Hybrid physics-machine learning models for quantitative electron diffraction refinements. [PDF]
Nat CommunMalik SA +5 more
europepmc +1 more source
Explainable machine learning reveals diverse yield-determining factors among Thai rice farmer cohorts: Implications for targeted agricultural support. [PDF]
PLoS OneSuriyalaksh M +5 more
europepmc +1 more source
Related searches:
Programming Neuromorphics Using the Neural Engineering Framework
2021Aaron R Voelker +2 more
exaly +2 more sources
Deep neural network based framework for complex correlations in engineering metrics
Advanced Engineering Informatics, 2020Abstract Linear or polynomial regression and artificial neural networks are often adopted to obtain correlation models between various attributes in engineering fields. Although these are straightforward, they may not perform well for datasets that involve complex correlations among multiple attributes, and overfitting can occur when high-order ...
Vahid Asghari, Shu-Chien Hsu
exaly +2 more sources
Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition [PDF]
IEEE Transactions on Biomedical Circuits and Systems, 2017 We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously ...
André Van Schaik +2 more
exaly +4 more sources
An efficient SpiNNaker implementation of the Neural Engineering Framework
2015 International Joint Conference on Neural Networks (IJCNN), 2015By building and simulating neural systems we hope to understand how the brain may work and use this knowledge to build neural and cognitive systems to tackle engineering problems. The Neural Engineering Framework (NEF) is a hypothesis about how such systems may be constructed and has recently been used to build the world’s first functional brain model,
Terrence Stewart +2 more
exaly +3 more sources
Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System
2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2018The Neural Engineering Framework (NEF) provides a methodology for implementing algorithms and models using spiking neurons. Although it is possible to run simulations based on the NEF on Von Neumann hardware, neuromorphic hardware holds the promise of increased computational efficiency and lower power implementation.
Terrence Stewart, Andreas G Andreou
exaly +2 more sources
2022 IEEE 30th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2022 Architecture reverse engineering has become an emerging attack against deep neural network (DNN) implementations. Several prior works have utilized side-channel leakage to recover the model architecture while the target is executing on a hardware acceleration platform.
Xiaolin Xu, Shijin Duan, Cheng Gongye
exaly +3 more sources
A compact neural core for digital implementation of the Neural Engineering Framework
2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings, 2014The Neural Engineering Framework (NEF) is a tool that is capable of synthesising large-scale cognitive systems from subnetworks; and it has been used to construct SPAUN, which is the first brain model capable of performing cognitive tasks. It has been implemented on computers using high-level programming languages.
André Van Schaik +2 more
exaly +2 more sources