65 citations · 78 across the 7 of their papers we have counts for
5 papers · 1 filter
Synaptic Dynamics Realize First-order Adaptive Learning and Weight Symmetry
Yukun Yang, Peng Li
Gradient-based first-order adaptive optimization methods such as the Adam optimizer are prevalent in training artificial networks, achieving the state-of-the-art results. This work…
A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation
Yukun Yang, Peng Li
Several recent studies attempt to address the biological implausibility of the well-known backpropagation (BP) method. While promising methods such as feedback alignment, direct fe…
BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks
Yukun Yang, Peng Li
Our brain consists of biological neurons encoding information through accurate spike timing, yet both the architecture and learning rules of our brain remain largely unknown. Compa…
Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks
Yukun Yang, Wenrui Zhang, Peng Li
While backpropagation (BP) has been applied to spiking neural networks (SNNs) achieving encouraging results, a key challenge involved is to backpropagate a continuous-valued loss o…
Temporal Surrogate Back-propagation for Spiking Neural Networks
Yukun Yang
Spiking neural networks (SNN) are usually more energy-efficient as compared to Artificial neural networks (ANN), and the way they work has a great similarity with our brain. Back-p…