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广东十分钟开奖十选五:Internal representation of hippocampal neuronal population spans a time-distance continuum
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The hippocampus organizes experience in sequences of events that form episodic memory. How are time and space internally computed in the hippocampus in the absence of sequential external inputs? Here we show that time and space are integrated together within the hippocampal network with different degrees of tuning across days. This was found by recording the activity of hundreds of pyramidal cells for several days. We also propose a mechanism supporting such spatiotemporal integration based on a ring attractor network model: the degree of tuning between space and time can be adjusted by modulating the power of a nonsequential external excitatory drive. In this way, the hippocampus is able to generate a spatiotemporal representation tuned to the task at hand.
The hippocampus plays a critical role in episodic memory: the sequential representation of visited places and experienced events. This function is mirrored by hippocampal activity that self organizes into sequences of neuronal activation that integrate spatiotemporal information. What are the underlying mechanisms of such integration is still unknown. Single cell activity was recently shown to combine time and distance information; however, it remains unknown whether a degree of tuning between space and time can be defined at the network level. Here, combining daily calcium imaging of CA1 sequence dynamics in running head-fixed mice and network modeling, we show that CA1 network activity tends to represent a specific combination of space and time at any given moment, and that the degree of tuning can shift within a continuum from 1 day to the next. Our computational model shows that this shift in tuning can happen under the control of the external drive power. We propose that extrinsic global inputs shape the nature of spatiotemporal integration in the hippocampus at the population level depending on the task at hand, a hypothesis which may guide future experimental studies.
?1C.H. and D.A.-G. contributed equally to this work.
- ?2To whom correspondence may be addressed. Email: or .
Author contributions: C.H., D.A.-G., A.T., R.C., and A.M. designed research; C.H., D.A.-G., V.V., and S.R. performed research; C.H., D.A.-G., and A.M. analyzed data; and C.H., D.A.-G., and A.M. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: All data and codes used in analysis and for the model are available on GitHub https://github.com/CarolineHaimerl27/duration_distance_coding.git.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1718518116/-/DCSupplemental.
Published under the PNAS license.