Abstract
SummaryIn the hippocampus, cell assemblies forming mnemonic representations of space are thought to arise as a result of changes in functional connections of pyramidal cells. We have found that CA1 interneuron circuits are also reconfigured during goal-oriented spatial learning through modification of inputs from pyramidal cells. As learning progressed, new pyramidal assemblies expressed in theta cycles alternated with previously established ones, and eventually overtook them. The firing patterns of interneurons developed a relationship to new, learning-related assemblies: some interneurons associated their activity with new pyramidal assemblies while some others dissociated from them. These firing associations were explained by changes in the weight of monosynaptic inputs received by interneurons from new pyramidal assemblies, as these predicted the associational changes. Spatial learning thus engages circuit modifications in the hippocampus that incorporate a redistribution of inhibitory activity that might assist in the segregation of competing pyramidal cell assembly patterns in space and time.
Highlights
Current theories of memory formation suggest that experiencedependent modifications of synaptic weights enable a selected group of neurons to form new associations, leading to the establishment of new cell assemblies to represent mnemonic information (Buzsaki, 2010; Martin and Morris, 2002)
The detailed temporal dynamics that facilitate the development of new maps during spatial learning remain to be examined
We found that the flickering of old and new maps takes place during spatial learning
Summary
Current theories of memory formation suggest that experiencedependent modifications of synaptic weights enable a selected group of neurons to form new associations, leading to the establishment of new cell assemblies to represent mnemonic information (Buzsaki, 2010; Martin and Morris, 2002). It is possible that such flickering may take place between old and newly-formed representations during spatial learning This could enable competitive processes in which old and new maps initially vie for prominence, with the new maps dominating in later stages of learning. Such competitive network dynamics may be an integral part of spatial learning and map refinement, allowing for effective behavioral adaptation in response to the environment
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