Department of Animal Sciences, The Hebrew University of Jerusalem, Rehovot, Israel.
Department of Cognitive and Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
Sci Rep. 2024 Aug 6;14(1):18228. doi: 10.1038/s41598-024-68003-8.
The brain's extraordinary abilities are often attributed to its capacity to learn and adapt. But memory has its limitations, especially when faced with tasks such as retrieving thousands of food items-a common behavior in scatter-hoarding animals. Here, we propose a brain mechanism that may facilitate caching and retrieval behaviors, with a focus on hippocampal spatial cells. Rather than memorizing the locations of their caches, as previously hypothesized, we suggest that cache-hoarding animals employ a static mechanism akin to hash functions commonly used in computing. Our mathematical model aligns with the activity of hippocampal spatial cells, which respond to an animal's positional attention. We know that the region that activates each spatial cell remains consistent across subsequent visits to the same area but not between areas. This remapping, combined with the uniqueness of cognitive maps, produces persistent hash functions that can serve both food caching and retrieval. We present a simple neural network architecture that can generate such a probabilistic hash that is unique to the animal and not sensitive to environmental changes. This mechanism could serve a virtually boundless capacity for the encoding of any structured data.
大脑的非凡能力通常归因于其学习和适应的能力。但是记忆有其局限性，尤其是在面对检索数千种食物等任务时——这是分散贮藏动物的常见行为。在这里，我们提出了一种可能促进缓存和检索行为的大脑机制，重点是海马体空间细胞。我们不建议像以前假设的那样，贮藏动物通过记忆其缓存的位置来工作，而是建议它们采用类似于计算中常用的哈希函数的静态机制。我们的数学模型与海马体空间细胞的活动相吻合，这些细胞对动物的位置注意力做出反应。我们知道，在随后访问同一区域时，激活每个空间细胞的区域保持一致，但在不同区域之间则不一致。这种重映射，再加上认知地图的独特性，产生了持久的哈希函数，可以同时用于食物缓存和检索。我们提出了一种简单的神经网络架构，可以生成对动物而言是独特的、不受环境变化影响的概率哈希。这种机制可以为任何结构化数据的编码提供几乎无限的容量。
