Barranca Victor J, Bhuiyan Asha, Sundgren Max, Xing Fangzhou
Department of Mathematics and Statistics, Swarthmore College, Swarthmore, PA, United States.
Front Neurosci. 2022 Feb 28;16:801847. doi: 10.3389/fnins.2022.801847. eCollection 2022.
The notion that a neuron transmits the same set of neurotransmitters at all of its post-synaptic connections, typically known as Dale's law, is well supported throughout the majority of the brain and is assumed in almost all theoretical studies investigating the mechanisms for computation in neuronal networks. Dale's law has numerous functional implications in fundamental sensory processing and decision-making tasks, and it plays a key role in the current understanding of the structure-function relationship in the brain. However, since exceptions to Dale's law have been discovered for certain neurons and because other biological systems with complex network structure incorporate individual units that send both positive and negative feedback signals, we investigate the functional implications of network model dynamics that violate Dale's law by allowing each neuron to send out both excitatory and inhibitory signals to its neighbors. We show how balanced network dynamics, in which large excitatory and inhibitory inputs are dynamically adjusted such that input fluctuations produce irregular firing events, are theoretically preserved for a single population of neurons violating Dale's law. We further leverage this single-population network model in the context of two competing pools of neurons to demonstrate that effective decision-making dynamics are also produced, agreeing with experimental observations from honeybee dynamics in selecting a food source and artificial neural networks trained in optimal selection. Through direct comparison with the classical two-population balanced neuronal network, we argue that the one-population network demonstrates more robust balanced activity for systems with less computational units, such as honeybee colonies, whereas the two-population network exhibits a more rapid response to temporal variations in network inputs, as required by the brain. We expect this study will shed light on the role of neurons violating Dale's law found in experiment as well as shared design principles across biological systems that perform complex computations.
神经元在其所有突触后连接中传递同一组神经递质的观点，通常被称为戴尔定律，在大脑的大部分区域都得到了充分支持，并且几乎所有研究神经网络计算机制的理论研究都假定了这一点。戴尔定律在基本的感觉处理和决策任务中有许多功能上的影响，并且在当前对大脑结构 - 功能关系的理解中起着关键作用。然而，由于已经发现某些神经元存在戴尔定律的例外情况，并且因为其他具有复杂网络结构的生物系统包含发送正反馈和负反馈信号的单个单元，所以我们研究了违反戴尔定律的网络模型动力学的功能影响，即允许每个神经元向其邻居发送兴奋性和抑制性信号。我们展示了平衡网络动力学，其中大的兴奋性和抑制性输入被动态调整，使得输入波动产生不规则的放电事件，在理论上对于违反戴尔定律的单个神经元群体是如何得以保留的。我们进一步在两个相互竞争的神经元池的背景下利用这个单群体网络模型，以证明也能产生有效的决策动力学，这与蜜蜂在选择食物源时的动力学实验观察结果以及经过最优选择训练的人工神经网络相符合。通过与经典的双群体平衡神经元网络进行直接比较，我们认为对于像蜂群这样计算单元较少的系统，单群体网络表现出更稳健的平衡活动，而双群体网络则如大脑所要求的那样，对网络输入的时间变化表现出更快的响应。我们期望这项研究将阐明实验中发现的违反戴尔定律的神经元的作用，以及跨执行复杂计算的生物系统共享的设计原则。
