Results 21 to 30 of about 21,684 (244)

Spiking neural networks for computer vision [PDF]

Interface Focus, 2018
State-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs. Biological vision systems use a quite different approach, where the eyes (cameras) sample the visual scene continuously ...
Michael Hopkins, Garibaldi Pineda-García, Petruţ A. Bogdan, Steve B. Furber   +3 more
openaire   +4 more sources

Models developed for spiking neural networks

MethodsX, 2023
Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have won different machine learning challenges. Although these networks are inspired by the brain, they lack biological plausibility, and they have structural differences compared ...
Shahriar Rezghi Shirsavar, Abdol-Hossein Vahabie, Mohammad-Reza A． Dehaqani   +2 more
openaire   +4 more sources

SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber–Physical Systems

Biomimetics, 2023
One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS).
William R. P. Nourse, Clayton Jackson, Nicholas S. Szczecinski, Roger D. Quinn   +3 more
doaj   +1 more source

Training Spiking Neural Networks for Reinforcement Learning Tasks With Temporal Coding Method

Frontiers in Neuroscience, 2022
Recent years witness an increasing demand for using spiking neural networks (SNNs) to implement artificial intelligent systems. There is a demand of combining SNNs with reinforcement learning architectures to find an effective training method.
Guanlin Wu, Dongchen Liang, Shaotong Luan, Ji Wang   +3 more
doaj   +1 more source

Neural Architecture Search for Spiking Neural Networks

, 2022
Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. However, most prior SNN methods use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal performance for temporal sequence ...
Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Priyadarshini Panda   +4 more
openaire   +2 more sources

Federated Learning With Spiking Neural Networks [PDF]

IEEE Transactions on Signal Processing, 2021
As neural networks get widespread adoption in resource-constrained embedded devices, there is a growing need for low-power neural systems. Spiking Neural Networks (SNNs)are emerging to be an energy-efficient alternative to the traditional Artificial Neural Networks (ANNs) which are known to be computationally intensive. From an application perspective,
Yeshwanth Venkatesha, Youngeun Kim, Leandros Tassiulas, Priyadarshini Panda   +3 more
openaire   +2 more sources

Deep learning in spiking neural networks [PDF]

Neural Networks, 2019
In recent years, deep learning has been a revolution in the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation.
Kheradpisheh, Saeed Reza, Tavanaei, Amirhossein, Ghodrati, Masoud, Kheradpisheh, Saeed Reza, Masquelier, Timothée, Maida, Anthony   +5 more
openaire   +3 more sources

Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications

Frontiers in Neuroscience, 2020
In the last few years, spiking neural networks (SNNs) have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained CNN to a Spiking CNN without a significant sacrifice ...
Martino Sorbaro, Martino Sorbaro, Qian Liu, Massimo Bortone, Sadique Sheik   +4 more
doaj   +1 more source

Brain-Inspired Computing: Models and Architectures

IEEE Open Journal of Circuits and Systems, 2020
With an exponential increase in the amount of data collected per day, the fields of artificial intelligence and machine learning continue to progress at a rapid pace with respect to algorithms, models, applications, and hardware.
Keshab K. Parhi, Nanda K. Unnikrishnan
doaj   +1 more source

Bifurcation Spiking Neural Network

, 2019
Spiking neural networks (SNNs) has attracted much attention due to its great potential of modeling time-dependent signals. The firing rate of spiking neurons is decided by control rate which is fixed manually in advance, and thus, whether the firing rate is adequate for modeling actual time series relies on fortune.
Zhang, Shao-Qun, Zhang, Zhao-Yu, Zhou, Zhi-Hua   +2 more
openaire   +3 more sources