Efficient federated learning with spike neural networks for traffic sign recognition
With the gradual popularization of self-driving, it is becoming increasingly important for
vehicles to smartly make the right driving decisions and autonomously obey traffic rules by …
vehicles to smartly make the right driving decisions and autonomously obey traffic rules by …
A layered spiking neural system for classification problems
Biological brains have a natural capacity for resolving certain classification tasks. Studies on
biologically plausible spiking neurons, architectures and mechanisms of artificial neural …
biologically plausible spiking neurons, architectures and mechanisms of artificial neural …
Temporal-coded spiking neural networks with dynamic firing threshold: Learning with event-driven backpropagation
Abstract Spiking Neural Networks (SNNs) offer a highly promising computing paradigm due
to their biological plausibility, exceptional spatiotemporal information processing capability …
to their biological plausibility, exceptional spatiotemporal information processing capability …
Sparse-firing regularization methods for spiking neural networks with time-to-first-spike coding
The training of multilayer spiking neural networks (SNNs) using the error backpropagation
algorithm has made significant progress in recent years. Among the various training …
algorithm has made significant progress in recent years. Among the various training …
Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network
I Jeon, T Kim - Frontiers in Computational Neuroscience, 2023 - frontiersin.org
Although it may appear infeasible and impractical, building artificial intelligence (AI) using a
bottom-up approach based on the understanding of neuroscience is straightforward. The …
bottom-up approach based on the understanding of neuroscience is straightforward. The …
Trainable Spiking-YOLO for low-latency and high-performance object detection
Spiking neural networks (SNNs) are considered an attractive option for edge-side
applications due to their sparse, asynchronous and event-driven characteristics. However …
applications due to their sparse, asynchronous and event-driven characteristics. However …
Delay learning based on temporal coding in Spiking Neural Networks
Abstract Spiking Neural Networks (SNNs) hold great potential for mimicking the brain's
efficient processing of information. Although biological evidence suggests that precise spike …
efficient processing of information. Although biological evidence suggests that precise spike …
Backpropagation with sparsity regularization for spiking neural network learning
The spiking neural network (SNN) is a possible pathway for low-power and energy-efficient
processing and computing exploiting spiking-driven and sparsity features of biological …
processing and computing exploiting spiking-driven and sparsity features of biological …
A fusion-based spiking neural network approach for predicting collaboration request in human-robot collaboration
In human-robot collaborative (HRC) manufacturing systems, how the collaborative robots
engage in the collaborative tasks and complete the corresponding work in a timely manner …
engage in the collaborative tasks and complete the corresponding work in a timely manner …
Sparseprop: Efficient event-based simulation and training of sparse recurrent spiking neural networks
R Engelken - Advances in Neural Information Processing …, 2023 - proceedings.neurips.cc
Abstract Spiking Neural Networks (SNNs) are biologically-inspired models that are capable
of processing information in streams of action potentials. However, simulating and training …
of processing information in streams of action potentials. However, simulating and training …