A computational model for generation of the P300 evoked potential component
A computational model for generation of the P300 evoked potential component
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DOI：
10.1142/s0219635212500215
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发表时间：
2012-09-01
影响因子：
1.8
通讯作者：
Jansen, Ben H.
中科院分区：
文献类型：
--
作者：
Bonala, Bharat K.;Jansen, Ben H.
P300 是一种内源性诱发电位，其幅度和潜伏期取决于刺激所携带的信息量，而不是其物理特征。有人认为P300是人类工作记忆中上下文更新机制的体现。我们提出了一种基于神经网络的模型，该模型模仿人类工作记忆中外部刺激的学习和遗忘机制，该机制被认为是 P300 生成的原因。设计了 Hebbian 学习规则的修改版本来控制网络的权重动态。通过将模拟 P300 的特征与实际实验结果（例如 P300 幅度与刺激概率之间的关系以及任务相关性）进行比较来验证模型。结果表明，所提出的 P300 模型模仿了负责 P300 生成的神经系统的许多方面。
The P300 is an endogenously evoked potential with amplitude and latency depending on the amount of information carried by the stimulus rather than its physical characteristics. It has been suggested that P300 is a manifestation of the context updating mechanism in the human working memory. We present a neural network-based model that mimics the learning and forgetting mechanisms of external stimuli in the human working memory that are believed to be responsible for P300 generation. A modified version of the Hebbian learning rule has been devised to govern the weight dynamics of the network. The model was validated by comparing the characteristics of simulated P300 with actual experimental findings such as the relationship between P300 amplitude and stimulus probability, and task relevance. The results show that the proposed P300 model mimics many aspects of the nervous system responsible for P300 generation.