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Found 31 papers, 6 papers with code
GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL
Spiking neural networks (SNNs) are widely applied in various fields due to their energy-efficient and fast-inference capabilities.
EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks
Event cameras, with their high dynamic range and temporal resolution, are ideally suited for object detection, especially under scenarios with motion blur and challenging lighting conditions.
Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS).
Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning
The Darwin3 chip supports up to 2. 35 million neurons, making it the largest of its kind in neuron scale.
Deep Pulse-Coupled Neural Networks
In this work, we leverage a more biologically plausible neural model with complex dynamics, i. e., a pulse-coupled neural network (PCNN), to improve the expressiveness and recognition performance of SNNs for vision tasks.
Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection
While advances in artificial intelligence and neuroscience have enabled the emergence of neural networks capable of learning a wide variety of tasks, our understanding of the temporal dynamics of these networks remains limited.
Neuromorphic Auditory Perception by Neural Spiketrum
Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems.
Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding
The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making.
Mitigating Communication Costs in Neural Networks: The Role of Dendritic Nonlinearity
Our comprehension of biological neuronal networks has profoundly influenced the evolution of artificial neural networks (ANNs).
Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes
This work presents the temporal conditioning spiking latent variable models (TeCoS-LVM) to simulate the neural response to natural visual stimuli.
Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection
We also present a SNN-based ultra-low latency and high accurate object detection model (SUHD) that achieves state-of-the-art performance on nontrivial datasets like PASCAL VOC and MS COCO, with about remarkable 750x fewer timesteps and 30% mean average precision (mAP) improvement, compared to the Spiking-YOLO on MS COCO datasets.
ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks
To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training.
Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks
Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations.
Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks
The proposed method explores a novel dynamical way for structure learning from scratch in SNNs which could build a bridge to close the gap between deep learning and bio-inspired neural dynamics.
Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation
Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, close to the biological neural systems.
A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning
In recent years, spiking neural networks (SNNs) have been used in reinforcement learning (RL) due to their low power consumption and event-driven features.
Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks
Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low energy consumption.
SPAIC: A Spike-based Artificial Intelligence Computing Framework
In this work, we present a Python based spiking neural network (SNN) simulation and training framework, aka SPAIC that aims to support brain-inspired model and algorithm researches integrated with features from both deep learning and neuroscience.
Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization
By evaluating on challenging datasets including CIFAR-10, CIFAR- 100 and ImageNet, the proposed method demonstrates the state-of-the-art performance in terms of accuracy, latency and energy preservation.
Human-Level Control through Directly-Trained Deep Spiking Q-Networks
Specifically, we propose a directly-trained deep spiking reinforcement learning architecture based on the Leaky Integrate-and-Fire (LIF) neurons and Deep Q-Network (DQN).
Successive POI Recommendation via Brain-inspired Spatiotemporal Aware Representation
POI vector representation (embedding) is the core of successive POI recommendation.
Indoor Lighting Estimation Using an Event Camera
Image-based methods for indoor lighting estimation suffer from the problem of intensity-distance ambiguity.
Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning
They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing.
Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks
Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditional frame-based cameras.
Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons
This paper proposes an unsupervised address event representation (AER) object recognition approach.
Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning
Our framework is a unifying system with a consistent integration of three major functional parts which are sparse encoding, efficient learning and robust readout.
Spiking Deep Residual Network
SNNs theoretically have at least the same computational power as traditional artificial neural networks (ANNs).
Connections Between Nuclear Norm and Frobenius Norm Based Representations
A lot of works have shown that frobenius-norm based representation (FNR) is competitive to sparse representation and nuclear-norm based representation (NNR) in numerous tasks such as subspace clustering.
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image.
A Unified Framework for Representation-based Subspace Clustering of Out-of-sample and Large-scale Data
In this paper, we propose a unified framework which makes representation-based subspace clustering algorithms feasible to cluster both out-of-sample and large-scale data.
Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i. e., intra-subspace data points).
