Mohsenzadeh Yalda, Dash Suryadeep, Crawford J Douglas
York Center for Vision Research, Canadian Action and Perception Network, York University Toronto, ON, Canada.
York Center for Vision Research, Canadian Action and Perception Network, York UniversityToronto, ON, Canada; Department of Physiology and Pharmacology, Robarts Research Institute, Western UniversityLondon, ON, Canada.
Front Syst Neurosci. 2016 May 12;10:39. doi: 10.3389/fnsys.2016.00039. eCollection 2016.
In the oculomotor system, spatial updating is the ability to aim a saccade toward a remembered visual target position despite intervening eye movements. Although this has been the subject of extensive experimental investigation, there is still no unifying theoretical framework to explain the neural mechanism for this phenomenon, and how it influences visual signals in the brain. Here, we propose a unified state-space model (SSM) to account for the dynamics of spatial updating during two types of eye movement; saccades and smooth pursuit. Our proposed model is a non-linear SSM and implemented through a recurrent radial-basis-function neural network in a dual Extended Kalman filter (EKF) structure. The model parameters and internal states (remembered target position) are estimated sequentially using the EKF method. The proposed model replicates two fundamental experimental observations: continuous gaze-centered updating of visual memory-related activity during smooth pursuit, and predictive remapping of visual memory activity before and during saccades. Moreover, our model makes the new prediction that, when uncertainty of input signals is incorporated in the model, neural population activity and receptive fields expand just before and during saccades. These results suggest that visual remapping and motor updating are part of a common visuomotor mechanism, and that subjective perceptual constancy arises in part from training the visual system on motor tasks.
在动眼系统中，空间更新是指尽管存在中间的眼球运动，仍能将扫视瞄准记忆中的视觉目标位置的能力。尽管这已成为广泛实验研究的主题，但仍然没有一个统一的理论框架来解释这种现象的神经机制，以及它如何影响大脑中的视觉信号。在这里，我们提出一个统一的状态空间模型（SSM）来解释两种眼球运动（扫视和平滑跟踪）过程中空间更新的动态变化。我们提出的模型是一个非线性SSM，通过双扩展卡尔曼滤波器（EKF）结构中的递归径向基函数神经网络实现。使用EKF方法依次估计模型参数和内部状态（记忆的目标位置）。所提出的模型复制了两个基本的实验观察结果：在平滑跟踪过程中与视觉记忆相关活动的连续注视中心更新，以及在扫视之前和期间视觉记忆活动的预测性重映射。此外，我们的模型做出了新的预测，即当模型中纳入输入信号的不确定性时，神经群体活动和感受野会在扫视之前和期间扩展。这些结果表明，视觉重映射和运动更新是一个共同的视觉运动机制的一部分，并且主观感知恒常性部分源于在运动任务上对视觉系统的训练。
