Results 1 to 10 of about 120,010 (245)
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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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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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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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 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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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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Computers and Education: Artificial Intelligence, 2022 Educators' loss of ability to read students' comprehension level during the class through quick questions or nonverbal communication is one of the main challenges of online and blended learning. Many researchers recently tackled this problem by proposing
Ali Al-Zawqari +2 more
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Automatic Optimization of the Computation Graph in the Nengo Neural Network Simulator
Frontiers in Neuroinformatics, 2017 One critical factor limiting the size of neural cognitive models is the time required to simulate such models. To reduce simulation time, specialized hardware is often used.
Jan Gosmann, Chris Eliasmith
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Low Cost Evolutionary Neural Architecture Search (LENAS) Applied to Traffic Forecasting
Machine Learning and Knowledge Extraction, 2023 Traffic forecasting is an important task for transportation engineering as it helps authorities to plan and control traffic flow, detect congestion, and reduce environmental impact.
Daniel Klosa, Christof Büskens
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Does the Entorhinal Cortex use the Fourier Transform?
Frontiers in Computational Neuroscience, 2013 Some neurons in the entorhinal cortex (EC) fire bursts when the animal occupies locations organized in a hexagonal grid pattern in their spatial environment.
Jeff eOrchard, Hao eYang, Xiang eJi
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