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基于学习目标动态的灵长类动物平稳跟踪模型。

A model of smooth pursuit in primates based on learning the target dynamics.

作者信息

Shibata Tomohiro, Tabata Hiromitsu, Schaal Stefan, Kawato Mitsuo

机构信息

Metalearing and Neuromodulation, CREST, Japan Science and Technology Corporation, Kyoto, Japan.

出版信息

Neural Netw. 2005 Apr;18(3):213-24. doi: 10.1016/j.neunet.2005.01.001. Epub 2005 Mar 29.

Abstract

While the predictive nature of the primate smooth pursuit system has been evident through several behavioural and neurophysiological experiments, few models have attempted to explain these results comprehensively. The model we propose in this paper in line with previous models employing optimal control theory; however, we hypothesize two new issues: (1) the medical superior temporal (MST) area in the cerebral cortex implements a recurrent neural network (RNN) in order to predict the current or future target velocity, and (2) a forward model of the target motion is acquired by on-line learning. We use stimulation studies to demonstrate how our new model supports these hypotheses.

摘要

虽然通过多项行为和神经生理学实验已证明灵长类动物的平稳跟踪系统具有预测性,但很少有模型试图全面解释这些结果。我们在本文中提出的模型与之前采用最优控制理论的模型一致;然而,我们假设了两个新问题:(1)大脑皮层中的内侧颞上叶(MST)区域实现了一个递归神经网络(RNN),以便预测当前或未来的目标速度,以及(2)通过在线学习获取目标运动的前向模型。我们使用刺激研究来证明我们的新模型如何支持这些假设。

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