Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity

By Spyridon Chavlis, Panagiotis C. Petrantonakis, Panayiota Poirazi. 2016

71 citations recorded by the source corpus.

The hippocampus plays a key role in pattern separation, the process of transforming similar incoming information to highly dissimilar, nonverlapping representations. Sparse firing granule cells (GCs) in the dentate gyrus (DG) have been proposed to undertake this computation, but little is known about which of their properties influence pattern separation. Dendritic atrophy has been reported in diseases associated with pattern separation deficits, suggesting a possible role for dendrites in this phenomenon. To investigate whether and how the dendrites of GCs contribute to pattern separation, we build a simplified, biologically relevant, computational model of the DG. Our model suggests that the presence of GC dendrites is associated with high pattern separation efficiency while their atrophy leads to increased excitability and performance impairments. These impairments can be rescued by restoring GC sparsity to control levels through various manipulations. We predict that dendrites contribute to pattern separation as a mechanism for controlling sparsity. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. N. A. Cayco-Gajic
2. R. Silver
3. Spyridon Chavlis
4. Panayiota Poirazi

In Cognitive Neuroscience

1. N. A. Cayco-Gajic
2. R. Silver
3. Spyridon Chavlis
4. Panayiota Poirazi

Citations in the corpus

• Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “In addition to adaptive inhibition, recent work also suggests that nonlinear dendritic processing contributes to sparse granule cell activity and pattern separation by nonlinear thresholding (Chavlis et al., 2017).”
• Pattern separation in the hippocampus through the eyes of computational modeling (2017, methodology): “Chavlis et al. (2017) predicted that dendrites, along with a number of other mechanisms such as synaptic strengths, somatic dimensions, ionic conductances, and so forth. contribute to pattern separation by controlling sparsity in the GC population.”