and covers most of the RE process i.e. elicitation, analysis, negotiation, documentation, validation, and management.   

scholarly journals Neural Components of Reading Revealed by Distributed and Symbolic Computational Models

2020 ◽  
Vol 1 (4) ◽  
pp. 381-401
Author(s):  
Ryan Staples ◽  
William W. Graves
Keyword(s):  
Neural Activity ◽  
Neural Coding ◽  
Computational Models ◽  
Ann Model ◽  
Distributed Representations ◽  
Neural Data ◽  
Cognitive Components ◽  
Components Of Reading ◽  
Artificial Neural Network Ann ◽  
Models Of Reading

Determining how the cognitive components of reading—orthographic, phonological, and semantic representations—are instantiated in the brain has been a long-standing goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit nonsymbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling–to–sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded to neural activity. However, the ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

scholarly journals Quantifying the signals contained in heterogeneous neural responses and determining their relationships with task performance

2014 ◽  
Vol 112 (6) ◽  
pp. 1584-1598 ◽  
Author(s):  
Marino Pagan ◽  
Nicole C. Rust
Keyword(s):  
Task Performance ◽  
Single Neuron ◽  
Weighted Sums ◽  
Neural Responses ◽  
Signal Modulation ◽  
Neural Data ◽  
Match To Sample ◽  
Neural Populations ◽  
Different Types ◽  
High Level

The responses of high-level neurons tend to be mixtures of many different types of signals. While this diversity is thought to allow for flexible neural processing, it presents a challenge for understanding how neural responses relate to task performance and to neural computation. To address these challenges, we have developed a new method to parse the responses of individual neurons into weighted sums of intuitive signal components. Our method computes the weights by projecting a neuron's responses onto a predefined orthonormal basis. Once determined, these weights can be combined into measures of signal modulation; however, in their raw form these signal modulation measures are biased by noise. Here we introduce and evaluate two methods for correcting this bias, and we report that an analytically derived approach produces performance that is robust and superior to a bootstrap procedure. Using neural data recorded from inferotemporal cortex and perirhinal cortex as monkeys performed a delayed-match-to-sample target search task, we demonstrate how the method can be used to quantify the amounts of task-relevant signals in heterogeneous neural populations. We also demonstrate how these intuitive quantifications of signal modulation can be related to single-neuron measures of task performance ( d′).

A Multi-Step Neural Control for Motor Brain-Machine Interface by Reinforcement Learning

2013 ◽  
Vol 461 ◽  
pp. 565-569 ◽  
Author(s):  
Fang Wang ◽  
Kai Xu ◽  
Qiao Sheng Zhang ◽  
Yi Wen Wang ◽  
Xiao Xiang Zheng
Keyword(s):  
Reinforcement Learning ◽  
Neural Activity ◽  
Neural Control ◽  
Brain Machine Interface ◽  
Learning Approach ◽  
Complex Task ◽  
Neural Data ◽  
Neural Spikes ◽  
Performance Improvements ◽  
Machine Interface

Brain-machine interfaces (BMIs) decode cortical neural spikes of paralyzed patients to control external devices for the purpose of movement restoration. Neuroplasticity induced by conducting a relatively complex task within multistep, is helpful to performance improvements of BMI system. Reinforcement learning (RL) allows the BMI system to interact with the environment to learn the task adaptively without a teacher signal, which is more appropriate to the case for paralyzed patients. In this work, we proposed to apply Q(λ)-learning to multistep goal-directed tasks using users neural activity. Neural data were recorded from M1 of a monkey manipulating a joystick in a center-out task. Compared with a supervised learning approach, significant BMI control was achieved with correct directional decoding in 84.2% and 81% of the trials from naïve states. The results demonstrate that the BMI system was able to complete a task by interacting with the environment, indicating that RL-based methods have the potential to develop more natural BMI systems.

scholarly journals Denoising neural data with state-space smoothing: Method and application

2009 ◽  
Vol 179 (1) ◽  
pp. 131-141 ◽  
Author(s):  
Hariharan Nalatore ◽  
Mingzhou Ding ◽  
Govindan Rangarajan
Keyword(s):  
State Space ◽  
Smoothing Method ◽  
Neural Data

Fast and Scalable Multi-Way Analysis of Massive Neural Data

2015 ◽  
Vol 64 (3) ◽  
pp. 707-719 ◽  
Author(s):  
Dan Chen ◽  
Xiaoli Li ◽  
Lizhe Wang ◽  
Samee U. Khan ◽  
Juan Wang ◽  
...  
Keyword(s):  
Neural Data
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