Article Figures & Data
Figures
- Figure 1.
Data, Noise model and Burst model signals. A, Data recorded from visually driven macaque V1. B, Noise model: simulated by assigning random phases to the Fourier amplitude spectrum of the stimulated data and taking the inverse transform. C, Burst model: simulated by generating background noise from the spontaneous activity amplitude spectrum as in B summed with bursts fit to the power spectrum of the stimulated data. D, Spectrograms and power spectra. Power spectrum of Burst model matched the data's spectrum.
- Figure 2.
Example of autocoherence analysis of V1 LFP data. A, Gamma-band spectrogram of a stimulated V1 LFP recording. B, Time course of the recording in A with numbered bursts. C, Phase portraits of detected gamma-band bursts. Numbers and colors of bursts correspond to matching periods in the time course in B. Frequencies of bursts are labeled above each portrait, arrows in portraits show direction of increasing time, and burst color indicates mean phase of bursts as coded by the color bar in B. Range of burst frequencies and times shown here are representative of those typically measured in V1 LFP recordings.
- Figure 3.
Empirical joint probability distributions of measured burst frequencies and time scales over entire population of 53 sites. A, Distribution of bursts measured in Noise model simulations. B, Distribution of bursts measured in the data. Distribution peak: 40 Hz and 71 ms. C, Distribution of bursts measured in Burst model simulations.
- Figure 4.
Statistical results. A, Marginal frequency distributions. B, Histogram of p values using the KS test for data and Noise model (green) and data and Burst model (red). C, p values described in B plotted versus gamma-band power; p values were not correlated with the amount of gamma activity.
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