Neural competition plays an essential role in active selection processes of noisy and ambiguous input signals, and it is assumed to underlie emergent properties of brain functioning, such as perceptual organization and decision-making. Despite ample theoretical research on neural competition, experimental tools to allow neurophysiological investigation of competing neurons have not been available. We developed a "hybrid" system where real-life neurons and a computer-simulated neural circuit interacted. It enabled us to construct a mutual inhibition circuit between two real-life pyramidal neurons. We then asked what dynamics this minimal unit of neural competition exhibits and compared them with the known behavioral-level dynamics of neural competition. We found that the pair of neurons shows bistability when activated simultaneously by current injections. The addition of modeled synaptic noise and changes in the activation strength showed that the dynamics of the circuit are strikingly similar to the known properties of bistable visual perception: The distribution of dominance durations showed a right-skewed shape, and the changes of the activation strengths caused changes in dominance, dominance durations, and reversal rates as stated in the well-known empirical laws of bistable perception known as Levelt's propositions.

SIGNIFICANCE STATEMENT Visual perception emerges as the result of neural systems actively organizing visual signals that involves selection processes of competing neurons. While the neural competition, realized by a "mutual inhibition" circuit has been examined in many theoretical studies, its properties have not been investigated in real neurons. We have developed a "hybrid" system where two real-life pyramidal neurons in a mouse brain slice interact through a computer-simulated mutual inhibition circuit. We found that simultaneous activation of the neurons leads to bistable activity. We investigated the effect of noise and the effect of changes in the activation strength on the dynamics. We observed that the pair of neurons exhibit dynamics strikingly similar to the known properties of bistable visual perception.

神经竞争在嘈杂和模棱两可的输入信号的主动选择过程中起着至关重要的作用，并且被认为是大脑功能的新兴特性的基础，例如感知组织和决策。尽管对神经竞争进行了大量的理论研究，但还没有能够对竞争神经元进行神经生理学研究的实验工具。我们开发了一个“混合”系统，其中真实的神经元和计算机模拟的神经回路相互作用。它使我们能够在两个现实生活中的锥体神经元之间构建一个相互抑制的电路。然后，我们询问这个最小的神经竞争单位表现出什么动态，并将它们与已知的神经竞争行为级动态进行比较。我们发现当电流注入同时激活时，这对神经元表现出双稳态。添加模拟突触噪声和激活强度的变化表明，电路的动力学与双稳态视觉感知的已知特性惊人地相似：优势持续时间的分布呈右偏态，激活强度的变化引起支配性、支配性持续时间和逆转率的变化，如众所周知的双稳态知觉经验法则中所述，称为 Levelt 的命题。

意义陈述视觉感知的出现是神经系统主动组织视觉信号的结果，这些视觉信号涉及竞争神经元的选择过程。虽然在许多理论研究中已经研究了通过“相互抑制”电路实现的神经竞争，但尚未在真实神经元中研究其特性。我们开发了一种“混合”系统，其中小鼠脑切片中的两个真实锥体神经元通过计算机模拟的相互抑制电路进行交互。我们发现同时激活神经元会导致双稳态活动。我们研究了噪声的影响以及激活强度变化对动力学的影响。