Keeping track of the number of times different stimuli have been experienced is a critical computation for behavior. Here, we propose a theoretical two-layer neural circuit that stores counts of stimulus occurrence frequencies. This circuit implements a data structure, called a count sketch, that is commonly used in computer science to maintain item frequencies in streaming data. Our first model implements a count sketch using Hebbian synapses and outputs stimulus-specific frequencies. Our second model uses anti-Hebbian plasticity and only tracks frequencies within four count categories (“1-2-3-many”), which trades-off the number of categories that need to be distinguished with the potential ethological value of those categories. We show how both models can robustly track stimulus occurrence frequencies, thus expanding the traditional novelty-familiarity memory axis from binary to discrete with more than two possible values. Finally, we show that an implementation of the “1-2-3-many” count sketch exists in the insect mushroom body.

记录经历不同刺激的次数是行为的关键计算。在这里，我们提出了一种理论上的两层神经电路，用于存储刺激发生频率的计数。该电路实现了一种称为计数草图的数据结构，该结构通常用于计算机科学中以维护流数据中的项目频率。我们的第一个模型使用赫布突触实现计数草图并输出特定刺激的频率。我们的第二个模型使用反赫布可塑性，仅跟踪四个计数类别（“1-2-3-多”）内的频率，这会权衡需要区分的类别数量与这些类别的潜在行为学价值。我们展示了这两种模型如何能够稳健地跟踪刺激发生频率，从而将传统的新颖性-熟悉性记忆轴从二进制扩展到具有两个以上可能值的离散。最后，我们展示了昆虫蘑菇体内存在“1-2-3-多”计数草图的实现。