Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal.
Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal.
J Vis. 2023 Jul 3;23(7):18. doi: 10.1167/jov.23.7.18.
The activity of neurons is influenced by random fluctuations and can be strongly modulated by firing rate adaptation, particularly in sensory systems. Still, there is ongoing debate about the characteristics of neuronal noise and the mechanisms of adaptation, and even less is known about how exactly they affect perception. Noise and adaptation are critical in binocular rivalry, a visual phenomenon where two images compete for perceptual dominance. Here, we investigated the effects of different noise processes and adaptation mechanisms on visual perception by simulating a model of binocular rivalry with Gaussian white noise, Ornstein-Uhlenbeck noise, and pink noise, in variants with divisive adaptation, subtractive adaptation, and without adaptation. By simulating the nine models in parameter space, we find that white noise only produces rivalry when paired with subtractive adaptation and that subtractive adaptation reduces the influence of noise intensity on rivalry strength and introduces convergence of the mean percept duration, an important metric of binocular rivalry, across all noise processes. In sum, our results show that white noise is an insufficient description of background activity in the brain and that subtractive adaptation is a stronger and more general switching mechanism in binocular rivalry than divisive adaptation, with important noise-filtering properties.
神经元的活动受到随机波动的影响,并可通过发放率适应强烈调节,特别是在感觉系统中。然而,神经元噪声的特征和适应机制仍存在争议,对于它们如何确切地影响感知,人们知之甚少。噪声和适应是双眼竞争(一种两个图像争夺感知主导地位的视觉现象)中的关键因素。在这里,我们通过模拟带有高斯白噪声、Ornstein-Uhlenbeck 噪声和粉红噪声的双眼竞争模型,以及带有除法适应、减法适应和无适应的变体,研究了不同噪声过程和适应机制对视觉感知的影响。通过在参数空间中模拟九个模型,我们发现只有在与减法适应相结合时,白噪声才会产生竞争,而减法适应会降低噪声强度对竞争强度的影响,并在所有噪声过程中引入平均感知持续时间的收敛,这是双眼竞争的一个重要度量。总之,我们的结果表明,白噪声是对大脑背景活动的一种不充分描述,而减法适应是比除法适应更强、更通用的双眼竞争开关机制,具有重要的噪声滤波特性。