Inspired by the natural nervous system, synaptic plasticity rules are applied
to train spiking neural networks with local information, making them suitable
for online learning on neuromorphic hardware. However, when such rules are
implemented to learn different new tasks, they usually require a significant
amount of work on task-dependent fine-tuning. This paper aims to make this
process easier by employing an evolutionary algorithm that evolves suitable
synaptic plasticity rules for the task at hand. More specifically, we provide a
set of various local signals, a set of mathematical operators, and a global
reward signal, after which a Cartesian genetic programming process finds an
optimal learning rule from these components. Using this approach, we find
learning rules that successfully solve an XOR and cart-pole task, and discover
new learning rules that outperform the baseline rules from literature.

本研究基于大自然神经系统的启发，应用突触可塑性规则训练带有局部信息的脉冲神经网络，使其适用于神经形态硬件的在线学习。其目的在于通过应用进化算法，使得各种不同任务的学习过程变得更加简单，通过提供一组各种局部信号、数学算子和全局奖励信号来找到最佳学习规则，我们发现通过这种方法可以成功解决 XOR 和车杆任务，并发现新的学习规则优于文献中的基准规则。

使用脉冲神经网络演化学习强化学习任务

Evolving-to-Learn Reinforcement Learning Tasks with Spiking Neural Networks

Ongoing studies have identified similarities between neural representations
in biological networks and in deep artificial neural networks. This has led to
renewed interest in developing analogies between the backpropagation learning
algorithm used to train artificial networks and the synaptic plasticity rules
operative in the brain. These efforts are challenged by biologically
implausible features of backpropagation, one of which is a reliance on
symmetric forward and backward synaptic weights. A number of methods have been
proposed that do not rely on weight symmetry but, thus far, these have failed
to scale to deep convolutional networks and complex data. We identify principal
obstacles to the scalability of such algorithms and introduce several
techniques to mitigate them. We demonstrate that a modification of the feedback
alignment method that enforces a weaker form of weight symmetry, one that
requires agreement of weight sign but not magnitude, can achieve performance
competitive with backpropagation. Our results complement those of Bartunov et
al. (2018) and Xiao et al. (2018b) and suggest that mechanisms that promote
alignment of feedforward and feedback weights are critical for learning in deep
networks.

研究深度人工神经网络中的反向传播学习算法与大脑神经元突触可塑性规律的类比，介绍了不依赖于对称前向和后向突触权重的算法，提出通过加强权重符号一致性的反馈对准法的修改，可以实现与反向传播法相当的性能。这些研究结果表明，促进前向和反馈权重对准的机制对于深度网络的学习是至关重要的。