论文标题
使用片上可塑性对Loihi进行序列学习和整合
Sequence Learning and Consolidation on Loihi using On-chip Plasticity
论文作者
论文摘要
在这项工作中，我们开发了一种关于神经形态硬件的预测学习模型。我们的模型使用Loihi芯片的片上可塑性功能来记住观察到的事件序列，并使用此内存来实时生成对未来事件的预测。鉴于片上可塑性规则的局部限制，生成预测而不干扰正在进行的学习过程是不平凡的。我们通过以海马重放启发的内存整合方法来应对这一挑战。序列存储器使用峰值依赖性可塑性存储在初始存储模块中。后来，在离线期间，记忆被整合到一个不同的预测模块中。然后，第二个模块能够代表预测的未来事件，而不会干扰第一个模块中的活动和可塑性，从而可以在线比较预测和地面真实观察。我们的模型是概念证明，可以用片上可塑性将在线预测学习模型部署在神经形态硬件上。
In this work we develop a model of predictive learning on neuromorphic hardware. Our model uses the on-chip plasticity capabilities of the Loihi chip to remember observed sequences of events and use this memory to generate predictions of future events in real time. Given the locality constraints of on-chip plasticity rules, generating predictions without interfering with the ongoing learning process is nontrivial. We address this challenge with a memory consolidation approach inspired by hippocampal replay. Sequence memory is stored in an initial memory module using spike-timing dependent plasticity. Later, during an offline period, memories are consolidated into a distinct prediction module. This second module is then able to represent predicted future events without interfering with the activity, and plasticity, in the first module, enabling online comparison between predictions and ground-truth observations. Our model serves as a proof-of-concept that online predictive learning models can be deployed on neuromorphic hardware with on-chip plasticity.
