Figure 1.
Simultaneous multisite LFP and unit recordings in awake cats.
Eight pairs of tungsten electrodes (placement illustrated on top) were inserted in cat cerebral cortex (area 5–7, parietal) as described in detail in [10]. The system simultaneously recorded LFPs (left) and multi-unit activity (right) at each pair of electrode.
Figure 2.
Detection of negative peaks in local field potentials and their relation with neuronal activity.
Top: detection of negative LFP peaks. The LFP signal is shown together with the detected nLFPs (circles). Middle: nLFP-based wave-triggered average (WTA) of unit activity, showing that the negative peaks were associated with an increase of neuronal firing. Bottom: rasters of nLFP activity. The same procedure is compared for high threshold (left panels) and low threshold (right panels).
Figure 3.
Avalanche analysis of nLFPs in the awake cat.
The nLFP avalanche size distributions were computed according to an avalanche analysis (see text). For a high detection threshold, the avalanche distribution is better fit by a power-law (left panels); for a low detection threshold, it is better explained by an exponential distribution (right panels).
Table 1.
Results of avalanche-analysis (summed LFP amplitudes).
Table 2.
Results of the avalanche size analysis (number of LFP peaks).
Figure 4.
Avalanche analysis of positive LFP peaks in the awake cat.
A. Detection of positive LFP peaks using identical procedures as for nLFPs. B. The WTA indicates no relation between pLFPs and unit activity. C. Scaling of avalanche size distribution, showing similar behavior as observed for nLFPs (compare with Fig. 3).
Figure 5.
Avalanche analysis of shuffled negative LFP peaks.
A. Shuffled peaks obtained from randomizing the timing of nLFP peaks. B. The WTA indicates that shuffling removes the relationship between nLFPs and neural activity C. Scaling of avalanche peak size distribution, showing similar behavior as for nLFPs (compare with Fig. 3).
Figure 6.
Avalanche-size distributions of negative LFP peaks from single channels.
The peak distribution is shown on log-linear (A) and logarithmic scale (B).
Table 3.
Results of avalanche-analysis for single-electrode LFP peaks.
Figure 7.
Peak-size distributions for the thresholded Poisson shot-noise process.
A. Sample of the stochastic process and detected peaks. B. Peak size distribution on a log-linear scale. C. Same distribution on a log-log scale. Straight lines indicate the best fit obtained using linear regression.
Figure 8.
Peak amplitude distribution for the Shot-Noise model.
Single-barrier case (A,B) on a log-linear scale (A) and on a log-log scale (B) show a globally linear trend. Excursions (C,D) show exactly the same profile. Simulation parameters: intensity of the process ,
,
,
,
, maximal value of peaks considered
(see text).
Table 4.
Results of avalanche-analysis for thresholded stochastic processes.
Figure 9.
Peak amplitude distribution for the Ornstein-Uhlenbeck process.
(A,B): single-barrier peaks, on a log-linear scale (A) and on a log-log scale (B), and excursions (C,D), on a log-linear scale (C) and on a log-log scale (D). Both case present the same profile and a globally linear trend for both axis. Simulation parameters: intensity of the process ,
,
,
,
, maximal value of peaks considered:
(see text).
Figure 10.
Avalanche analysis of a simulated neural network displaying self-organized criticality.
The power-law distribution provides a very good graphical fit (A), whereas the exponential distribution provides a poor fit (B). Data from ref. [8].
Table 5.
Results of avalanche-analysis for the artificial network model [8] at criticality.