编码
海马结构
子空间拓扑
计算机科学
齿状回
神经科学
线性子空间
灵活性(工程)
运动前神经元活动
基础(线性代数)
拓扑(电路)
海马体
大脑活动与冥想
转化(遗传学)
异步通信
神经网络
神经元回路
生物神经网络
下游(制造业)
算法
线性地图
理论计算机科学
结合属性
联动装置(软件)
上游(联网)
电子线路
平衡(能力)
作者
Joaquín González,Mihály Vöröslakos,Deren Aykan,Nina Soto,Noam Nitzan,Rachel Swanson,Mürsel Karadas,Zhe Chen,György Buzsáki
出处
期刊:Nature
[Nature Portfolio]
日期:2026-05-13
标识
DOI:10.1038/s41586-026-10481-z
摘要
The capacity of hippocampal circuits to transform inputs into downstream outputs is fundamental to navigation and memory, yet the circuit-level mechanisms that enable this flexibility in adapting to experience remain unclear. Here we approach this problem by performing large-scale (up to 1,024 channel) recordings across the hippocampal–retrosplenial cortex (RSC) circuit in behaving mice, enabling simultaneous access to spiking activity in dentate gyrus (DG), CA3, CA2, CA1 and RSC. On the basis of a linear dimensionality-reduction technique known as partial canonical correlation analysis, we identify low-dimensional communication subspaces1 between two regions while accounting for influences from a third area. These subspaces captured distinct input–output transformations in the CA1 region, linking upstream hippocampal activity (DG, CA3 and CA2) to downstream cortical targets (RSC). Intrinsic firing properties and anatomical location constrained subspace memberships—members were mapped to deep sublayers of the CA3–CA1–RSC axis during both spatial and non-spatial tasks. These subspaces could recombine overlapping neuronal pools to support distinct interareal interactions across changing experiences and brain states. Reactivation patterns of CA1–CA3 subspaces, but not those of CA1–RSC, during post-experience sleep correlated with replay, reflecting a plasticity–stability balance in the input–output transformation along the hippocampal–retrosplenial axis. Our findings suggest a model in which hippocampal–neocortical communication reconfigures predetermined circuit motifs to flexibly encode experiences. Hippocampal–neocortical communication reconfigures predetermined circuit motifs to flexibly encode experiences.
科研通智能强力驱动
Strongly Powered by AbleSci AI