Graduate Program in Computational Neuroscience and the Department of Neurobiology, The University of Chicago, Chicago, Illinois, United States of America.
Center for Theoretical Neuroscience and Mortimer B. Zuckerman Mind, Brain and Behavior Institute, Columbia University, New York, New York, United States of America.
PLoS Comput Biol. 2023 Aug 9;19(8):e1011327. doi: 10.1371/journal.pcbi.1011327. eCollection 2023 Aug.
A pedestrian crossing a street during rush hour often looks and listens for potential danger. When they hear several different horns, they localize the cars that are honking and decide whether or not they need to modify their motor plan. How does the pedestrian use this auditory information to pick out the corresponding cars in visual space? The integration of distributed representations like these is called the assignment problem, and it must be solved to integrate distinct representations across but also within sensory modalities. Here, we identify and analyze a solution to the assignment problem: the representation of one or more common stimulus features in pairs of relevant brain regions-for example, estimates of the spatial position of cars are represented in both the visual and auditory systems. We characterize how the reliability of this solution depends on different features of the stimulus set (e.g., the size of the set and the complexity of the stimuli) and the details of the split representations (e.g., the precision of each stimulus representation and the amount of overlapping information). Next, we implement this solution in a biologically plausible receptive field code and show how constraints on the number of neurons and spikes used by the code force the brain to navigate a tradeoff between local and catastrophic errors. We show that, when many spikes and neurons are available, representing stimuli from a single sensory modality can be done more reliably across multiple brain regions, despite the risk of assignment errors. Finally, we show that a feedforward neural network can learn the optimal solution to the assignment problem, even when it receives inputs in two distinct representational formats. We also discuss relevant results on assignment errors from the human working memory literature and show that several key predictions of our theory already have support.
行人和穿过街道时，往往会在高峰时段观察和倾听潜在的危险。当他们听到几个不同的喇叭声时，他们会定位鸣喇叭的汽车，并决定是否需要修改他们的驾驶计划。行人如何使用这种听觉信息来在视觉空间中挑选出相应的汽车？这种分布式表示的整合被称为分配问题，它必须解决，以整合不同的表示形式跨越但也在感官模式内。在这里，我们确定并分析了分配问题的解决方案：在相关脑区对一个或多个共同刺激特征的表示——例如，对汽车空间位置的估计在视觉和听觉系统中都有表示。我们描述了这种解决方案的可靠性如何取决于刺激集的不同特征（例如，集合的大小和刺激的复杂性）以及分裂表示的细节（例如，每个刺激表示的精度和重叠信息量）。接下来，我们在一个具有生物合理性的感受野代码中实现了这个解决方案，并展示了这个代码对神经元和尖峰数量的限制如何迫使大脑在局部和灾难性错误之间进行权衡。我们表明，当有大量的尖峰和神经元可用时，尽管存在分配错误的风险，但是可以在多个大脑区域中更可靠地表示来自单一感觉模式的刺激。最后，我们表明，即使在接收两个不同表示格式的输入时，前馈神经网络也可以学习分配问题的最佳解决方案。我们还讨论了来自人类工作记忆文献的相关分配错误结果，并表明我们的理论已经有了一些关键预测的支持。
