Supervised learning in spiking neural networks: A review of algorithms and evaluations
X Wang, X Lin, X Dang - Neural Networks, 2020 - Elsevier
As a new brain-inspired computational model of the artificial neural network, a spiking
neural network encodes and processes neural information through precisely timed spike …
neural network encodes and processes neural information through precisely timed spike …
Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks
Melanocytes are skin cells that give color to the skin and form melanin color pigments. The
unbalanced division and proliferation of these cells result in skin cancer. The early diagnosis …
unbalanced division and proliferation of these cells result in skin cancer. The early diagnosis …
BP-STDP: Approximating backpropagation using spike timing dependent plasticity
The problem of training spiking neural networks (SNNs) is a necessary precondition to
understanding computations within the brain, a field still in its infancy. Previous work has …
understanding computations within the brain, a field still in its infancy. Previous work has …
Detection of weather images by using spiking neural networks of deep learning models
The transmission of weather information of a location at certain time intervals affects the
living conditions of the people there directly or indirectly. According to weather information …
living conditions of the people there directly or indirectly. According to weather information …
H2learn: High-efficiency learning accelerator for high-accuracy spiking neural networks
Although spiking neural networks (SNNs) take benefits from the bioplausible neural
modeling, the low accuracy under the common local synaptic plasticity learning rules limits …
modeling, the low accuracy under the common local synaptic plasticity learning rules limits …
A brief review on spiking neural network-a biological inspiration
TH Rafi - 2021 - preprints.org
Recent advancement of deep learning has been elevated the multifaceted nature in various
applications of this field. Artificial neural networks are now turning into a genuinely old …
applications of this field. Artificial neural networks are now turning into a genuinely old …
Behaviornet: A fine-grained behavior-aware network for dynamic link prediction
Dynamic link prediction has become a trending research subject because of its wide
applications in the web, sociology, transportation, and bioinformatics. Currently, the …
applications in the web, sociology, transportation, and bioinformatics. Currently, the …
A survey on learning models of spiking neural membrane systems and spiking neural networks
Spiking neural networks (SNN) are a biologically inspired model of neural networks with
certain brain-like properties. In the past few decades, this model has received increasing …
certain brain-like properties. In the past few decades, this model has received increasing …
Training multi-layer spiking neural networks using NormAD based spatio-temporal error backpropagation
Spiking neural networks (SNNs) have garnered a great amount of interest for supervised
and unsupervised learning applications. This paper deals with the problem of training multi …
and unsupervised learning applications. This paper deals with the problem of training multi …
RETRACTED ARTICLE: Classification of ultrasound breast cancer tumor images using neural learning and predicting the tumor growth rate
VMK Rani, SS Dhenakaran - Multimedia Tools and Applications, 2020 - Springer
Ultrasound breast cancer tumor growth model is dependence on the cancer tumor growth
size on time. Breast cancer tumor progresses on its growth and evaluated to estimate the …
size on time. Breast cancer tumor progresses on its growth and evaluated to estimate the …