利用生物神经网络模型比较个体化和知觉平均化的时间动态

Rakesh Sengupta, Anuj Shukla, Ravichander Janapati, Bhavesh Verma
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引用次数: 0

摘要

分析视觉场景和计算集合统计(即感知平均)对于认知主体的稳定感官体验至关重要。尽管在场景中应用滤波器表面上很简单,但我们的大脑却要在不同参照系(视网膜、空间和半球)之间无缝切换总结和个别化,这给我们带来了挑战。在本研究中,我们探索了神经网络在个性化和概括化之间动态切换的能力。我们选择的计算模型是一个全连接的中心外循环神经网络,以前曾用于枚举/个体化,它展示了同时提取摘要统计数据和实现高个体化准确性的潜力。值得注意的是,我们的研究结果表明,在 100 毫秒的呈现持续时间内,个体化准确率可以达到接近完美的水平,但总结准确率却并非如此。我们还展示了该网络的空间变化激发版本,它可以解释许多有趣的时空感知模式。这些发现不仅凸显了这种神经网络的可行性,而且还提供了对集合感知时空动态的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative temporal dynamics of individuation and perceptual averaging using a biological neural network model
Analyzing visual scenes and computing ensemble statistics, known as perceptual averaging, is crucial for the stable sensory experience of a cognitive agent. Despite the apparent simplicity of applying filters to scenes, the challenge arises from our brain’s seamless transition between summarization and individuation across various reference frames (retinotopic, spatiotopic, and hemispheric). In this study, we explore the capability of a neural network to dynamically switch between individuation and summarization. Our chosen computational model, a fully connected on-center off-surround recurrent neural network previously employed for enumeration/individuation, demonstrates the potential to extract both summary statistics and achieve high individuation accuracy. Notably, our results show that the individuation accuracy can reach close to perfection within a presentation duration of 100 ms, but not so for summarization. We have also shown a spatially varying excitation version of the network that can explain quite a few interesting spatio-temporal patterns of perception. These findings not only highlight the feasibility of such a neural network but also provide insights into the temporal dynamics of ensemble perception.
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CiteScore
3.30
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