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Title: Recordings from hippocampal area CA1, PRE, during and POST novel spatial learning.      
dateReleased:
12-02-2016
privacy:
not applicable
aggregation:
instance of dataset
dateCreated:
12-02-2016
refinement:
analyzed
ID:
doi:10.6080/K0862DC5
creators:
Grosmark, A.D.
Long, J.
Buzsáki, G.
availability:
available
types:
model
description:
This data set is composed of eight bilateral silicon-probe multi-cellular electrophysiological recordings performed on four male Long-Evans rats. These recordings were performed to assess the effect of novel spatial learning on hippocampal CA1 neural firing and LFP patterns in naïve animals. Each session consisted of a long (~4 hour) PRE rest/sleep epoch home-cage recordings performed in a familiar room, followed by a Novel MAZE running epoch (~45 minutes) in which the animals were transferred to a novel room, and water-rewarded to run on a novel maze. These mazes were either A) a wooden 1.6m linear platform, B) a wooden 1m diameter circular platform or C) a 2m metal linear platform. Animals were rewarded either at both ends of the linear platform, or at a predetermined location on the circular platform. The animal was gently encouraged to run unidirectionally on the circular platform. After the MAZE epochs the animals were transferred back to their home-cage in the familiar room where a long (~4 hour) POST rest/sleep was recorded. All eight sessions were concatenated from the PRE, MAZE, and POST recording epochs. In addition to hippocampal electrophysiological recordings, neck EMG and head-mounted accelerometer signals were recorded, and the animal’s position during MAZE running epochs was tracked via head-mounted LEDs.
accessURL: https://doi.org/10.6080/K0862DC5
storedIn:
CRCNS.org
qualifier:
not compressed
format:
HTML
accessType:
landing page
authentication:
none
authorization:
none
abbreviation:
Cal
homePage: http://berkeley.edu/
ID:
SCR:011618
name:
University of California at Berkeley
abbreviation:
CRCNS
homePage: http://crcns.org
ID:
SCR:006907
name:
Collaborative Research in Computational Neuroscience