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Visual number sense in untrained deep neural networks

机译:未经培训的深神经网络中的视觉数量感

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Number sense, the ability to estimate numerosity, is observed in na?ve animals, but how this cognitive function emerges in the brain remains unclear. Here, using an artificial deep neural network that models the ventral visual stream of the brain, we show that number-selective neurons can arise spontaneously, even in the complete absence of learning. We also show that the responses of these neurons can induce the abstract number sense, the ability to discriminate numerosity independent of low-level visual cues. We found number tuning in a randomly initialized network originating from a combination of monotonically decreasing and increasing neuronal activities, which emerges spontaneously from the statistical properties of bottom-up projections. We confirmed that the responses of these number-selective neurons show the single- and multineuron characteristics observed in the brain and enable the network to perform number comparison tasks. These findings provide insight into the origin of innate cognitive functions.
机译:数量感,估计数量的能力,在Na ve动物中观察到,这种认知功能如何在大脑中仍然不清楚。在这里,使用模拟大脑腹侧视觉流的人造深神经网络,我们表明,即使在完全没有学习的情况下,也可以自发地出现数量选择性神经元。我们还表明,这些神经元的响应可以诱导抽象号码,区分差异独立于低级视觉线索的能力。我们发现源自单调减少和增加神经元活动的组合的随机初始化网络中的数字调整,从而从自下而上投影的统计性质自发地出现。我们证实,这些数量选择性神经元的响应显示了在大脑中观察到的单一和多阵度特性,并使网络能够执行数字比较任务。这些发现提供了对先天认知功能的起源的洞察力。

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