2 citations · 6 across the 9 of their papers we have counts for
9 papers
Continual Learning of Multiple Cognitive Functions with Brain-inspired Temporal Development Mechanism
Bing Han, Feifei Zhao, Yinqian Sun +2
Cognitive functions in current artificial intelligence networks are tied to the exponential increase in network scale, whereas the human brain can continuously learn hundreds of co…
Evolving Efficient Genetic Encoding for Deep Spiking Neural Networks
Wenxuan Pan, Feifei Zhao, Bing Han +2
By exploiting discrete signal processing and simulating brain neuron communication, Spiking Neural Networks (SNNs) offer a low-energy alternative to Artificial Neural Networks (ANN…
Brain-inspired and Self-based Artificial Intelligence
Yi Zeng, Feifei Zhao, Yuxuan Zhao +17
The question "Can machines think?" and the Turing Test to assess whether machines could achieve human-level intelligence is one of the roots of AI. With the philosophical argument…
Brain-inspired Evolutionary Architectures for Spiking Neural Networks
Wenxuan Pan, Feifei Zhao, Zhuoya Zhao +1
The complex and unique neural network topology of the human brain formed through natural evolution enables it to perform multiple cognitive functions simultaneously. Automated evol…
Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks
Bing Han, Feifei Zhao, Wenxuan Pan +4
The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most existing continual learning algorithms…
Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks
Bing Han, Feifei Zhao, Yi Zeng +2
Children possess the ability to learn multiple cognitive tasks sequentially, which is a major challenge toward the long-term goal of artificial general intelligence. Existing conti…
Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks
Wenxuan Pan, Feifei Zhao, Guobin Shen +1
Spiking Neural Networks (SNNs) have received considerable attention not only for their superiority in energy efficiency with discrete signal processing but also for their natural s…
Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks
Wenxuan Pan, Feifei Zhao, Yi Zeng +1
The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spi…
Adaptive Sparse Structure Development with Pruning and Regeneration for Spiking Neural Networks
Bing Han, Feifei Zhao, Yi Zeng +1
Spiking Neural Networks (SNNs) are more biologically plausible and computationally efficient. Therefore, SNNs have the natural advantage of drawing the sparse structural plasticity…