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A model of angle selectivity in area V2 with local divisive normalization

机译:具有局部除法归一化的区域V2中的角度选择性模型

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Efficient coding hypothesis states that the goal of sensory system of the brain is to remove redundancies in the sensory input. Several models tried to remove redundancy in the visual input and successfully modeled the functional properties of neurons in the primary visual cortex. However there has been no progress to extend these models to simulate the properties of neurons in extrastriate visual areas. In this paper, we propose that visual cortex tries to remove higher order dependencies in a hierarchical architecture. In each layer a nonlinear mechanism removes redundancies in a local neighborhood. We used the biologically plausible divisive normalization mechanism in a two layer model network to remove nonlinear dependencies in the input. Units in this model can simulate the responses of neurons in area V2 to angle stimuli.
机译:有效的编码假设指出,大脑的感觉系统的目标是消除感觉输入中的冗余。几种模型试图消除视觉输入中的冗余,并成功地对初级视觉皮层中神经元的功能特性进行了建模。但是,扩展这些模型以模拟极视区域的神经元特性尚无进展。在本文中,我们提出视觉皮层试图消除分层体系结构中的更高阶依赖性。在每一层中,非线性机制消除了局部邻域中的冗余。我们在两层模型网络中使用了生物学上可行的除法归一化机制,以消除输入中的非线性依赖性。该模型中的单元可以模拟V2区域中的神经元对角度刺激的响应。

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