Spatial transformations in the parietal cortex using basis functions
Spatial transformations in the parietal cortex using basis functions
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DOI：
10.1162/jocn.1997.9.2.222
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发表时间：
1997-03-01
影响因子：
3.2
通讯作者：
Sejnowski, TJ
中科院分区：
文献类型：
--
作者：
Pouget, A;Sejnowski, TJ
感觉运动变换是感觉输入到运动反应的非线性映射。我们在这里探索的可能性，顶叶皮层的单个神经元的反应作为这些转换的基础功能。基函数分解是一种通用的非线性函数逼近方法，具有计算效率高、适应性强等优点。特别是，单个顶叶神经元的响应可以近似为视网膜位置的高斯函数和眼睛位置的S形函数的乘积，称为增益场。这样的函数的一个大集合形成可以用于通过直接投影执行任意运动响应的基集。我们比较了这一假设与其他方法，通常用于人口模型的代码，如计算地图和矢量表示。这两种方法都不能完全解释顶叶神经元的反应，并且它们对于非线性变换的计算效率较低。基函数还具有不依赖于任何坐标系或参考系的优点。因此，一个物体的位置可以同时在多个参考系中表示，这一特性与顶叶皮层病变的偏侧脑患者的行为一致。
Sensorimotor transformations are nonlinear mappings of sensory inputs to motor responses. We explore here the possibility that the responses of single neurons in the parietal cortex serve as basis functions for these transformations. Basis function decomposition is a general method for approximating nonlinear functions that is computationally efficient and well suited for adaptive modification. In particular, the responses of single parietal neurons can be approximated by the product of a Gaussian function of retinal location and a sigmoid function of eye position, called a gain field. A large set of such functions forms a basis set that can be used to perform an arbitrary motor response through a direct projection. We compare this hypothesis with other approaches that are commonly used to model population codes, such as computational maps and vectorial representations. Neither of these alternatives can fully account for the responses of parietal neurons, and they are computationally less efficient for nonlinear transformations. Basis functions also have the advantage of not depending on any coordinate system or reference frame. As a consequence, the position of an object can be represented in multiple reference frames simultaneously, a property consistent with the behavior of hemineglect patients with lesions in the parietal cortex.