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Found 13 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.
YAYI 2: Multilingual Open-Source Large Language Models
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence.
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.
Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks
This shift in focus from weight adjustment to mastering the intricacies of synaptic change offers a more flexible and dynamic pathway for neural networks to evolve and adapt.
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.
Multi-compartment Neuron and Population Encoding improved Spiking Neural Network for Deep Distributional Reinforcement Learning
In this paper, we proposed a brain-inspired SNN-based deep distributional reinforcement learning algorithm with combination of bio-inspired multi-compartment neuron (MCN) model and population coding method.
A Brain-inspired Memory Transformation based Differentiable Neural Computer for Reasoning-based Question Answering
Reasoning and question answering as a basic cognitive function for humans, is nevertheless a great challenge for current artificial intelligence.
Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks
The proposed DPAP model considers multiple biologically realistic mechanisms (such as dendritic spine dynamic plasticity, activity-dependent neural spiking trace, and local synaptic plasticity), with the addition of an adaptive pruning strategy, so that the network structure can be dynamically optimized during learning without any pre-training and retraining.
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.
BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation
These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions.
