Poster Presentation

Contributed Talk Session: Tuesday, August 4, 2:00 – 3:00 pm, Skirball Theater
Poster E in Poster Session E: Thursday, August 6, 10:30 am – 12:15 pm, Kimmel Center, Shorin & Rosenthal Rooms

Memory Subspaces for Temporal Locations Across Timescales in the Monkey Precuneus

Zhiyong Jin¹, Ning Su², Aakash Sarkar³, Xufeng Zhou¹, Jiayu Cheng⁴, Makoto Kusunoki⁵, Sze Chai Kwok¹; ¹Duke Kunshan University, ²East China Normal University, ³University of California, San Francisco, ⁴Wuhan University, ⁵University of Oxford

Presenter: Sze Chai Kwok

Understanding how the brain organizes the temporal structure of experience across timescales is central to episodic memory. Here, we examine neural population dynamics in primate precuneus during a temporal order judgment task spanning seconds to minutes, and cross-day retrieval of intertwined episodes. We recorded large-scale population activity (∼3,000 neurons) across 3 days and analyzed it using generalized linear models and low-dimensional manifold methods. Single neurons showed consistent positive modulation for temporal locations (TLs) across both 2-s and 1-min delay conditions during retrieval. At the population level, neural activity formed structured low-dimensional manifolds in dPCA space, maintaining a subspace that persistently encoded the TLs of extracted frames throughout the TOJ period. Correct trials exhibited coherent, synchronized dynamics across different TLs, while behavioral errors were associated with manifold collapse and loss of dynamical synchrony. During cross-day retrieval, episodic states corresponding to the TL of clips viewed across both days rapidly separated into day-specific clusters within a shared state space, an effect absent in control conditions. The chronologically recent Day-2 subspace occupied a larger representational volume, corresponding to higher memory performance compared to Day-1 clips (70.9 vs. 59.1%). These results suggest that episodic time is encoded as a dynamically maintained neural manifold whose geometric integrity supports accurate memory decisions.

Topic Area: Memory, Learning & Knowledge Structures