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. 2015 Jul 6;4:e06444. doi: 10.7554/eLife.06444

Figure 3. Differential sensitivity of gamma oscillations and grid fields to changes in the strength of E and I synapses.

(A–C) Examples of inhibitory (red) and excitatory (blue) synaptic currents recorded respectively from excitatory and inhibitory neurons from simulations highlighted by arrows in panels (D–F). (D–F) Top: Correlation value at the first local maximum of the autocorrelation of inhibitory synaptic currents (I → E cells, 25 randomly selected E cells), plotted as a function of gE and gI, for networks without noise (D), with noise level set to σ = 150 pA (E), and noise level set to σ = 300 pA (F). Each point is an average over five simulation trials. In these simulations velocity and place cell inputs were disabled. The duration of simulations was 10 s. Bottom: Frequency corresponding to the peaks of the autocorrelation functions for simulations in the top panels. Black lines in (E) indicate the region from Figure 2E where the gridness score = 0.5. (G) Scatter plots show gridness score as a function of gamma oscillation strength (top) and frequency (bottom) for simulations with noise absent (green), with an intermediate level of noise (red) and highest simulated noise level (blue). Each dot represents data from a single network configuration. (H) Top: Gamma oscillation strength plotted as a function of standard deviation of the noise current. Grey color indicates simulations with gE = 3 nS, gI = 1 nS (A). Red color indicates simulations with gE = 1 nS, gI = 3 nS (B). Bottom: Frequency corresponding to the detected autocorrelation peak.

DOI: http://dx.doi.org/10.7554/eLife.06444.012

Figure 3.

Figure 3—figure supplement 1. Sensitivity of gamma oscillations to changes in the strength of E and I synapses in networks with connection probability between pairs of neurons drawn according to the synaptic profile functions in Figure 1B.

Figure 3—figure supplement 1.

(A–C) Examples of inhibitory (red) and excitatory (blue) synaptic currents recorded respectively from excitatory and inhibitory neurons from simulations highlighted by arrows in panels (D–F). (D–F) Top: Correlation value at the first local maximum of an autocorrelation of inhibitory synaptic currents (I → E cells, 25 randomly selected E cells), plotted as a function of gE and gI, for networks without noise (D), with noise set to σ = 150 pA (E), and noise set to σ = 300 pA (F). Each point is an average over five simulation trials. In these simulations velocity and place cell inputs were disabled. The duration of simulations was 10 s. Bottom: Frequency corresponding to the peaks of the autocorrelation functions for simulations in the top panels. Black lines in (E) indicate the region from Figure 2—figure supplement 1 where the gridness score = 0.5.
DOI: http://dx.doi.org/10.7554/eLife.06444.013
Figure 3—figure supplement 2. Scatter plots of gridness score as a function of the amplitude of gamma oscillations.

Figure 3—figure supplement 2.

(A–C) The plots show relationships between grid field computations (gridness score) and the power of nested gamma oscillations for deterministic networks (A), networks with moderate noise (B) and networks with the highest simulated noise level (C). Noise level is indicated by σ. The strength of the oscillation was obtained by computing autocorrelation functions of inhibitory currents impinging onto 25 randomly selected E cells in the network and detecting their first local maxima. The correlation value at the first local maximum is plotted on the abscissa. Color coding determines the values of gE and gI, as shown in the 2D colorbar.
DOI: http://dx.doi.org/10.7554/eLife.06444.014
Figure 3—figure supplement 3. Scatter plots of gridness score as a function of the detected oscillation frequency.

Figure 3—figure supplement 3.

(A–C) The plots show relationships between grid field computations (gridness score) and the frequency of gamma oscillations for deterministic networks (A), networks with moderate noise (B) and networks with the highest simulated noise level (C). Noise level is indicated by σ. The frequency of the oscillation was obtained by computing autocorrelation functions of inhibitory currents impinging onto 25 randomly selected E cells in the network and detecting their first local maxima. The time lag at the first local maximum yielded the frequency of the oscillation, which is plotted on the abscissa. Color coding determines the values of gE and gI, as shown in the 2D colorbar.
DOI: http://dx.doi.org/10.7554/eLife.06444.015
Figure 3—figure supplement 4. Amplitude and frequency of gamma oscillations in the gE and gI parameter regions where grid fields are robust.

Figure 3—figure supplement 4.

Amplitude (top) and frequency (bottom) of detected gamma oscillations for simulations in which gridness score is greater than 0.5, in deterministic networks (A), networks with an intermediate level of noise (B) and in networks with the highest simulated level of noise (C). The data in this figure are from simulations in Figure 3.
DOI: http://dx.doi.org/10.7554/eLife.06444.016