NatureNeurosciencePeer-reviewed journal article
Cortical reactivations predict future sensory responses
Nghia D. NguyeniD, Andrew LutasiD, Oren AmsalemiD, Jesseba FernandoiD, Andy Young-Eon Ahn, Richard HakimiD, Josselyn VergaraiD, Justin McMahoniD, Jordane DimidschsteiniD, Bernardo Luis SabatiniiD, Mark L. AndermanniD
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Abstract
Prevailing theories of offline memory consolidation posit that the pattern of neurons activated during a salient sensory experience will be faithfully reactivated, thereby stabilizing the entire pattern1-3. However, sensory-evoked patterns are not stable, but instead drift across repeated experiences4-7. To investigate potential roles of reactivations in the stabilization and/or drift of sensory representations, we imaged calcium activity of thousands of excitatory neurons in mouse lateral visual cortex. Presentation of a stimulus resulted in transient, stimulus-specific reactivations during the following minute. These reactivations depended on local circuit activity, as they were abolished by local silencing during the preceding stimulus. Contrary to prevailing theories, reactivations systemically differed from previous patterns evoked by the stimulus. Instead, they were more similar to future patterns evoked by the stimulus, thereby predicting representational drift. In particular, neurons that participated more or less in early reactivations than in stimulus response patterns subsequently increased or decreased their future stimulus responses, respectively. The rate and content of these reactivations was sufficient to accurately predict future changes in stimulus responses and, surprisingly, the decreasing similarity of responses to distinct stimuli. Thus, activity patterns during sensory cortical reactivations may guide the drift in sensory responses to improve sensory discrimination8.
- organism: mouse
- assay: electronic health records
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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