Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands, 6525 AJ
School of Life Sciences, University of Sussex, Brighton, United Kingdom, BN1 9QG.
J Neurosci. 2021 Feb 10;41(6):1251-1264. doi: 10.1523/JNEUROSCI.2503-20.2020. Epub 2020 Dec 22.
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. 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糊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涌现属性的基础，例如知觉组织和决策。尽管对神经竞争进行了大量的理论研究，但还没有可用的实验工具来允许对竞争神经元进行神经生理学研究。我们开发了一种“混合”系统，其中真实的神经元和计算机模拟的神经网络相互作用。这使我们能够在两个真实的锥体神经元之间构建一个相互抑制电路。然后，我们询问这种最小的神经竞争单元表现出什么样的动态，并将其与已知的神经竞争的行为水平动态进行比较。我们发现，当通过电流注入同时激活一对神经元时，它们表现出双稳态。添加模拟的突触噪声和激活强度的变化表明，该电路的动态与已知的双稳态视觉感知的特性非常相似：主导持续时间的分布呈现右偏形状，并且激活强度的变化导致主导地位，主导持续时间和反转率的变化，正如双稳态感知的著名经验法则（称为 Levelt 的命题）所述。视觉感知是作为涉及竞争神经元选择过程的主动组织视觉信号的神经系统的结果而出现的。虽然由“相互抑制”电路实现的神经竞争在许多理论研究中已经得到了检验，但它在真实神经元中的特性尚未得到研究。我们已经开发了一种“混合”系统，其中一只老鼠脑切片中的两个真实的锥体神经元通过计算机模拟的相互抑制电路相互作用。我们发现，神经元的同时激活导致双稳态活动。我们研究了噪声的影响和激活强度变化对动力学的影响。我们观察到，这对神经元表现出的动力学与已知的双稳态视觉感知的特性非常相似。
