Bezerra S, Caron A, Cherruault Y
Laboratoire MEDIMAT, Université Pierre et Marie Curie, Paris, France.
Int J Biomed Comput. 1993 May;32(3-4):181-95. doi: 10.1016/0020-7101(93)90013-v.
The first purpose of this paper is to present a neural net model of the visual cortex of higher vertebrates based on the electrophysiological properties of the ganglion cells. This model takes Hebb's law [1] as the physiological learning rule for synaptic modification. The model consists of 85 x 85 neurons forming a layer similar to the cortex. The neurones are massively connected via weights that are typically adapted. We simulate several input patterns and show that the model reproduces the pattern recognition, contours pictures and moving perception.
本文的首要目的是基于神经节细胞的电生理特性,提出一种高等脊椎动物视觉皮层的神经网络模型。该模型将赫布定律[1]作为突触修饰的生理学习规则。模型由85×85个神经元组成一层,类似于皮层。神经元通过通常经过调整的权重进行大量连接。我们模拟了几种输入模式,并表明该模型能够重现模式识别、轮廓图像和运动感知。