Figure 11.
A computational network model of slow oscillatory activity also shows fast oscillations in the up states and allows for mechanistic explorations. A, Schematic representation of the network architecture, with excitatory (red circles) and inhibitory (blue circles) neurons disposed on a line. Whether a neuron connected synaptically to another neuron at a given distance on the line was determined according to Gaussian probability densities (dashed curves). B, Spontaneous activity in the network organizes in recurring bursts (frequency <1 Hz) that traveled along the network [see a complete description of this phenomenon in (Compte et al., 2003a)]. C, Looking at the network activity during up states, fast oscillations could be detected from simulated multiunit records (event trains combining spikes from 60 neighboring neurons), both in the autocorrelation (middle panel) and in the power spectrum (right panel). Data from the computational model was analyzed with precisely the same methods and parameters as data from our in vitro experiments. D, A parametric change in the decay time constant of inhibitory conductances τI (τI = 10, 9, 8, 7 ms plotted in red, green, blue, and black, respectively. The control case in C corresponds to τI = 10 ms), modified the power spectra of network activity parametrically, in that the frequency of the peak increased progressively as τI was reduced (see inset). E, No change in the frequency of fast oscillations was detected when the inhibitory conductance between inhibitory neurons was modulated (from one half to double the control value). F, Manipulation of the inhibitory conductance strength between inhibitory and excitatory neurons produced a parametric change in the frequency of the fast oscillation during the up states. G, Manipulation of the conductance of a Ca2+-dependent K+ channel in excitatory neurons also had an effect in the frequency of the fast oscillation. Shades indicate 95% CI.