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Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation [PDF]
Frontiers in Neuroscience, 2021 Brain-inspired hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for artificial intelligence.
Avi Hazan, Elishai Ezra Tsur
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A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks [PDF]
SensorsThis paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN ...
Dailin Marrero, John Kern, Claudio Urrea
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Configurable Analog-Digital Conversion Using the Neural EngineeringFramework [PDF]
Frontiers in Neuroscience, 2014 Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to ...
Christian G Mayr +3 more
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Neuromorphic Neural Engineering Framework-Inspired Online Continuous Learning with Analog Circuitry [PDF]
Applied Sciences, 2022 Neuromorphic hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for machine learning. Here, we propose a neuromorphic analog design for continuous real-time learning.
Avi Hazan, Elishai Ezra Tsur
doaj +3 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 ...
Runchun Wang +2 more
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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.
Yukui Luo, Yunsi Fei, Xiaolin Xu
exaly +3 more sources
Computer Methods in Applied Mechanics and Engineering, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sourav Saha +2 more
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Neuromorphic NEF-Based Inverse Kinematics and PID Control
Frontiers in Neurorobotics, 2021 Neuromorphic implementation of robotic control has been shown to outperform conventional control paradigms in terms of robustness to perturbations and adaptation to varying conditions.
Yuval Zaidel +4 more
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Mapping Low-Dimensional Dynamics to High-Dimensional Neural Activity: A Derivation of the Ring Model From the Neural Engineering Framework [PDF]
Neural Computation, 2021 Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of ...
Omri Barak, Sandro Romani
openaire +4 more sources
Brain Sciences, 2023 The Neural Engineering Framework (Eliasmith & Anderson, 2003) is a long-standing method for implementing high-level algorithms constrained by low-level neurobiological details. In recent years, this method has been expanded to incorporate more biological
Nicole Sandra-Yaffa Dumont +5 more
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