Cerebral Cortex Advance Access published online on August 16, 2007
Cerebral Cortex, doi:10.1093/cercor/bhm136
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The Hippocampal CA1 Region and Dentate Gyrus Differentiate between Environmental and Spatial Feature Encoding through Long-Term Depression
1 Learning and Memory Research, Medical Faculty, 2 International Graduate School for Neuroscience, Ruhr University Bochum, 44780 Bochum, Germany
Address correspondence to Denise Manahan-Vaughan, PhD, Learning and Memory Research, Medical Faculty, Ruhr University Bochum, FNO 1/116, Universitaetsstrasse 150, 44780 Bochum, Germany. Email: dmv-igsn{at}rub.de.
Novel spatial information is encoded in the hippocampus by plastic changes of synaptic properties. Novel space consists of several types of information that may evoke differential synaptic responses in individual hippocampal subregions. To examine this possibility, we recorded field potentials from the dentate gyrus (DG) and CA1 region in freely moving adult rats. Stimulation protocols that were marginally subthreshold for the induction of persistent long-term potentiation (LTP) or long-term depression (LTD) were implemented, concurrent with exposure to novel spatial information. We found that in both hippocampal subregions, exploration of a novel empty hole board facilitated LTP. However, LTD facilitation was subregion specific and dependent on the nature of the cues. In the CA1 region, partially concealed cues had a facilitatory effect on LTD. LTD in the DG was facilitated by large directional cues. Thus, although LTP was facilitated uniformly in both areas by the same novel environment, LTD was facilitated in a region-specific manner, based on the nature of the cue. This implies that spatial changes within an environment elicit local changes of synaptic weights dependent on the type of information and, hence, generate a complete cognitive map as a consequence of cooperation of synaptic plasticity in all participating subregions.
Key Words: CA1 dentate gyrus long-term depression long-term potentiation novelty acquisition spatial learning
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