Deep learning in spiking neural networks
A Tavanaei, M Ghodrati, SR Kheradpisheh… - Neural networks, 2019 - Elsevier
In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
A review of learning in biologically plausible spiking neural networks
Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …
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 …
BP-STDP: Approximating backpropagation using spike timing dependent plasticity
A Tavanaei, A Maida - Neurocomputing, 2019 - Elsevier
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 …
Computing primitive of fully VCSEL-based all-optical spiking neural network for supervised learning and pattern classification
S **ang, Z Ren, Z Song, Y Zhang, X Guo… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
We propose computing primitive for an all-optical spiking neural network (SNN) based on
vertical-cavity surface-emitting lasers (VCSELs) for supervised learning by using biologically …
vertical-cavity surface-emitting lasers (VCSELs) for supervised learning by using biologically …
Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements
Compared to the abilities of the animal brain, many Artificial Intelligence systems have
limitations which emphasise the need for a Brain-Inspired Artificial Intelligence paradigm …
limitations which emphasise the need for a Brain-Inspired Artificial Intelligence paradigm …
Graphene–ferroelectric transistors as complementary synapses for supervised learning in spiking neural network
The hardware design of supervised learning (SL) in spiking neural network (SNN) prefers 3-
terminal memristive synapses, where the third terminal is used to impose supervise signals …
terminal memristive synapses, where the third terminal is used to impose supervise signals …
Spiking neural networks for crop yield estimation based on spatiotemporal analysis of image time series
This paper presents spiking neural networks (SNNs) for remote sensing spatiotemporal
analysis of image time series, which make use of the highly parallel and low-power …
analysis of image time series, which make use of the highly parallel and low-power …
A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks
There is a biological evidence to prove information is coded through precise timing of spikes
in the brain. However, training a population of spiking neurons in a multilayer network to fire …
in the brain. However, training a population of spiking neurons in a multilayer network to fire …
Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke
The paper presents a novel method and system for personalised (individualised) modelling
of spatio/spectro-temporal data (SSTD) and prediction of events. A novel evolving spiking …
of spatio/spectro-temporal data (SSTD) and prediction of events. A novel evolving spiking …