海马体递归网络模型中的条形码活动可实现高效记忆绑定

Ching Fang, Jack Lindsey, Dmitriy Aronov, LF Abbott, Selmaan N Chettih
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引用次数: 0

摘要

形成外显记忆需要将在一次经历中同时出现的不同元素结合在一起。这一过程的一种模式是,代表记忆不同组成部分的神经元与 "索引"--该记忆独有的神经元子集--结合在一起。最近在雏鸟身上发现了这种模式的证据,雏鸟利用海马记忆来存储和回忆食物的存放位置。雏鸟的海马产生稀疏的高维模式("条形码"),独特地指明了每个贮藏事件。意想不到的是,参与条形码的神经元也表现出传统的位置调谐。目前还不清楚条形码活动是如何产生的,以及它在记忆形成和检索过程中发挥了什么作用。此外,记忆索引(如条形码)如何在代表记忆内容(如位置)的同一神经群中发挥作用也不清楚。在这里,我们设计了一个在生物学上看似合理的模型,它能生成条形码并用条形码绑定经验内容。我们的模型通过递归神经网络的混沌动力学从位置输入生成条形码,并利用海比可塑性将条形码存储为吸引子状态。该模型与实验观察结果相吻合,即记忆指数(条形码)和内容信号(地点调谐)在单个神经元的活动中随机混合。我们证明,条形码能减少相关经验之间的记忆干扰。我们还证明,地点调谐与条形码起着互补作用,可实现灵活的、与上下文相适应的记忆检索。最后,我们的模型与以往海马产生预测图的模型是一致的。网络的预测和索引功能是通过调整全局递归增益来实现的。我们的研究结果表明,海马可以利用条形码来解决一般记忆系统中记忆特异性(模式分离)和灵活回忆(模式完成)之间的基本矛盾。
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Barcode activity in a recurrent network model of the hippocampus enables efficient memory binding
Forming an episodic memory requires binding together disparate elements that co-occur in a single experience. One model of this process is that neurons representing different components of a memory bind to an "index" --- a subset of neurons unique to that memory. Evidence for this model has recently been found in chickadees, which use hippocampal memory to store and recall locations of cached food. Chickadee hippocampus produces sparse, high-dimensional patterns ("barcodes") that uniquely specify each caching event. Unexpectedly, the same neurons that participate in barcodes also exhibit conventional place tuning. It is unknown how barcode activity is generated, and what role it plays in memory formation and retrieval. It is also unclear how a memory index (e.g. barcodes) could function in the same neural population that represents memory content (e.g. place). Here, we design a biologically plausible model that generates barcodes and uses them to bind experiential content. Our model generates barcodes from place inputs through the chaotic dynamics of a recurrent neural network and uses Hebbian plasticity to store barcodes as attractor states. The model matches experimental observations that memory indices (barcodes) and content signals (place tuning) are randomly intermixed in the activity of single neurons. We demonstrate that barcodes reduce memory interference between correlated experiences. We also show that place tuning plays a complementary role to barcodes, enabling flexible, contextually-appropriate memory retrieval. Finally, our model is compatible with previous models of the hippocampus as generating a predictive map. Distinct predictive and indexing functions of the network are achieved via an adjustment of global recurrent gain. Our results suggest how the hippocampus may use barcodes to resolve fundamental tensions between memory specificity (pattern separation) and flexible recall (pattern completion) in general memory systems.
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