论文标题
在尖峰神经网络中使用神经调节的突触可塑性学习在线学习
Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks
论文作者
论文摘要
我们建议为了利用对神经科学对机器学习的理解，我们必须首先拥有强大的工具来训练类似大脑的学习模型。尽管在理解大脑学习动态方面取得了重大进展，但神经科学衍生的学习模型尚未证明与深度学习（例如梯度下降）方法相同的性能能力。受到使用梯度下降的机器学习成功的启发，我们证明了神经调节神经科学的突触可塑性的模型可以在尖峰神经网络（SNN）中培训，其中具有学习框架，以通过梯度下降来解决挑战性的在线学习问题。该框架为开发神经科学启发的在线学习算法开辟了一条新的途径。
We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Although substantial progress has been made toward understanding the dynamics of learning in the brain, neuroscience-derived models of learning have yet to demonstrate the same performance capabilities as methods in deep learning such as gradient descent. Inspired by the successes of machine learning using gradient descent, we demonstrate that models of neuromodulated synaptic plasticity from neuroscience can be trained in Spiking Neural Networks (SNNs) with a framework of learning to learn through gradient descent to address challenging online learning problems. This framework opens a new path toward developing neuroscience inspired online learning algorithms.
