2021
DOI: 10.1101/2021.10.18.464855
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Estimating null and potent modes of feedforward communication in a computational model of cortical activity

Abstract: Communication across anatomical areas of the brain is key to both sensory and motor processes. Dimensionality reduction approaches have shown that the covariation of activity across cortical areas follows well-delimited patterns. Some of these patterns fall within the “potent space” of neural interactions and generate downstream responses; other patterns fall within the “null space” and prevent the feedforward propagation of synaptic inputs. Despite growing evidence for the role of null space activity in visua… Show more

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Cited by 1 publication
(2 citation statements)
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“…Our final contribution is to incorporate the empirical findings within A1 and PFC in a multi-area network that postulates their interactions through low-rank communication subspaces. Previous work modeling communication subspaces have focused on noise correlations in spontaneous activity and feedforward interactions (Gozel and Doiron 2022; Thivierge and Pilzak 2022); see also (Perich, Gallego, and L. E. Miller 2018) for a model of ‘output-null’ subspaces in the context of motor preparation. In contrast, we now propose a multi-region network with interactions in both feedforward and feedback directions, where PFC acts as a controller of A1, dynamically selecting the appropriate communication subspace for the ongoing context.…”
Section: Discussionmentioning
confidence: 99%
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“…Our final contribution is to incorporate the empirical findings within A1 and PFC in a multi-area network that postulates their interactions through low-rank communication subspaces. Previous work modeling communication subspaces have focused on noise correlations in spontaneous activity and feedforward interactions (Gozel and Doiron 2022; Thivierge and Pilzak 2022); see also (Perich, Gallego, and L. E. Miller 2018) for a model of ‘output-null’ subspaces in the context of motor preparation. In contrast, we now propose a multi-region network with interactions in both feedforward and feedback directions, where PFC acts as a controller of A1, dynamically selecting the appropriate communication subspace for the ongoing context.…”
Section: Discussionmentioning
confidence: 99%
“…In this work, we opted to model the simplest hypothesis that could explain our findings within A1 and PFC. Regardless, this model is to our knowledge the first neural implementation of the communication subspace hypothesis (Semedo, Zandvakili, et al 2019) that performs a cognitive task (but see Gozel and Doiron 2022; Thivierge and Pilzak 2022). While future theoretical work will be necessary to fully flesh out the implications of the communication subspace hypothesis (Semedo, Jasper, et al 2022; Semedo, Zandvakili, et al 2019), our model reveals several interesting insights.…”
Section: Discussionmentioning
confidence: 99%