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Found 7 papers, 1 papers with code
Brain-inspired and Self-based Artificial Intelligence
In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm.
Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks
In this paper, we propose a brain-inspired continual learning algorithm with adaptive reorganization of neural pathways, which employs Self-Organizing Regulation networks to reorganize the single and limited Spiking Neural Network (SOR-SNN) into rich sparse neural pathways to efficiently cope with incremental tasks.
Brain-inspired Evolutionary Architectures for Spiking Neural Networks
This work explores brain-inspired neural architectures suitable for SNNs and also provides preliminary insights into the evolutionary mechanisms of biological neural networks in the human brain.
Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks
In addition, the overlapping shared structure helps to quickly leverage all acquired knowledge to new tasks, empowering a single network capable of supporting multiple incremental tasks (without the separate sub-network mask for each task).
Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks
The neural motifs topology, modular regional structure and global cross-brain region connection of the human brain are the product of natural evolution and can serve as a perfect reference for designing brain-inspired SNN architecture.
Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks
For structural evolution, an adaptive evolvable LSM model is developed to optimize the neural architecture design of liquid layer with separation property.
Adaptive Sparse Structure Development with Pruning and Regeneration for Spiking Neural Networks
Experimental results on spatial (MNIST, CIFAR-10) and temporal neuromorphic (N-MNIST, DVS-Gesture) datasets demonstrate that our method can flexibly learn appropriate compression rate for various tasks and effectively achieve superior performance while massively reducing the network energy consumption.
