Single-Column Thalamocortical Network Model Exhibiting Gamma Oscillations, Sleep Spindles, and Epileptogenic Bursts

Abstract

To better understand population phenomena in thalamocortical neuronal ensembles, we have constructed a preliminary network model with 3,560 multicompartment neurons (containing soma, branching dendrites, and a portion of axon). Types of neurons included superficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing behaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic types of connectivity, and located in superficial and deep cortical layers; low threshold spiking (LTS) interneurons, which contacted principal cell dendrites; deep pyramids, which could have RS or intrinsic bursting (IB) firing behaviors, and endowed either with nontufted apical dendrites or with long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis (nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were based on published data, although many arbitrary choices were necessary. In addition to synaptic connectivity (by AMPA/kainate, NMDA, and GABA(A) receptors), we also included electrical coupling between dendrites of interneurons, nRT cells, and TCR cells, and--in various combinations--electrical coupling between the proximal axons of certain cortical principal neurons. Our network model replicates several observed population phenomena, including 1) persistent gamma oscillations; 2) thalamocortical sleep spindles; 3) series of synchronized population bursts, resembling electrographic seizures; 4) isolated double population bursts with superimposed very fast oscillations (>100 Hz, "VFO"); 5) spike-wave, polyspike-wave, and fast runs (about 10 Hz). We show that epileptiform bursts, including double and multiple bursts, containing VFO occur in rat auditory cortex in vitro, in the presence of kainate, when both GABA(A) and GABA(B) receptors are blocked. Electrical coupling between axons appears necessary (as reported previously) for persistent gamma and additionally plays a role in the detailed shaping of epileptogenic events. The degree of recurrent synaptic excitation between spiny stellate cells, and their tendency to fire throughout multiple bursts, also appears critical in shaping epileptogenic events.

Studies of stimulus parameters for seizure disruption using neural network simulations

“…The total number of neurons currently represented is 16,384, and this will be expanded in future studies. At some point, to truly appreciate the effects of external stimulation, it might be necessary to make the model even more complex as in (Traub et al 2005). We view the current effort as a middle ground, which allows for easy implementation of a computational representation of cortical stimulation.…”

“…The parameter space of variables describing epilepsy is vast and complex, and ultimately the origins of the most common types of seizures are not completely understood. Investigators in the last century began to use computational modeling to study epilepsy, utilizing its important ability to rapidly change descriptive variables such as ion channels and synaptic properties, connections between neurons, the relative importance of different neuronal compartments, and the temporal characteristics of synaptic messaging (Traub et al 1994;Traub et al 1999;Kudela et al 1997;Kudela et al 2003a;Traub et al 2005).…”

Mechanisms of physiological and epileptic HFO generation

“…Fast ripples (250-600 Hz)-Very recently, three computational models were proposed to investigate the mechanisms underlying the generation of fast ripples (250-600 Hz) which are likely to be a signature of pathological processes, as distinct from ripples at slower frequencies which may be generated under physiological conditions. One network model (Roopun et al, 2010) followed the network designed by Traub et al (2005a). Using this model in conjunction with physiological measurements in human tissue resected from neocortical epileptic foci, in vitro, these authors argued for the importance of axo-axonic gap junctions in fast ripples.…”

Introduction to Focus Issue: Rhythms and Dynamic Transitions in Neurological Disease: Modeling, Computation, and Experiment

“…52 On the three fronts of modeling, computation, and experiment, there have been a series of important recent advances, and many new avenues of research have emerged. These include: (1) new techniques to record high-density brain activity; [55][56][57] (2) theoretical advances and clinical application of deep brain stimulation methods; 58 (3) sophisticated biophysical models of rhythms and disease; 59,60 (4) novel optogenetic techniques that permit interrogation of neural circuits in vivo; 61 and, (5) increasingly-sophisticated data-analysis techniques. 62 The modeling and computational work-both deterministic and stochastic-have focused in part on reproducing experimental results, and on the investigation of the underlying biophysical and dynamical mechanisms, especially when experiments are not possible or very difficult to perform.…”