Uncovering the organization of neural circuits with Generalized Phase Locking Analysis
Fig 7
Neural field simulation using EPSP as LFP proxy.
(A) Simulation with weak recurrent inhibition. Example time course at center location for exogenous input (top), E- and I- populations rates (bottom). (B) Same as A for strong recurrent inhibition. (C) gPLV as a function of frequency for both models. (D) Phase of spike vector coefficients as a function of their modulus for the frequency band yielding maximum gPLV for both models (each dot one coefficient, and the continuous lines are plotted based on linear regression). (E) Shift between averaged phase of spike vector and averaged phase of LFP vector, as a function of frequency. (F) Schematic of the spike vector’s phase gradient in the two models according to Eq 8. X-axis is the distance from center and y-axis is the connectivity strength. Line color indicates the phase according to the colorbar on the right. (G) Resulting GPLA in 3 frequency bands (indicated on the left) for weak recurrent inhibition (model schematized in A). (H) Same as G for strong recurrent inhibition (model schematized in B). In both G and H, color of pixel code the values of spike/LFP vector coefficients, with colorbar on top of H. Colors are represented in HSV mode, in which a complex number (reiϕ) is represented by hue and brightness of a pixel. Hue of a pixel indicates the phase (ϕ) and the brightness of a pixel indicates the magnitude (r). Related supplementary Figures: S5 Fig, Phase-modulus relation dependency on level of inhibition; S6 Fig, GPLA using IPSP as LFP proxy.