Complex temporal patterns processing by a neural mass model of a cortical column

Abstract

It is well known that neuronal networks are capable of transmitting complex spatiotemporal information in the form of precise sequences of neuronal discharges characterized by recurrent patterns. At the same time, the synchronized activity of large ensembles produces local field potentials that propagate through highly dynamic oscillatory waves, such that, at the whole brain scale, complex spatiotemporal dynamics of electroencephalographic (EEG) signals may be associated to sensorimotor decision making processes. Despite these experimental evidences, the link between highly temporally organized input patterns and EEG waves has not been studied in detail. Here, we use a neural mass model to investigate to what extent precise temporal information, carried by deterministic nonlinear attractor mappings, is filtered and transformed into fluctuations in phase, frequency and amplitude of oscillatory brain activity. The phase shift that we observe, when we drive the neural mass model with specific chaotic inputs, shows that the local field potential amplitude peak appears in less than one full cycle, thus allowing traveling waves to encode temporal information. After converting phase and amplitude changes obtained into point processes, we quantify input-output similarity following a threshold-filtering algorithm onto the amplitude wave peaks. Our analysis shows that the neural mass model has the capacity for gating the input signal and propagate selected temporal features of that signal. Finally, we discuss the effect of local excitatory/inhibitory balance on these results and how excitability in cortical columns, controlled by neuromodulatory innervation of the cerebral cortex, may contribute to set a fine tuning and gating of the information fed to the cortex.

Realistic alpha oscillations and transient responses in a cortical microcircuit model

“…In addition, SCIOM's response to input transients is strongly dependent on the phase of alpha oscillations [60,10], consistent with a variety of psychophysical studies involving visual, auditory, and somatosensory detection [40,38,44,18] and discrimination [25,55,5], and physiological studies highlighting the role of ongoing activity in explaining response variability [34,29]. The physiological basis for these effects has long been the subject of modeling [51,40,41]. For example, models have typically assumed that the inverse relationship between alpha waves and cortical excitability is governed by a strong association between alpha waves and inhibitory neurons [36,43], and that control of alpha wave magnitude is governed by thalamocortical inputs [63].…”

“…[29,31] Although the Jansen-Rit (JR) model successfully models how alpha rhythms are modulated in a phase dependent manner by stimulation, it does not accurately simulate several aspects of cortical responses. [41] First, alpha rhythms are largely associated with relative quiescence [1,15,36]: the first observations of cortical alpha were obtained from subjects with their eyes closed in the absence of any visual stimulation. [6] However, stable oscillations in the absence of parameter manipulations emerges in the JR system when driven by drive abpve 155 Hz.…”

“…[6] However, stable oscillations in the absence of parameter manipulations emerges in the JR system when driven by drive abpve 155 Hz. [23,30] Second, the stable oscillations come at the cost of realistic response dynamics to input transients, which are largely lost, or substantially altered, in the output of these models [41,17]. For example, stable oscillations in the JR system are seen at input rates as high as 315 Hz, precluding the faithful transmission of high input transients.…”

Evolutionary Advantages of Stimulus-Driven EEG Phase Transitions in the Upper Cortical Layers

“…Spatiotemporal sequences of time-position patterns have been observed in the human brain associated with cognitive tasks (Tal and Abeles, 2018 ). Recent models describing sequential processing of complex patterns of brain activity are developed in, e.g., Cabessa and Villa ( 2018 ); Malagarriga et al ( 2019 ).…”

A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

“…It improves our understanding of abnormal brain electrical activities [ 6 ]. After many years, the computational neural model has derived a variety of variations and it has been well used in [ 7 , 8 , 9 ]. We employ the widely used neural mass model with nonlinear lumped parameters that can simulate various physiological signals in this work [ 10 ].…”