Control of slow oscillations in the thalamocortical neuron: a computer model

International audienceWe investigated computer models of a single thalamocortical neuron to assess the interaction of intrinsic voltage-sensitive channels and cortical synaptic input in producing the range of oscillation frequencies observed in these cells in vivo. A morphologically detailed model with Hodgkin-Huxley-like ion channels demonstrated that intrinsic properties would be sufficient to readily produce 3 to 6 Hz oscillations. Hyperpolarization of the model cell reduced its oscillation frequency monotonically whether through current injection or modulation of a potassium conductance, simulating the response to a neuromodulatory input. We performed detailed analysis of highly reduced models to determine the mechanism of this frequency control. The interburst interval was controlled by two different mechanisms depending on whether or not the pacemaker current, IH, was present. In the absence of IH, depolarization during the interburst interval occurred at the same rate with different current injections. The voltage difference from the nadir to threshold for the low-threshold calcium current, IT, determined the interburst interval. In contrast, with IH present, the rate of depolarization depended on injected current. With the full model, simulated repetitive cortical synaptic input entrained oscillations up to approximately double the natural frequency. Cortical input readily produced phase resetting as well. Our findings suggest that neither ascending brainstem control altering underlying hyperpolarization, nor descending drive by repetitive cortical inputs, would alone be sufficient to produce the range of oscillation frequencies seen in thalamocortical neurons. Instead, intrinsic neuronal mechanisms would dominate for generating the delta range (0.5-4 Hz) oscillations seen during slow wave sleep, whereas synaptic interactions with cortex and the thalamic reticular nucleus would be required for faster oscillations in the frequency range of spindling (7-14 Hz)

Effects of basal ganglia on cortical computation: A hybrid network/neural field model

The basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany neuronal degeneration in Parkinson's disease. Since motor commands originate from the cortex, an important functional question is how the basal ganglia influence cortical computation, and how this influence becomes pathological in Parkinson's disease. To explore this issue, we developed a hybrid neuronal network/neural field model. The neuronal network consisted of 9900 event-driven rule-based neurons, divided into 15 excitatory and inhibitory thalamocortical cell populations. This model was then embedded in a neural field model of the basal ganglia-thalamocortical system. This model included direct and indirect pathways via the striatum, a hyperdirect pathway via the subthalamic nucleus, thalamostriatal connections, and local inhibition in the striatum and globus pallidus. Both network and field models have been separately validated in previous work, with both shown to produce realistic firing rates and spectra. Spikes generated by the field model were used to drive the network model. Four types of drive were explored: (1) spikes drawn from a white-noise distribution; (2) spikes generated by the thalamocortical field model; (3) spikes generated by the full basal ganglia-thalamocortical field model; and (4) spikes generated by the full field model, with parameters based on parkinsonian individuals. In each case, we explored the information throughput in the network model from layer 2/3 (representing input from premotor and other cortical areas) to layer 5 (representing output to spinal cord motor neurons) using spectral Granger causality. All models had peaks in the coherence spectra at roughly 0 and 15 Hz; however, overall coherence in the white-noise model was roughly half that of the other three models, indicating that input from the thalamocortical system is the primary determinant of overall coherence. Compared to the healthy basal ganglia model, the parkinsonian model showed greater Granger causality at frequencies below 10 Hz, and less at frequencies above 10 Hz. Causality in the opposite direction (layer 5 to layer 2/3) at 5 Hz was several times times greater in the parkinsonian versus the healthy model, indicating a reversal in the direction of normal information flow. We speculate that the observed increases in Granger causality at low frequencies may be associated with tremor, while decreases at higher frequencies may contribute to bradykinesia. These results demonstrate that the brain's large-scale oscillatory environment strongly influences the information processing that occurs within its subnetworks