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基于神经形态传感系统的皮层运动计算的神经机制

Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System.

作者信息

Abdul-Kreem Luma Issa, Neumann Heiko

机构信息

Institute for Neural Information Processing, Ulm University, Ulm, Germany.

Control and Systems Engineering Department, University of Technology, Baghdad, Iraq.

出版信息

PLoS One. 2015 Nov 10;10(11):e0142488. doi: 10.1371/journal.pone.0142488. eCollection 2015.

Abstract

The visual cortex analyzes motion information along hierarchically arranged visual areas that interact through bidirectional interconnections. This work suggests a bio-inspired visual model focusing on the interactions of the cortical areas in which a new mechanism of feedforward and feedback processing are introduced. The model uses a neuromorphic vision sensor (silicon retina) that simulates the spike-generation functionality of the biological retina. Our model takes into account two main model visual areas, namely V1 and MT, with different feature selectivities. The initial motion is estimated in model area V1 using spatiotemporal filters to locally detect the direction of motion. Here, we adapt the filtering scheme originally suggested by Adelson and Bergen to make it consistent with the spike representation of the DVS. The responses of area V1 are weighted and pooled by area MT cells which are selective to different velocities, i.e. direction and speed. Such feature selectivity is here derived from compositions of activities in the spatio-temporal domain and integrating over larger space-time regions (receptive fields). In order to account for the bidirectional coupling of cortical areas we match properties of the feature selectivity in both areas for feedback processing. For such linkage we integrate the responses over different speeds along a particular preferred direction. Normalization of activities is carried out over the spatial as well as the feature domains to balance the activities of individual neurons in model areas V1 and MT. Our model was tested using different stimuli that moved in different directions. The results reveal that the error margin between the estimated motion and synthetic ground truth is decreased in area MT comparing with the initial estimation of area V1. In addition, the modulated V1 cell activations shows an enhancement of the initial motion estimation that is steered by feedback signals from MT cells.

摘要

视觉皮层沿着通过双向互连进行交互的分层排列的视觉区域分析运动信息。这项工作提出了一种受生物启发的视觉模型,该模型专注于皮层区域的交互作用,其中引入了一种新的前馈和反馈处理机制。该模型使用了一种神经形态视觉传感器(硅视网膜),它模拟了生物视网膜的尖峰生成功能。我们的模型考虑了两个主要的模型视觉区域,即V1和MT,它们具有不同的特征选择性。在模型区域V1中,使用时空滤波器来局部检测运动方向,从而估计初始运动。在这里,我们采用了最初由阿德尔森和伯根提出的滤波方案,并使其与DVS的尖峰表示相一致。V1区域的响应由对不同速度(即方向和速度)具有选择性的MT细胞进行加权和汇总。这种特征选择性在这里是从时空域中的活动组合中得出的,并在更大的时空区域(感受野)上进行整合。为了考虑皮层区域的双向耦合,我们在反馈处理中匹配两个区域的特征选择性属性。对于这种联系,我们沿着特定的首选方向对不同速度的响应进行整合。在空间和特征域上对活动进行归一化,以平衡模型区域V1和MT中单个神经元的活动。我们的模型使用在不同方向移动的不同刺激进行了测试。结果表明,与V1区域的初始估计相比,MT区域中估计运动与合成地面真值之间的误差幅度减小了。此外,调制后的V1细胞激活显示出初始运动估计的增强,这是由MT细胞的反馈信号引导的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20fc/4640561/e4a0eaf752ef/pone.0142488.g001.jpg

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