Linking canonical microcircuits and neuronal activity:Dynamic Causal Modelling of Laminar Recordings

Pinotsis, D A, Geerts, J P, Pinto, L, FitzGerald, T H B, Litvak, V, Auksztulewicz, R and Friston, K J (2017) Linking canonical microcircuits and neuronal activity:Dynamic Causal Modelling of Laminar Recordings. NeuroImage, 146. 355–366. ISSN 1053-8119

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Abstract

Neural models describe brain activity at different scales, ranging from single cells to whole brain networks. Here, we attempt to reconcile models operating at the microscopic (compartmental) and mesoscopic (neural mass) scales to analyse data from microelectrode recordings of intralaminar neural activity. Although these two classes of models operate at different scales, it is relatively straightforward to create neural mass models of ensemble activity that are equipped with priors obtained after fitting data generated by detailed microscopic models. This provides generative (forward) models of measured neuronal responses that retain construct validity in relation to compartmental models. We illustrate our approach using cross spectral responses obtained from V1 during a visual perception paradigm that involved optogenetic manipulation of the basal forebrain. We find that the resulting neural mass model can distinguish between activity in distinct cortical layers - both with and without optogenetic activation - and that cholinergic input appears to enhance (disinhibit) superficial layer activity relative to deep layers. This is particularly interesting from the perspective of predictive coding, where neuromodulators are thought to boost prediction errors that ascend the cortical hierarchy.

Item Type: Article
Additional Information: This article is open access under a Creative Commons Attribution 4.0 International licence (CC BY).
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Pure Connector
Date Deposited: 29 Nov 2016 00:01
Last Modified: 17 Mar 2020 22:41
URI: https://ueaeprints.uea.ac.uk/id/eprint/61512
DOI: 10.1016/j.neuroimage.2016.11.041

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