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Found 13 papers, 5 papers with code
Biologically-Plausible Topology Improved Spiking Actor Network for Efficient Deep Reinforcement Learning
Diverging from the conventional direct linear weighted sum, the BPT-SAN models the local nonlinearities of dendritic trees within the inter-layer connections.
Local Convolution Enhanced Global Fourier Neural Operator For Multiscale Dynamic Spaces Prediction
One of the most notable methods is the Fourier Neural Operator (FNO), which is inspired by Green's function method and approximate operator kernel directly in the frequency domain.
Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transforms
The results indicate that compared to the SOTA Spikformer with SSA, Spikformer with LT achieves higher Top-1 accuracy on neuromorphic datasets (i. e., CIFAR10-DVS and DVS128 Gesture) and comparable Top-1 accuracy on static datasets (i. e., CIFAR-10 and CIFAR-100).
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.
Mixture of personality improved Spiking actor network for efficient multi-agent cooperation
Inspired by this two-step psychology theory, we propose a biologically plausible mixture of personality (MoP) improved spiking actor network (SAN), whereby a determinantal point process is used to simulate the complex formation and integration of different types of personality in MoP, and dynamic and spiking neurons are incorporated into the SAN for the efficient reinforcement learning.
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition
The spiking neural network (SNN) using leaky-integrated-and-fire (LIF) neurons has been commonly used in automatic speech recognition (ASR) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network
Learning from the interaction is the primary way biological agents know about the environment and themselves.
Motif-topology improved Spiking Neural Network for the Cocktail Party Effect and McGurk Effect
Here, we propose a Motif-topology improved SNN (M-SNN) for the efficient multi-sensory integration and cognitive phenomenon simulations.
Motif-topology and Reward-learning improved Spiking Neural Network for Efficient Multi-sensory Integration
Network architectures and learning principles are key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs).
Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning
Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state representation from two different scales: input coding and neuronal coding.
Quantum Superposition Inspired Spiking Neural Network
Despite advances in artificial intelligence models, neural networks still cannot achieve human performance, partly due to differences in how information is encoded and processed compared to human brain.
Tuning Convolutional Spiking Neural Network with Biologically-plausible Reward Propagation
The performance of the proposed BRP-SNN is further verified on the spatial (including MNIST and Cifar-10) and temporal (including TIDigits and DvsGesture) tasks, where the SNN using BRP has reached a similar accuracy compared to other state-of-the-art BP-based SNNs and saved 50% more computational cost than ANNs.
Finite Meta-Dynamic Neurons in Spiking Neural Networks for Spatio-temporal Learning
Spiking Neural Networks (SNNs) have incorporated more biologically-plausible structures and learning principles, hence are playing critical roles in bridging the gap between artificial and natural neural networks.
