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CNN models of receptive field dynamics of the central visual system neurons

机译:中央视野神经元接受场动态的CNN模型

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Deals with the biological aspects of the receptive field (RF) concept and its possible cellular neural network (CNN) modeling. Three kinds of receptive field definitions are discussed: the experimentally measured RF, the mathematical model of the RF and its anatomical background. Previously, new RF-mapping techniques have revealed that neurons in the visual pathway exhibit striking RF dynamics, which implies that for adequate characterization the RF profile has to be examined in the space-time domain. Starting from these findings in the present study the neurons' static RF definition is purified and some experimental results of De Angelis et al. (1995) are modeled by the CNN. Our CNN model indicates that the spatio-temporal RF dynamics can be generated by time invariant synaptic strength values.
机译:涉及接受领域(RF)概念的生物方面及其可能的细胞神经网络(CNN)建模。讨论了三种接受场定义:实验测量的RF,RF的数学模型及其解剖背景。以前,新的RF映射技术揭示了视觉途径中的神经元表现出尖锐的RF动力学,这意味着对于足够的表征,必须在空间域中检查RF曲线。从本研究开始,神经元的静态RF定义是纯化的,并且De Aggelis等人的一些实验结果。 (1995)由CNN建模。我们的CNN模型表示可以通过时间不变突触强度值产生时空RF动态。

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