Department of Physics, Emory University, Atlanta, Georgia, United States of America.
Initiative in Theory and Modeling of Living Systems, Emory University, Atlanta, Georgia, United States of America.
PLoS Comput Biol. 2020 May 7;16(5):e1007875. doi: 10.1371/journal.pcbi.1007875. eCollection 2020 May.
Modern recording methods enable sampling of thousands of neurons during the performance of behavioral tasks, raising the question of how recorded activity relates to theoretical models. In the context of decision making, functional connectivity between choice-selective cortical neurons was recently reported. The straightforward interpretation of these data suggests the existence of selective pools of inhibitory and excitatory neurons. Computationally investigating an alternative mechanism for these experimental observations, we find that a randomly connected network of excitatory and inhibitory neurons generates single-cell selectivity, patterns of pairwise correlations, and the same ability of excitatory and inhibitory populations to predict choice, as in experimental observations. Further, we predict that, for this task, there are no anatomically defined subpopulations of neurons representing choice, and that choice preference of a particular neuron changes with the details of the task. We suggest that distributed stimulus selectivity and functional organization in population codes could be emergent properties of randomly connected networks.
现代记录方法使得在执行行为任务期间可以对数千个神经元进行采样，这就提出了一个问题，即记录的活动如何与理论模型相关。在决策背景下，最近报道了选择选择性皮质神经元之间的功能连接。这些数据的直接解释表明存在选择性的抑制性和兴奋性神经元池。通过计算研究这些实验观察的替代机制，我们发现兴奋性和抑制性神经元的随机连接网络会产生单细胞选择性、成对相关性模式，以及兴奋性和抑制性群体预测选择的相同能力，就像在实验观察中一样。此外，我们预测对于这项任务，不存在代表选择的神经元的解剖定义亚群，并且特定神经元的选择偏好会随任务细节而变化。我们认为，在群体代码中，分布式刺激选择性和功能组织可能是随机连接网络的涌现特性。
