Sparse, environmentally selective expression of Arc RNA in the upper blade of the rodent fascia dentata by brief spatial experience

By Monica K. Chawla, John Guzowski, V. Ramírez-Amaya, Peter Lipa, K. Hoffman, L. K. Marriott, P. Worley, Bruce L. McNaughton, C. A. Barnes. 2005

403 citations recorded by the source corpus.

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

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Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “In the dentate gyrus, ultra-sparse granule cell population activity (2-5%) over long timescales was originally inferred from immediate-early gene expression (Chawla et al., 2005; Tashiro et al., 2007; Alme et al., 2010).”
Adult Neurogenesis in the Hippocampus: From Stem Cells to Behavior. (2016, positive): “Remarkably, only 2% of DGCs respond when exposed to any given context, as recorded by IEG expression (Chawla et al., 2005), and a recent study has found that the size of neuronal representations (cell ensembles) depends heavily on lateral inhibition from somatostatin-expressing interneurons in the…”
Permanent Genetic Access to Transiently Active Neurons via TRAP: Targeted Recombination in Active Populations (2013, result): “The increased TRAPing of DG granule cells with novel environment exploration was also greater in the upper blade than in the lower blade (Figure 6C), consistent with prior reports of an upper blade-selective increase in Arc expression in rats exploring a novel environment (Chawla et al., 2005).”
Hilar Mossy Cell Degeneration Causes Transient Dentate Granule Cell Hyperexcitability and Impaired Pattern Separation (2012, positive): “In a typical environment where granule cells are only rarely activated (Chawla et al., 2005), different incoming signals disperse onto largely nonoverlapping granule cell populations, thereby supporting their role in pattern separation.”
Hippocampal granule cells opt for early retirement (2010, methodology): “The data of Chawla et al. (2005) yield an estimate of P in the suprapyramidal blade of 0.018.”
Differential control of learning and anxiety along the dorso-ventral axis of the dentate gyrus (2013, background): “genes in only 5% of the GC population (Chawla et al., 2005).”
Using immediate-early genes to map hippocampal subregional functions. (2007, background): “%) than in CA1 (∼35%), but far greatest in the dentate gyrus, where exploration induced Arc in only ∼2% of granule cells in the dorsal blade and practically in none in the ventral blade (Temple et al. 2003; Chawla et al. 2005).”
Anatomical gradients of adult neurogenesis and activity: young neurons in the ventral dentate gyrus are activated by water maze training (2009, positive): “Blade-dependent differences in excitability, GABAergic inhibition and exploration-induced arc expression (Scharfman et al., 2002; Chawla et al., 2005; Ramirez-Amaya et al., 2005) indicate that cells in the two blades also contribute to different aspects of hippocampal function.”
Constitutive and Synaptic Activation of GIRK Channels Differentiates Mature and Newborn Dentate Granule Cells (2018, background): “The DG exhibits sparse neural activity wherein only a small fraction of the principal granule cells (GCs) are active at any given time and active GCs have generally low firing rates (Jung and McNaughton, 1993; Chawla et al., 2005; Leutgeb et al., 2007; Neunuebel and Knierim, 2012).”
Spatial representations of granule cells and mossy cells of the dentate gyrus (2017, background): “…coding scheme and multiple lines of evidence for sparse granule cell activity that have more recently emerged (Alme et al., 2010; Danielson et al., 2016; Chawla et al., 2005; Neunuebel and Knierim, 2012; Diamantaki et al., 2016a, 2016b; see also Jung and McNaughton, 1993; Skaggs et al., 1996).”
Spatial firing correlates of physiologically distinct cell types of the rat dentate gyrus (2012, result): “This result is consistent with singlefield cells being localized to the densely packed granule layer, with only a small percentage of cells active in a given environment (Jung and McNaughton, 1993; Skaggs et al., 1996; Chawla et al., 2005).”
A putative role for neurogenesis in neuro-computational terms: inferences from a hippocampal model. (2009, neutral): “The sparse coding in DG, mainly due to the highly divergent input stream from EC to DG (Amaral, Ishizuka, & Claiborne, 1990) has been assumed to support a pattern separation function (Chawla et al., 2005; Treves & Rolls, 1992).”