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Found 6 papers, 5 papers with code
QKFormer: Hierarchical Spiking Transformer using Q-K Attention
ii) We incorporate the hierarchical structure, which significantly benefits the performance of both the brain and artificial neural networks, into spiking transformers to obtain multi-scale spiking representation.
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties.
Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli
Taken together, our work is the first to apply deep recurrent SNNs to model the mouse visual cortex under movie stimuli and we establish that these networks are competent to capture both static and dynamic representations and make contributions to understanding the movie information processing mechanisms of the visual cortex.
Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse
However, they highly simplify the computational properties of neurons compared to their biological counterparts.
Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations
PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator.
DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation
In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner.
