title: Linking canonical microcircuits and neuronal activity: Dynamic causal modelling of laminar recordings.
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.
authors.affiliation: The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK. Electronic address: pinotsis@mit.edu.
authors.forename: D A
authors.initials: DA
authors.lastname: Pinotsis
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
authors.forename: J P
authors.initials: JP
authors.lastname: Geerts
authors.affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, United States.
authors.forename: L
authors.initials: L
authors.lastname: Pinto
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK; MPS - UCL Centre for Computational Psychiatry and Ageing Research, Russell Square House, London, WC1B 5EH, UK.
authors.forename: T H B
authors.initials: THB
authors.lastname: FitzGerald
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
authors.forename: V
authors.initials: V
authors.lastname: Litvak
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK; Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.
authors.forename: R
authors.initials: R
authors.lastname: Auksztulewicz
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
authors.forename: K J
authors.initials: KJ
authors.lastname: Friston
mesh_terms: D000109:Acetylcholine / Q000502:physiology; D000818:Animals; D066187:Basal Forebrain / Q000502:physiology; D001499:Bayes Theorem; D006801:Humans; D051379:Mice; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*
chemical_list: D000109:Acetylcholine