A Realistic Neural Mass Model of the Cortex with Laminar-Specific Connections and Synaptic Plasticity – Evaluation with Auditory Habituation

Abstract

In this work we propose a biologically realistic local cortical circuit model (LCCM), based on neural masses, that incorporates important aspects of the functional organization of the brain that have not been covered by previous models: (1) activity dependent plasticity of excitatory synaptic couplings via depleting and recycling of neurotransmitters and (2) realistic inter-laminar dynamics via laminar-specific distribution of and connections between neural populations. The potential of the LCCM was demonstrated by accounting for the process of auditory habituation. The model parameters were specified using Bayesian inference. It was found that: (1) besides the major serial excitatory information pathway (layer 4 to layer 2/3 to layer 5/6), there exists a parallel “short-cut” pathway (layer 4 to layer 5/6), (2) the excitatory signal flow from the pyramidal cells to the inhibitory interneurons seems to be more intra-laminar while, in contrast, the inhibitory signal flow from inhibitory interneurons to the pyramidal cells seems to be both intra- and inter-laminar, and (3) the habituation rates of the connections are unsymmetrical: forward connections (from layer 4 to layer 2/3) are more strongly habituated than backward connections (from Layer 5/6 to layer 4). Our evaluation demonstrates that the novel features of the LCCM are of crucial importance for mechanistic explanations of brain function. The incorporation of these features into a mass model makes them applicable to modeling based on macroscopic data (like EEG or MEG), which are usually available in human experiments. Our LCCM is therefore a valuable building block for future realistic models of human cognitive function.

Pathological responses to single‐pulse electrical stimuli in epilepsy: The role of feedforward inhibition

“…Following Wilson and Cowan (); David et al. (); Wang and Knösche (), we shift this function such that S (0) = 0, to model deviations from the normal activity. Accordingly, negative values are interpreted as activity below baseline.…”

“…The development of NMMs dates back to the early seventies to explain generation of the α-rhythm in the thalamus (Lopes da Silva, Hoeks, Smits, & Zetterberg, 1974). Later, NMMs were extended to models for cortical activity (Jansen & Rit, 1995;Ursino, Cona, & Zavaglia, 2010;Wang & Knösche, 2013;Wendling, Bartolomei, Bellanger, & Chauvel, 2002). NMMs are able to generate a variety of both healthy and epileptic EEG rhythms (Sotero, Trujillo-Barreto, Iturria-Medina, Carbonell, & Jimenez, 2007;Wendling, Bellanger, Bartolomei, & Chauvel, 2000) and also to describe event-related responses (Cona, Zavaglia, Massimini, Rosanova, & Ursino, 2011;David, Harrison, & Friston, 2005;Jansen & Rit, 1995;Jansen, Zouridakis, & Brandt, 1993;Wang & Knösche, 2013).…”

Alpha oscillations control cortical gain by modulating excitatory-inhibitory background activity

“…2. Large-scale cortical dynamics are well represented by an average firing rate model (88)(89)(90)(91)(92)(93). Like the case of dendritic effects above, extending our approach to include populations of heterogeneous neurons represents another important future extension (94,95).…”

A Circuit Model of Auditory Cortex

“…Existing large-scale mechanistic models of the auditory cortex apply to the scale of the whole brain [16,54], or apply to AC, but treat inhibitory neurons as one large group [30,26,60]. Only a handful of studies, restricted to specific results, include multiple inhibitory neuronal subtypes.…”