基于钙信号的突触可塑性规则，让脉冲神经网络同时利用发放时间和频率学习
Learning in Spiking Neural Networks with a Calcium-based Hebbian Rule for Spike-timing-dependent Plasticity
- 用钙信号追踪神经活动，构建结合时间与频率的脉冲可塑性规则
- 在MNIST上实现手写数字识别，且学习率随相关发放自动调节
- 首次实现时间与速率互补驱动网络连接优化，适合低功耗计算研究者
理解生物神经网络如何通过局部可塑性机制塑造，有助于构建节能且自适应的信息处理系统，有望缓解边缘计算中的部分瓶颈。尽管生物学中同时利用脉冲发放时间与平均发放率来调节突触强度，但多数模型仅关注其中之一。本文提出一种基于钙信号追踪神经活动的赫布式局部学习规则，能够重现神经科学实验中关于脉冲时间与脉冲率协议的结果。进一步地，我们使用该模型在MNIST手写数字数据集上训练脉冲神经网络，揭示学习真实世界模式所需的机制。结果表明，该模型对相关脉冲活动敏感，可自动调节网络学习率，而无需改变神经元平均发放率或学习规则超参数。据我们所知，这是首个展示脉冲时间与发放率在塑造脉冲神经网络连接中具有互补作用的工作。
原文摘要 · Abstract (English)
Understanding how biological neural networks are shaped via local plasticity mechanisms can lead to energy-efficient and self-adaptive information processing systems, which promises to mitigate some of the current roadblocks in edge computing systems. While biology makes use of spikes to seamless use both spike timing and mean firing rate to modulate synaptic strength, most models focus on one of the two. In this work, we present a Hebbian local learning rule that models synaptic modification as a function of calcium traces tracking neuronal activity. We show how the rule reproduces results from spike time and spike rate protocols from neuroscientific studies. Moreover, we use the model to train spiking neural networks on MNIST digit recognition to show and explain what sort of mechanisms are needed to learn real-world patterns. We show how our model is sensitive to correlated spiking activity and how this enables it to modulate the learning rate of the network without altering the mean firing rate of the neurons nor the hyparameters of the learning rule. To the best of our knowledge, this is the first work that showcases how spike timing and rate can be complementary in their role of shaping the connectivity of spiking neural networks.
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