Meta BCI : Hippocampus-striatum network inspired architecture towards flexible BCI

Minryung R. Song, Sang Wan Lee
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引用次数: 2

Abstract

Classifying neural signals is a crucial step in the brain-computer interface (BCI). Although Deep Neural Network (DNN) has been shown to be surprisingly good at classification, DNN suffers from long training time and catastrophic forgetting. Catastrophic forgetting refers to a phenomenon in which a DNN tends to forget previously learned task when it learns a new task. Here we argue that the solution to this problem may be found in the human brain, specifically, by combining functions of the two regions: the striatum and the hippocampus, which is pivotal for reinforcement learning and memory recall relevant to the current context, respectively. The mechanism of these brain regions provides insights into resolving catastrophic forgetting and long training time of DNNs. Referring to the hippocampus-striatum network we discuss design principles of combining different types of DNNs for building a new BCI architecture, called “Meta BCI”.
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Meta脑机接口:海马体-纹状体网络启发的灵活脑机接口架构
神经信号分类是脑机接口(BCI)的关键步骤。尽管深度神经网络(DNN)已被证明具有惊人的分类能力,但DNN存在训练时间长和灾难性遗忘的问题。灾难性遗忘是指深度神经网络在学习新任务时倾向于忘记之前学习过的任务的现象。在这里,我们认为解决这个问题的方法可以在人脑中找到,具体来说,通过结合纹状体和海马体这两个区域的功能,这两个区域分别是与当前环境相关的强化学习和记忆回忆的关键。这些脑区的作用机制为解决dnn的灾难性遗忘和长时间训练提供了新的思路。参考海马体-纹状体网络,我们讨论了结合不同类型的dnn构建新的脑机接口架构的设计原则,称为“Meta脑机接口”。
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