Pattern Separation in the Dentate Gyrus: A Role for the CA3 Backprojection

By Catherine E. Myers, H. Scharfman. 2010

136 citations recorded by the source corpus.

Many theories of hippocampal function assume that area CA3 of hippocampus is capable of performing rapid pattern storage, as well as pattern completion when a partial version of a familiar pattern is presented, and that the dentate gyrus (DG) is a preprocessor that performs pattern separation, facilitating storage and recall in CA3. The latter assumption derives partly from the anatomical and physiological properties of DG. However, the major output of DG is from a large number of DG granule cells to a smaller number of CA3 pyramidal cells, which potentially negates the pattern separation performed in the DG. Here, we consider a simple CA3 network model, and consider how it might interact with a previously developed computational model of the DG. The resulting “standard” DG‐CA3 model performs pattern storage and completion well, given a small set of sparse, randomly derived patterns representing entorhinal input to the DG and CA3. However, under many circumstances, the pattern separation achieved in the DG is not as robust in CA3, resulting in a low storage capacity for CA3, compared to previous mathematical estimates of the storage capacity for an autoassociative network of this size. We also examine an often‐overlooked aspect of hippocampal anatomy that might increase functionality in the combined DG‐CA3 model. Specifically, axon collaterals of CA3 pyramidal cells project “back” to the DG (“backprojections”), exerting inhibitory effects on granule cells that could potentially ensure that different subpopulations of granule cells are recruited to respond to similar patterns. In the model, addition of such backprojections improves both pattern separation and storage capacity. We also show that the DG‐CA3 model with backprojections provides a better fit to empirical data than a model without backprojections. Therefore, we hypothesize that CA3 backprojections might play an important role in hippocampal function. © 2010 Wiley Periodicals, Inc.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Developmental Neuroscience

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Citations in the corpus

Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity (2016, unknown): “The model consists of four types of cells (MCs, BCs, HIPP, and GCs) modeled as simplified integrate-and-fire neurons.”
Tracking the Flow of Hippocampal Computation: Pattern Separation, Pattern Completion, and Attractor Dynamics (2015, methodology): “Investigators elaborated Marr's subsequent theory of the hippocampus (Marr, 1971) and ascribed a similar function to the DG granule cell layer (McNaughton & Morris, 1987; McNaughton & Nadel, 1990; Rolls & Treves, 1998; O'Reilly & McClelland, 1994; Myers & Scharfman, 2009; Myers & Scharfman, 2011).”
Pattern separation in the hippocampus through the eyes of computational modeling (2017, methodology): “The Scharfman lab introduced a number of simple, biologically relevant, models of the hippocampal DG-CA3 microcircuit (Myers, Bermudez-Hernandez, & Scharfman, 2013; Myers & Scharfman, 2013, 2011; Scharfman & Myers, 2015).”