论文标题
通过Hebbian可塑性，富含内存的计算和学习在尖峰神经网络中
Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity
论文作者
论文摘要
记忆是生物神经系统的关键组成部分，可以使信息保留在大量的时间尺度上，范围从数百毫秒到数年。尽管据信Hebbian可塑性在生物记忆中起关键作用，但到目前为止，它主要是在模式完成和无监督学习的背景下进行的。在这里，我们建议Hebbian可塑性对于生物神经系统的计算至关重要。我们介绍了一种新颖的尖峰神经网络结构，该建筑富含Hebbian突触可塑性。我们表明，Hebbian富集在其计算和学习能力方面令人惊讶地吸引了神经网络。它提高了他们的分数概括，一次性学习，跨模式生成协会，语言处理和基于奖励的学习能力。由于尖峰神经网络是节能神经形态硬件的基础，因此这也表明可以根据该原理建立强大的认知神经形态系统。
Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning. Here, we propose that Hebbian plasticity is fundamental for computations in biological neural systems. We introduce a novel spiking neural network architecture that is enriched by Hebbian synaptic plasticity. We show that Hebbian enrichment renders spiking neural networks surprisingly versatile in terms of their computational as well as learning capabilities. It improves their abilities for out-of-distribution generalization, one-shot learning, cross-modal generative association, language processing, and reward-based learning. As spiking neural networks are the basis for energy-efficient neuromorphic hardware, this also suggests that powerful cognitive neuromorphic systems can be build based on this principle.
