Research progress of spiking neural network in image classification: a review
LY Niu, Y Wei, WB Liu, JY Long, T Xue - Applied intelligence, 2023 - Springer
Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs),
which is more analogous with the brain. It has been widely considered with neural …
which is more analogous with the brain. It has been widely considered with neural …
Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network
VA Demin, DV Nekhaev, IA Surazhevsky, KE Nikiruy… - Neural Networks, 2021 - Elsevier
This work is aimed to study experimental and theoretical approaches for searching effective
local training rules for unsupervised pattern recognition by high-performance memristor …
local training rules for unsupervised pattern recognition by high-performance memristor …
Deep learning incorporating biologically inspired neural dynamics and in-memory computing
Spiking neural networks (SNNs) incorporating biologically plausible neurons hold great
promise because of their unique temporal dynamics and energy efficiency. However, SNNs …
promise because of their unique temporal dynamics and energy efficiency. However, SNNs …
Deep spiking neural network model for time-variant signals classification: a real-time speech recognition approach
Speech recognition has become an important task to improve the human-machine interface.
Taking into account the limitations of current automatic speech recognition systems, like non …
Taking into account the limitations of current automatic speech recognition systems, like non …
Building brain-inspired computing systems: Examining the role of nanoscale devices
Brain-inspired computing is attracting considerable attention because of its potential to solve
a wide variety of data-intensive problems that are difficult for even state-of-the-art …
a wide variety of data-intensive problems that are difficult for even state-of-the-art …
Surrogate gradients design
L Herranz-Celotti, J Rouat - arXiv preprint arXiv:2202.00282, 2022 - arxiv.org
Surrogate gradient (SG) training provides the possibility to quickly transfer all the gains
made in deep learning to neuromorphic computing and neuromorphic processors, with the …
made in deep learning to neuromorphic computing and neuromorphic processors, with the …
Improving spiking neural networks trained with spike timing dependent plasticity for image recognition
P Falez - 2019 - hal.science
Computer vision is a strategic field, in consequence of its great number of potential
applications which could have a high impact on society. This area has quickly improved over …
applications which could have a high impact on society. This area has quickly improved over …
The role of short-term plasticity in neuromorphic learning: Learning from the timing of rate-varying events with fatiguing spike-timing-dependent plasticity
T Moraitis, A Sebastian… - IEEE Nanotechnology …, 2018 - ieeexplore.ieee.org
Neural networks (NNs) have been able to provide record-breaking performance in several
machine-learning tasks, such as image and speech recognition, natural-language …
machine-learning tasks, such as image and speech recognition, natural-language …
[PDF][PDF] Deep learning incorporating biologically-inspired neural dynamics
Neural networks have become the key technology of artificial intelligence and have
contributed to breakthroughs in several machine learning tasks, primarily owing to advances …
contributed to breakthroughs in several machine learning tasks, primarily owing to advances …
Spiking neural networks enable two-dimensional neurons and unsupervised multi-timescale learning
T Moraitis, A Sebastian… - 2018 International Joint …, 2018 - ieeexplore.ieee.org
The capabilities of artificial neural networks (ANNs) are limited by the operations possible at
their individual neurons and synapses. For instance, each neuron's activation only …
their individual neurons and synapses. For instance, each neuron's activation only …