Collective Activity of Many Bistable Assemblies Reproduces Characteristic Dynamics of Multistable Perception
Collective Activity of Many Bistable Assemblies Reproduces Characteristic Dynamics of Multistable Perception
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DOI：
10.1523/jneurosci.4626-15.2016
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发表时间：
2016-06-29
影响因子：
5.3
通讯作者：
Braun, Jochen
中科院分区：
文献类型：
--
作者：
Cao, Robin;Pastukhov, Alexander;Braun, Jochen
感知决策的时间取决于确定性和随机因素，因为感官证据（确定性）的逐渐积累受到感官和/或内部噪声（随机）的污染。当人类观察者看到多稳定的视觉显示时，连续的随机积累事件在视觉外观的反复逆转中达到高潮。将反转时间视为“首次通过时间”问题，我们询问观察到的定时密度如何约束潜在的随机积累。重要的是，平均反转时间（即确定性因素）在显示器/观察者/刺激水平之间差异很大，而反转时间的方差和偏度（即随机因素）保持平均值的特征比例。什么样的随机过程可以重现这种高度一致的“缩放特性”？在这里，我们证明了双稳态单元有限种群的集体活动（即广义Ehrenfest过程）定量地再现了多稳态现象的标度特性的所有方面，与考虑中的其他过程（Poisson， Wiener或Ornstein-Uhlenbeck过程）相反。假设的单位表达了在不同活动状态之间转换的吸引子集合的自发动力学。可能的候选是皮质柱或柱簇，因为它们优先连接并自发地探索有限的活动状态。我们的研究结果表明，感知表征是颗粒状的，概率性的，并且远离平衡，从而为统计推断提供了合适的基础。
The timing of perceptual decisions depends on both deterministic and stochastic factors, as the gradual accumulation of sensory evidence (deterministic) is contaminated by sensory and/or internal noise (stochastic). When human observers view multistable visual displays, successive episodes of stochastic accumulation culminate in repeated reversals of visual appearance. Treating reversal timing as a "first-passage time" problem, we ask how the observed timing densities constrain the underlying stochastic accumulation. Importantly, mean reversal times (i.e., deterministic factors) differ enormously between displays/observers/stimulation levels, whereas the variance and skewness of reversal times (i.e., stochastic factors) keep characteristic proportions of the mean. What sort of stochastic process could reproduce this highly consistent "scaling property?" Here we show that the collective activity of a finite population of bistable units (i.e., a generalized Ehrenfest process) quantitatively reproduces all aspects of the scaling property of multistable phenomena, in contrast to other processes under consideration (Poisson, Wiener, or Ornstein-Uhlenbeck process). The postulated units express the spontaneous dynamics of attractor assemblies transitioning between distinct activity states. Plausible candidates are cortical columns, or clusters of columns, as they are preferentially connected and spontaneously explore a restricted repertoire of activity states. Our findings suggests that perceptual representations are granular, probabilistic, and operate far from equilibrium, thereby offering a suitable substrate for statistical inference.