2021
DOI: 10.1111/ejn.15177
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GEVI cell‐type specific labelling and a manifold learning approach provide evidence for lateral inhibition at the population level in the mouse hippocampal CA1 area

Abstract: The CA1 area in the mammalian hippocampus is essential for spatial learning. Pyramidal cells are the hippocampus output neurons and their activities are regulated by inhibition exerted by a diversified population of interneurons. Lateral inhibition has been suggested as the mechanism enabling the reconfiguration of pyramidal cell assembly activity observed during spatial learning tasks in rodents. However, lateral inhibition in the CA1 lacks the overwhelming evidence reported in other hippocampal areas such as… Show more

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Cited by 7 publications
(8 citation statements)
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“…In our experiment, we measured ensemble activations following stimulation by averaging across ROI activity and aligning this activity trace with the stimulus onsets. This analysis revealed a pattern of activity similar to a previous report using genetically encoded voltage indicators (GEVI) in vitro ( Nakajima et al, 2021 ) but, to our knowledge, has not been previously characterized using Thy1-GCaMP6f animals. We broke the activity down into three distinct features that occurred between ∼0 and ∼5 s following stimulus onset and consisted of (1) a first ensemble maximum; a narrow (∼0.2 s) and prominent peak usually between 0 and 1 s following stimulus onset.…”
Section: Discussionsupporting
confidence: 86%
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“…In our experiment, we measured ensemble activations following stimulation by averaging across ROI activity and aligning this activity trace with the stimulus onsets. This analysis revealed a pattern of activity similar to a previous report using genetically encoded voltage indicators (GEVI) in vitro ( Nakajima et al, 2021 ) but, to our knowledge, has not been previously characterized using Thy1-GCaMP6f animals. We broke the activity down into three distinct features that occurred between ∼0 and ∼5 s following stimulus onset and consisted of (1) a first ensemble maximum; a narrow (∼0.2 s) and prominent peak usually between 0 and 1 s following stimulus onset.…”
Section: Discussionsupporting
confidence: 86%
“…(2) An ensemble minimum; a significant and consistent decrease in ensemble activity, often times even to 0% of the ROIs being active and lasting around 1 s. We attribute this to lateral inhibition in the CA1 field following excitation of somata in the first maximum. This hypothesis is supported by the previous study of CA1 stimulation using GEVI mice as the similar dip in activity following stimulation was sensitive to GABA blockers ( Nakajima et al, 2021 ). This decrease in ensemble activity was the most consistent across slices, groups, and sessions.…”
Section: Discussionsupporting
confidence: 76%
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“…Injection of the same AAV into PV-cre mice restricted expression to PV+ cells (PV-ArcLight) of the motor cortex. Although not all CaMKIIα+ reactive cells in the motor cortex express cre recombinase in the CaMKIIα-cre mouse, cre recombinase mediated ArcLight expression likely results in most of the pyramidal cells in layers II and III expressing ArcLight ( Jones et al, 1994 ; Wang et al, 2013 ; Nakajima et al, 2021 ). Given that there is virtually no overlap between the CaMKIIα+ and PV+ cells ( Jones et al, 1994 ; Wang et al, 2013 ), CaMKIIα+ cell and PV+ cell specific expression of ArcLight should be mutually exclusive.…”
Section: Resultsmentioning
confidence: 99%
“…ArcLight was therefore used to detect oscillations in distinct cell types of the motor cortex. Although not all CaMKIIα+ reactive cells in the motor cortex express cre recombinase in the CaMKIIα-cre mouse, cre recombinase mediated ArcLight expression likely results in most of the pyramidal cells in layers II and III expressing ArcLight (Jones et al, 1994;Wang et al, 2013;Nakajima et al, 2021). Given that there is virtually no overlap between the CaMKIIα+ and PV+ cells (Jones et al, 1994;Wang et al, 2013), CaMKIIα+ cell and PV+ cell specific expression of ArcLight should be mutually exclusive.…”
Section: Using the K-nearest Neighbor Algorithm To Detect Oscillationsmentioning
confidence: 99%