We used responses to large gratings (10°) drifting in 16 different directions (conditions averaged in Fig. 2A). To quantify across recording sites, we defined the preferred orientation (that inducing maximal gamma power) at each site to be 0°. The average power spectra for a subset of orientations are shown in Figure 2D (left; same n = 209 sites as in Fig. 2A). On average, gamma power at the preferred orientation was approximately twice that of the orthogonal orientations. However, the peak frequency of gamma was not significantly modulated by stimulus orientation, with the peak frequency at the preferred and orthogonal orientations nearly indistinguishable (36.8 ± 0.3 vs 36.6 ± 0.3 Hz, p = 0.6, t test; Fig. 2D, middle). When arranged by the preferred orientation of gamma power, spike rate tuning functions showed a response at the preferred orientation that was significantly <1, indicating that the preferred orientation defined by spike rates was often different from gamma at individual sites (Berens et al., 2008; Jia et al., 2011).
Make of gamma age bracket
We simulated the stimulus dependence of spiking activity and gamma power and peak frequency with a simple three component model, an extended but simplified version of the model developed by Kang et al. (2010). The model consisted of a local excitatory (E), local inhibitory (I), and global (G) excitatory component (see Fig. 1 and Materials and 
Methods). The local E and I components represent populations in a local V1 region, such as in a cortical hypercolumn. The local E and I components were recurrently connected-the excitatory component provided input to the inhibitory component (WEI) and to itself (WEE), and similarly for the inhibitory component (WWeb browser and WII). Both the E and I components also received external and independent Poisson-distributed input (IE and II). This architecture captures the basic pyramidal-interneuron network gamma (PING) model, commonly used to model gamma generation (Bartos et al., 2007; Tiesinga and Sejnowski, 2009; Whittington et al., 2011).
The global role brings together excitatory pastime when you look at the several regional nations (W
The third component-the global or G component-represents a more spatially extensive mechanism. Eg), and affects both the local E and I components through excitatory connections (WGE and WGI, respectively). G might arise from long-distance horizontal connections between columns in V1 or feedback from higher visual areas (Angelucci and Bresslof, 2006). Figure 3, A and B, shows the behavior of the E component of the model for a small and large high contrast “gratings” (i.e., input), respectively. The mean activity of E is higher for the smaller stimulus. Spectral analysis shows the presence of elevated gamma frequency components in the activity of E, which are weaker and at a higher peak frequency for small gratings (centered around 50 Hz, Fig. 3C) than large gratings (?40 Hz, Fig. 3D). The peak frequency of gamma shifts lower as power increases because the time constant for the global component, recruited more strongly by the large stimulus, is slower than for the local E and I, lowering the resonant frequency of the network. Note that for both simulations gamma power fluctuates in time, consistent with previous analysis of physiological data (Burns et al., 2010a,b, 2011).
Example simulation answers. An excellent, Response of one’s Elizabeth aspect of a small grating (r = 3), while the a purpose of date. Notice the strong suggest impulse, together with visibility regarding transient gamma action. Faster gratings (roentgen = several) lead stronger responses, but deficiencies in gamma power to image in a single trial. B, Response of one’s Age element of an enormous grating (r = 5). Mention the newest ma band activity. C, D, Spectrogram of epochs shown in the A good and you may B, respectively. Gamma activity are weaker as well as a higher frequency to the short grating. Spectra was in fact computed within the a sliding 512 ms windows, built at the time shown; spectra have been smoothed for screen simply, of the convolving a-two-dimensional Gaussian kernel on the analysis.
