Guigon Emmanuel
INSERM U483, Universitacuté Pierre et Marie Curie, Paris, France.
J Cogn Neurosci. 2004 Apr;16(3):382-9. doi: 10.1162/089892904322926728.
Unlike most artificial systems, the brain is able to face situations that it has not learned or even encountered before. This ability is not in general echoed by the properties of most neural networks. Here, we show that neural computation based on least-square error learning between populations of intensity-coded neurons can explain interpolation and extrapolation capacities of the nervous system in sensorimotor and cognitive tasks. We present simulations for function learning experiments, auditory-visual behavior, and visuomotor transformations. The results suggest that induction in human behavior, be it sensorimotor or cognitive, could arise from a common neural associative mechanism.
与大多数人工系统不同，大脑能够面对它从未学习过甚至从未遇到过的情况。大多数神经网络的特性通常无法反映这种能力。在此，我们表明，基于强度编码神经元群体之间最小二乘误差学习的神经计算可以解释神经系统在感觉运动和认知任务中的内插和外推能力。我们展示了函数学习实验、视听行为和视觉运动转换的模拟。结果表明，人类行为中的归纳，无论是感觉运动还是认知方面，都可能源于一种共同的神经关联机制。
