Optimal sensorimotor integration in recurrent cortical networks:: A neural implementation of Kalman filters
Optimal sensorimotor integration in recurrent cortical networks:: A neural implementation of Kalman filters
复制标题
DOI：
10.1523/jneurosci.3985-06.2007
复制
发表时间：
2007-05-23
影响因子：
5.3
通讯作者：
Pouget, Alexandre
中科院分区：
文献类型：
--
作者：
Deneve, Sophie;Duhamel, Jean-Rene;Pouget, Alexandre
一些行为实验表明，神经系统使用身体动态的内部模型来实现对卡尔曼滤波器的近似。该滤波器可用于近乎最优地执行各种任务，例如预测运动动作的感觉结果，整合感觉和身体姿势信号，以及计算运动命令。我们建议，这种卡尔曼滤波器的神经实现涉及经常性的基函数网络与吸引子动态，一种架构，可以很容易地映射到皮层电路。在这样的网络中，对诸如手臂速度等变量的调谐曲线是非常不恒定的，因为给定神经元的调谐曲线的幅度和宽度可以根据诸如手臂的位置或感觉反馈的可靠性等其他变量而发生很大变化。这一特性可以解释运动皮层和前运动皮层中调谐曲线的一些令人困惑的特性，并导致了几个新的预测。
Several behavioral experiments suggest that the nervous system uses an internal model of the dynamics of the body to implement a close approximation to a Kalman filter. This filter can be used to perform a variety of tasks nearly optimally, such as predicting the sensory consequence of motor action, integrating sensory and body posture signals, and computing motor commands. We propose that the neural implementation of this Kalman filter involves recurrent basis function networks with attractor dynamics, a kind of architecture that can be readily mapped onto cortical circuits. In such networks, the tuning curves to variables such as arm velocity are remarkably noninvariant in the sense that the amplitude and width of the tuning curves of a given neuron can vary greatly depending on other variables such as the position of the arm or the reliability of the sensory feedback. This property could explain some puzzling properties of tuning curves in the motor and premotor cortex, and it leads to several new predictions.