Related Experiment Video
Updated: Feb 5, 2026

Longitudinal Two-Photon Imaging of Dorsal Hippocampal CA1 in Live Mice
Published on: June 19, 2019
Hippocampal CA1 gamma power predicts the precision of spatial memory judgments
Rebecca F Stevenson1,2, Jie Zheng3, Lilit Mnatsakanyan4
1Department of Neurobiology and Behavior, University of California, Irvine, CA 92697.
Abstract:
The hippocampus plays a critical role in spatial memory. However, the exact neural mechanisms underlying high-fidelity spatial memory representations are unknown. We report findings from presurgical epilepsy patients with bilateral hippocampal depth electrodes performing an object-location memory task that provided a broad range of spatial memory precision. During encoding, patients were shown a series of objects along the circumference of an invisible circle. At test, the same objects were shown at the top of the circle (0°), and patients used a dial to move the object to its location shown during encoding. Angular error between the correct location and the indicated location was recorded as a continuous measure of performance. By registering pre- and postimplantation MRI scans, we were able to localize the electrodes to specific hippocampal subfields. We found a correlation between increased gamma power, thought to reflect local excitatory activity, and the precision of spatial memory retrieval in hippocampal CA1 electrodes. Additionally, we found a similar relationship between gamma power and memory precision in the dorsolateral prefrontal cortex and a directional relationship between activity in this region and in the CA1, suggesting that the dorsolateral prefrontal cortex is involved in postretrieval processing. These results indicate that local processing in hippocampal CA1 and dorsolateral prefrontal cortex supports high-fidelity spatial memory representations.
More Related Videos
Related Concept Videos
Uncertainty in Measurement: Accuracy and Precision
Predicting Molecular Geometry
Power
System of Memory
Working Memory
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

