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Found 6 papers, 1 papers with code
Converting High-Performance and Low-Latency SNNs through Explicit Modelling of Residual Error in ANNs
One of the mainstream approaches to implementing deep SNNs is the ANN-SNN conversion, which integrates the efficient training strategy of ANNs with the energy-saving potential and fast inference capability of SNNs.
Deep Learning for Visual Neuroprosthesis
The visual pathway involves complex networks of cells and regions which contribute to the encoding and processing of visual information.
Spike timing reshapes robustness against attacks in spiking neural networks
The success of deep learning in the past decade is partially shrouded in the shadow of adversarial attacks.
ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks
To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training.
Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks
The proposed method explores a novel dynamical way for structure learning from scratch in SNNs which could build a bridge to close the gap between deep learning and bio-inspired neural dynamics.
Biologically Plausible Variational Policy Gradient with Spiking Recurrent Winner-Take-All Networks
One stream of reinforcement learning research is exploring biologically plausible models and algorithms to simulate biological intelligence and fit neuromorphic hardware.
