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统一海马下托功能:一种预测图谱方法。

Unifying Subicular Function: A Predictive Map Approach.

作者信息

Bennett Lauren, de Cothi William, Muessig Laurenz, Rodrigues Fábio R, Cacucci Francesca, Wills Tom J, Sun Yanjun, Giocomo Lisa M, Lever Colin, Poulter Steven, Barry Caswell

机构信息

Department of Cell and Developmental Biology, University College London, London, UK.

Department of Neuroscience, Physiology & Pharmacology, University College London, London, UK.

出版信息

bioRxiv. 2024 Nov 7:2024.11.06.622306. doi: 10.1101/2024.11.06.622306.

DOI:10.1101/2024.11.06.622306
PMID:39574744
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11580870/
Abstract

The successor representation has emerged as a powerful model for understanding mammalian navigation and memory; explaining the spatial coding properties of hippocampal place cells and entorhinal grid cells. However, the diverse spatial responses of subicular neurons, the primary output of the hippocampus, have eluded a unified account. Here, we demonstrate that incorporating rodent behavioural biases into the successor representation successfully reproduces the heterogeneous activity patterns of subicular neurons. This framework accounts for the emergence of boundary and corner cells; neuronal types absent in upstream hippocampal regions. We provide evidence that subicular firing patterns are more accurately described by the successor representation than a purely spatial or boundary vector cell model of subiculum. Our work offers a unifying theory of subicular function that positions the subiculum, more than other hippocampal regions, as a predictive map of the environment.

摘要

后继表征已成为理解哺乳动物导航和记忆的强大模型;它解释了海马体位置细胞和内嗅皮层网格细胞的空间编码特性。然而,海马体主要输出区域——下托神经元的多样空间反应,却一直未能得到统一的解释。在这里,我们证明,将啮齿动物的行为偏差纳入后继表征,能够成功再现下托神经元的异质性活动模式。该框架解释了边界细胞和角落细胞的出现;这些神经元类型在上游海马区域中并不存在。我们提供的证据表明,与下托的纯空间或边界向量细胞模型相比,后继表征能更准确地描述下托的放电模式。我们的研究提出了一种关于下托功能的统一理论,该理论认为,与其他海马区域相比,下托更是环境的预测地图。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbad/11580870/2a1570931d04/nihpp-2024.11.06.622306v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbad/11580870/b81d93962f76/nihpp-2024.11.06.622306v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbad/11580870/08c8c25a03a5/nihpp-2024.11.06.622306v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbad/11580870/75f0a2dda833/nihpp-2024.11.06.622306v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbad/11580870/98c013eab358/nihpp-2024.11.06.622306v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbad/11580870/2a1570931d04/nihpp-2024.11.06.622306v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbad/11580870/b81d93962f76/nihpp-2024.11.06.622306v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbad/11580870/08c8c25a03a5/nihpp-2024.11.06.622306v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbad/11580870/75f0a2dda833/nihpp-2024.11.06.622306v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbad/11580870/98c013eab358/nihpp-2024.11.06.622306v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbad/11580870/2a1570931d04/nihpp-2024.11.06.622306v1-f0005.jpg

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本文引用的文献

1
Identifying representational structure in CA1 to benchmark theoretical models of cognitive mapping.识别海马体CA1区的表征结构,以衡量认知地图理论模型。
Neuron. 2025 Jan 22;113(2):307-320.e5. doi: 10.1016/j.neuron.2024.10.027. Epub 2024 Nov 22.
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Predictive Representations: Building Blocks of Intelligence.预测表示:智能的基石。
Neural Comput. 2024 Oct 11;36(11):2225-2298. doi: 10.1162/neco_a_01705.
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Subicular neurons encode concave and convex geometries.海兔神经元对凹面和凸面几何形状进行编码。
Nature. 2024 Mar;627(8005):821-829. doi: 10.1038/s41586-024-07139-z. Epub 2024 Mar 6.
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RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments.盒子里的老鼠,一个用于模拟连续环境中运动和神经元活动的工具包。
Elife. 2024 Feb 9;13:e85274. doi: 10.7554/eLife.85274.
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Environment geometry alters subiculum boundary vector cell receptive fields in adulthood and early development.环境几何形状改变成年和早期发育中海马下托区边界向量细胞感受野。
Nat Commun. 2024 Feb 1;15(1):982. doi: 10.1038/s41467-024-45098-1.
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Neural learning rules for generating flexible predictions and computing the successor representation.用于生成灵活预测和计算后继表示的神经学习规则。
Elife. 2023 Mar 16;12:e80680. doi: 10.7554/eLife.80680.
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Rapid learning of predictive maps with STDP and theta phase precession.具有 STDP 和 theta 相位进动的预测图的快速学习。
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Learning predictive cognitive maps with spiking neurons during behavior and replays.在行为和重放期间使用尖峰神经元学习预测性认知图。
Elife. 2023 Mar 16;12:e80671. doi: 10.7554/eLife.80671.
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Predictive maps in rats and humans for spatial navigation.大鼠和人类空间导航的预测图。
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State transitions in the statistically stable place cell population correspond to rate of perceptual change.在统计稳定的位置细胞群体中,状态转变对应于知觉变化率。
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