Characteristics of CA1 place fields in a complex maze with multiple choice points. Issue 2 (8th November 2017)

• Record Type: Journal Article
• Authors: Tanila, H.; Ku, S.; Kloosterman, F.; Wilson, M. A.
• Is Part Of: Hippocampus. Volume 28:Issue 2(2018)
• Page Start: 81
• Page End: 96
• Publication Date: 2017-11-08
• Subjects: detour -- navigation -- place cell -- spatial learning -- spatial memory; Hippocampus (Brain) -- Periodicals; 612.825
• DOI: 10.1002/hipo.22810
• Languages: English
• ISSNs: 1050-9631

Abstract

Abstract: For the sake of rigorous control of task variables, hippocampal place cells have been usually studied in relatively simple environments. To approach the situation of real‐life navigation in an urban‐like environment, we recorded CA1 place cells while rats performance a memory task in a "Townmaze" with two start locations, three alternate paths in the maze midsection, followed by a two‐way choice that determined the trial outcome, access to a goal compartment. Further, to test the ability of place cells to update their spatial representation upon local changes in the environment while maintaining the integrity of the overall spatial map to allow effective navigation, we occasionally introduced barriers in the maze mid‐section to force the rat to select a nonpreferred route. The "Townmaze" revealed many new interesting features of CA1 neurons. First, we found neurons with 3–5 fields that appear to represent segments on a single common route through the maze. Second, we found neurons with 3–5 fields similarly aligned along the longitudinal or transverse maze axis. Responses to the barriers were assessed separately near and far from the barriers. Appearance of new fields in response to the barriers took place almost exclusively only locally near the barrier, whereas in‐field firing rate changes occurred throughout the maze. Further, field location changes did not correlate with the task performance, whereas firing rate changes did. These findings suggest that in a complex environment with blocked distal views, CA1 neurons code for the environment as sequences of significant nodes but are also capable of extracting and associating common elements across these sequences.