StereoSpike: Depth Learning With a Spiking Neural Network [PDF]
IEEE Access, 2021 Depth estimation is an important computer vision task, useful in particular for navigation in autonomous vehicles, or for object manipulation in robotics.
Ulysse Rançon +3 more
semanticscholar +1 more source
HF-SNN: High-Frequency Spiking Neural Network
IEEE Access, 2021 As the third generation of neural networks, spiking neural network (SNN) motivated by neurophysiology enjoys considerable advances due to integrating different information, such as time and space.
Jing Su, Jing Li
doaj +1 more source
SpikeMS: Deep Spiking Neural Network for Motion Segmentation [PDF]
IEEE/RJS International Conference on Intelligent RObots and Systems, 2021 Spiking Neural Networks (SNN) are the so-called third generation of neural networks which attempt to more closely match the functioning of the biological brain. They inherently encode temporal data, allowing for training with less energy usage and can be
Chethan Parameshwara +5 more
semanticscholar +1 more source
Complementarity of spike- and rate-based dynamics of neural systems [PDF]
, 2012 Relationships between spiking-neuron and rate-based approaches to the dynamics of neural assemblies are explored by analyzing a model system that can be treated by both methods, with the rate-based method further averaged over multiple neurons to give a ...
P A Robinson +11 more
core +1 more source
Building Logistic Spiking Neuron Models Using Analytical Approach
IEEE Access, 2019 Spiking neuron models are inspired by biological neurons. They can simulate the neuronal activities of the mammalian brains, such as spiking (integrator) and periodic oscillation (resonator).
Lei Zhang
doaj +1 more source
An Implementation of Actor-Critic Algorithm on Spiking Neural Network Using Temporal Coding Method
Applied Sciences, 2022 Taking advantage of faster speed, less resource consumption and better biological interpretability of spiking neural networks, this paper developed a novel spiking neural network reinforcement learning method using actor-critic architecture and temporal ...
Junqi Lu +4 more
doaj +1 more source
BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons [PDF]
Neural Networks, 2021 Spiking neural networks (SNNs) transmit information through discrete spikes that perform well in processing spatial-temporal information. Owing to their nondifferentiable characteristic, difficulties persist in designing SNNs that deliver good ...
Dongcheng Zhao, Yi Zeng, Yang Li
semanticscholar +1 more source
Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition [PDF]
, 2009 Neuromorphic engineering (NE) is an emerging research field that has been attempting to identify neural types of computational principles, by implementing biophysically realistic models of neural systems in Very Large Scale Integration (VLSI) technology.
Douglas, R J +8 more
core +1 more source
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection [PDF]
AAAI Conference on Artificial Intelligence, 2019 Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications. As we try to solve more advanced problems, increasing demands for computing and power resources has become inevitable. Spiking neural
Seijoon Kim +3 more
semanticscholar +1 more source
A systematic method for configuring VLSI networks of spiking neurons [PDF]
, 2011 Neftci E, Chicca E, Indiveri G, Douglas RJ. A systematic method for configuring VLSI networks of spiking neurons. Neural Computation. 2011;23(10):2457-2497.An increasing number of research groups are developing custom hybrid analog/digital very large ...
Rodney Douglas +11 more
core +1 more source