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Extending the neural engineering framework for nonideal silicon synapses
2017 IEEE International Symposium on Circuits and Systems (ISCAS), 2017The Neural Engineering Framework (NEF) is a theory for mapping computations onto biologically plausible networks of spiking neurons. This theory has been applied to a number of neuromorphic chips. However, within both silicon and real biological systems, synapses exhibit higher-order dynamics and heterogeneity.
Terrence Stewart +2 more
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Modeling speech production using the Neural Engineering Framework
2014 5th IEEE Conference on Cognitive Infocommunications (CogInfoCom), 2014A neurobiologically plausible model of speech production is introduced here using the Neural Engineering Framework (NEF). This approach allows detailed modeling of temporal aspects of action selection and action execution in speech production at the level of single spiking neurons.
Trevor Bekolay +2 more
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Optoelectronic neuromorphic system using the neural engineering framework
Applied Optics, 2017There has been a recent explosion of interest in neuromorphic computing capable of processing sophisticated and large-scale information based on spike coding, which has advantages in the implementation on electronic or optical neuromorphic systems.
Rui Wang +3 more
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Efficient SpiNNaker simulation of a heteroassociative memory using the Neural Engineering Framework
2016 International Joint Conference on Neural Networks (IJCNN), 2016The biological brain is a highly plastic system within which the efficacy and structure of synaptic connections are constantly changing in response to internal and external stimuli. While numerous models of this plastic behavior exist at various levels of abstraction, how these mechanisms allow the brain to learn meaningful values is unclear.
James C. Knight +4 more
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A wavelet neural network framework for diagnostics of complex engineered systems
Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206), 2002This paper introduces a new model-free diagnostic methodology to detect and identify machine failures and product defects. The basic module of the methodology is a novel multidimensional wavelet neural network construct used as the failure mode classifier. Validated sensor data are preprocessed and a vector of appropriate features is extracted.
G. Vachtsevanos +2 more
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2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2020
In this paper, we discuss the intermediate results of our on-going study of DeepFakes detection in videos. Our core focus is in exploitation of feature engineering as a precursor filtering technique, to the deep learning-based convolution neural network (CNN) classification frameworks.
Sonya J. Burroughs +3 more
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In this paper, we discuss the intermediate results of our on-going study of DeepFakes detection in videos. Our core focus is in exploitation of feature engineering as a precursor filtering technique, to the deep learning-based convolution neural network (CNN) classification frameworks.
Sonya J. Burroughs +3 more
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NEURAL NETWORKS IN CONSULTING ENGINEERING MANAGEMENT: A FRAMEWORK FOR BID-DECISION MANAGEMENT
Cybernetics and Systems, 1993Neural networks are devoted mainly to solving ill-structured problems. The field of consulting engineering embraces many such problems from environmental engineering to engineering management. It is a proper and adequate business environment in which to be assisted by the technology of expert (rule and nonrule based) systems.
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2023
Background: Neural Networks, inspired by the human brain, are a class of machine learning models composed of interconnected artificial neurons. They have a rich history dating back to the 1940s, with notable advancements in the 1980s and 1990s when techniques like backpropagation enabled the training of multi-layer ...
Haytham Al Ewaidat +3 more
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Background: Neural Networks, inspired by the human brain, are a class of machine learning models composed of interconnected artificial neurons. They have a rich history dating back to the 1940s, with notable advancements in the 1980s and 1990s when techniques like backpropagation enabled the training of multi-layer ...
Haytham Al Ewaidat +3 more
openaire +1 more source
2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 2019
Quality evaluation for higher engineering education has important guiding significance and feedback role on cultivating engineering talents. Combining with the educational core concept of outcomes-based education (OBE) and the educational process data, a fuzzy quality evaluation algorithm is developed for engineering education deriving from a ...
Shi-Yuan Han
exaly +2 more sources
Quality evaluation for higher engineering education has important guiding significance and feedback role on cultivating engineering talents. Combining with the educational core concept of outcomes-based education (OBE) and the educational process data, a fuzzy quality evaluation algorithm is developed for engineering education deriving from a ...
Shi-Yuan Han
exaly +2 more sources