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Found 7 papers, 5 papers with code
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.
Recent Advances and New Frontiers in Spiking Neural Networks
In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven characteristics that naturally fit the neuromorphic hardware.
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.
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.
