Data_Sheet_1_Bottlenecks, Modularity, and the Neural Control of Behavior.PDF

In almost all animals, the transfer of information from the brain to the motor circuitry is facilitated by a relatively small number of neurons, leading to a constraint on the amount of information that can be transmitted. Our knowledge of how animals encode information through this pathway, and the consequences of this encoding, however, is limited. In this study, we use a simple feed-forward neural network to investigate the consequences of having such a bottleneck and identify aspects of the network architecture that enable robust information transfer. We are able to explain some recently observed properties of descending neurons—that they exhibit a modular pattern of connectivity and that their excitation leads to consistent alterations in behavior that are often dependent upon the desired behavioral state of the animal. Our model predicts that in the presence of an information bottleneck, such a modular structure is needed to increase the efficiency of the network and to make it more robust to perturbations. However, it does so at the cost of an increase in state-dependent effects. Despite its simplicity, our model is able to provide intuition for the trade-offs faced by the nervous system in the presence of an information processing constraint and makes predictions for future experiments.

几乎所有动物中，大脑向运动神经环路传递信息的过程均由数量相对较少的神经元介导，这使得可传输的信息量受到限制。然而，目前学界对于动物通过该神经通路编码信息的机制，以及此类编码所产生的后续影响，仍所知有限。本研究采用简单的前馈神经网络（feed-forward neural network），探究此类信息瓶颈所带来的影响，并识别出能够实现稳健信息传递的网络架构特征。我们得以阐释近期观测到的下行神经元（descending neurons）的若干特性：其一，它们呈现出模块化的连接模式；其二，其兴奋活动会引发行为的一致性改变，而此类改变往往依赖于动物的预期行为状态。我们的模型预测，在存在信息瓶颈的情况下，此类模块化结构可提升网络的信息传输效率，并增强其对扰动的鲁棒性，但该结构的代价是会增强状态依赖型效应。尽管本模型结构简洁，却能够为神经系统在面临信息处理约束时所面临的权衡取舍提供直观解释，并可为未来的实验研究提供预测方向。