A large scale neural network simulation with realistic cortical architecture has been undertaken to investigate the effects of external electrical stimulation on the propagation and evolution of ongoing seizure activity. This is an effort to explore the parameter space of stimulation variables to uncover promising avenues of research for this therapeutic modality. The model consists of an approximately 800 μm × 800 μm region of simulated cortex, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. The cell dynamics are governed by a modified version of the Hodgkin-Huxley equations in single compartment format. Axonal connections are patterned after histological data and published models of local cortical wiring. Stimulation induced action potentials take place at the axon initial segments, according to threshold requirements on the applied electric field distribution. Stimulation induced action potentials in horizontal axonal branches are also separately simulated. The calculations are performed on a 16 node distributed 32-bit processor system. Clear differences in seizure evolution are presented for stimulated versus the undisturbed rhythmic activity. Data is provided for frequency dependent stimulation effects demonstrating a plateau effect of stimulation efficacy as the applied frequency is increased from 60 Hz to 200 Hz. Timing of the stimulation with respect to the underlying rhythmic activity demonstrates a phase dependent sensitivity. Electrode height and position effects are also presented. Using a dipole stimulation electrode arrangement, clear orientation effects of the dipole with respect to the model connectivity is also demonstrated. A sensitivity analysis of these results as a function of the stimulation threshold is also provided.

The development of synchronous bursting in neuronal ensembles represents an important change in network behavior. To determine the influences on development of such synchronous bursting behavior we study the dynamics of small networks of sparsely connected excitatory and inhibitory neurons using numerical simulations. The synchronized bursting activities in networks evoked by background spikes are investigated. Specifically, patterns of bursting activity are examined when the balance between excitation and inhibition on neuronal inputs is varied and the fraction of inhibitory neurons in the network is changed. For quantitative comparison of bursting activities in networks, measures of the degree of synchrony are used. We demonstrate how changes in the strength of excitation on inputs of neurons can be compensated by changes in the strength of inhibition without changing the degree of synchrony in the network. The effects of changing several network parameters on the network activity are analyzed and discussed. These changes may underlie the transition of network activity from normal to potentially pathologic (e.g., epileptic) states.

SummaryPurpose-A neural network simulation with realistic cortical architecture has been used to study synchronized bursting as a seizure representation. This model has the property that bursting epochs arise and cease spontaneously, and bursting epochs can be induced by external stimulation. We have used this simulation to study the time-frequency properties of the evolving bursting activity, as well as effects due to network stimulation. Methods-The model represents a cortical region of 1.6 mm × 1.6 mm, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. There are a total of 65, 536 modeled single compartment neurons that operate according to a version of Hodgkin-Huxley dynamics. The intercellular wiring is based on histological studies and our previous modeling efforts.Results-The bursting phase is characterized by a flat frequency spectrum. Stimulation pulses are applied to this modeled network, with an electric field provided by a 1 mm radius circular electrode represented mathematically in the simulation. A phase dependence to the post-stimulation quiescence is demonstrated, with local relative maxima in efficacy occurring before or during the network depolarization phase in the underlying activity. Brief periods of network insensitivity to stimulation are also demonstrated. The phase dependence was irregular and did not reach statistical significance when averaged over the full 2.5 seconds of simulated bursting investigated. This result provides Conclusions-This large scale multi-neuron neural network simulation reproduces many aspects of evolving cortical bursting behaviour as well as the timing-dependent effects of electrical stimulation on that bursting.

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Neural responses recorded from auditory cortex exhibit adaptation, a stimulus-specific decrease that occurs when the same sound is presented repeatedly. Stimulus-specific adaptation is thought to facilitate perception in noisy environments. Although adaptation is assumed to arise independently from cortex, this has been difficult to validate directly in vivo. In this study, we used a neural network model of auditory cortex with multicompartmental cell modeling to investigate cortical adaptation. We found that repetitive, non-adapted inputs to layer IV neurons in the model elicited frequency-specific decreases in simulated single neuron, population-level and local field potential (LFP) activity, consistent with stimulus-specific cortical adaptation. Simulated recordings of LFPs, generated solely by excitatory post-synaptic inputs and recorded from layers II/III in the model, showed similar waveform morphologies and stimulus probability effects as auditory evoked responses recorded from human cortex. We tested two proposed mechanisms of cortical adaptation, neural fatigue and neural sharpening, by varying the strength and type of inter- and intra-layer synaptic connections (excitatory, inhibitory). Model simulations showed that synaptic depression modeled in excitatory (AMPA) synapses was sufficient to elicit a reduction in neural firing rate, consistent with neural fatigue. However, introduction of lateral inhibition from local layer II/III interneurons resulted in a reduction in the number of responding neurons, but not their firing rates, consistent with neural sharpening. These modeling results demonstrate that adaptation can arise from multiple neural mechanisms in auditory cortex.

The space-lumped two-variable neuron model is studied. Extension of the neural model by adding a simple synaptic current allows the demonstration of neural interactions. The production of synchronous burst activity in this simple two-neuron excitatory loop is modeled, including the influence of random background excitatory input. The ability of the neuron model to integrate inputs spatially and temporally is shown. Two refractory periods after stimuli were identified and their role in burst cessation is demonstrated. Our findings show that simple neural units without long-lasting membrane processes are capable of generating long lasting patterns of activity. The results of simulation of simple background activity suggest that an increase in background activity tends to cause decreased activity of the network. This phenomenon, as well as the existence of two refractory periods, allows for burst cessation without inhibition in this simple model.

Four different passivations are applied on AlGaN/GaN heterostructure field-effect transistors (HFETs) and their performance is compared. SiO 2 and SiN layers of different thicknesses and deposition temperatures, which induced different stresses, are used for passivation. The sheet charge density and the saturated drain current increased up to 27% and 37%, respectively, with increasing stress from compressive (−150 MPa) to tensile (50 MPa). The change of the stress-induced sheet charge density is 1.5 × 10 11 cm −2 for 100 MPa. For non-stressed conditions, a passivation-induced sheet charge density of ∼1.3 × 10 12 cm −2 is extrapolated. This indicates that the passivation-induced stress is only a partial effect of the HFET passivation, compared to the surface states reduction. The current collapse evaluation, by consecutive I-V sweeps with 20 ms and 416 µs integration time, shows that the devices with 30 nm thick SiN passivation exhibited the best performance. However, two processes with different time constants need to be considered. Trapping processes at the GaN/passivation interface as well as in the AlGaN(GaN) barrier layer are supposed to be responsible for the observed behaviour.

Objectives Computational modeling studies were performed to identify presynaptic elements of cortical neurons that are activated by subdural electrical stimulation. Materials and Methods The computer model consists of layers of multicompartmental neurons arranged in 3D space in an anatomically realistic fashion inside a 4.8×4.8×3.4 mm volume of gray matter modeled as a homogenous and isotropic medium. The model was subjected to an electric field generated by a circular disk electrode. Results The initiation of presynaptic action potentials (PAPs) in neurons takes place predominantly in the axon initial segment (AIS) or ectopically in axonal branch terminals. PAPs that were initiated in only one axonal terminal were typically followed by a second PAP (spike duplet) resulting from the activation of the AIS by the antidromically propagating initial PAP. There were significant time delays (up to 0.5 ms) in the propagation of these ectopically initiated PAPs along the axons to non-activated axonal branches and, associated with these delays, latencies in the occurrence of spike duplets in different axonal terminals. The effect of the dendritic arbor 3D structure on the AIS activation threshold was contingent on whether the net axonal and somato-dendritic current flows made an antagonistic or synergetic contribution. Conclusions This study examines the effects of subdural electrical stimulation on a high density network consisting of several populations of multicompartment cell types. The effect of dendritic arbor structure on the axonal activation threshold is prominent in the case of multipolar neurons with large-diameter symmetric dendrites (basal/apical) that are oriented parallel to the electric field lines. The timing of presynaptic terminal activation after stimulation is not determined solely by the axonal delay (orthodromic propagation) but depends on the details of the applied stimulation field and axonal branching structure, which may be important factors in characterizing the effects of electrical stimulation in neuromodulation systems.