m8ta
use https for features. 

{1520}  
PMID15142952 Visual binding through reentrant connectivity and dynamic synchronization in a brainbased device
 
{1079}  
PMID21147836[0] Resting oscillatory corticosubthalamic connectivity in patients with Parkinson’s disease
____References____
 
{1073}  
PMID12040070[0] Enhanced synchrony among primary motor cortex neurons in the 1methyl4phenyl1,2,3,6tetrahydropyridine primate model of Parkinson's disease.
____References____
 
{1076}  
PMID17017503[0] Synchronizing activity of basal ganglia and pathophysiology of Parkinson's disease.
____References____
 
{763}  
I recently wrote a matlab script to measure & plot the autocorrelation of a spike train; to test it, I generated a series of timestamps from a homogeneous Poisson process: function [x, isi]= homopoisson(length, rate) % function [x, isi]= homopoisson(length, rate) % generate an instance of a poisson point process, unbinned. % length in seconds, rate in spikes/sec. % x is the timestamps, isi is the intervals between them. num = length * rate * 3; isi = (1/rate).*log(1rand(num, 1)); x = cumsum(isi); %%find the x that is greater than length. index = find(x > length); x = x(1:index(1,1)1, 1); isi = isi(1:index(1,1)1, 1); The autocorrelation of a Poisson process is, as it should be, flat: Above:
The problem with my recordings is that there is generally high longrange correlation, correlation which is destroyed by shuffling. Above is a plot of 1/isi for a noise channel with very high mean 'firing rate' (> 100Hz) in blue. Behind it, in red, is 1/shuffled isi. Noise and changes in the experimental setup (bad!) make the channel very nonstationary. Above is the autocorrelation plotted in the same way as figure 1. Normally, the firing rate is binned at 100Hz and highpass filtered at 0.005hz so that longrange correlation is removed, but I turned this off for the plot. Note that the suffled data has a number of different offsets, primarily due to differing longrange correlations / nonstationarities. Same plot as figure 3, with highpass filtering turned on. Shuffled data still has far more local correlation  why? The answer seems to be in the relation between individual isis. Shuffling isi order obviuosly does not destroy the distribution of isi, but it does destroy the ordering or pairwise correlation between isi(n) and isi(n+1). To check this, I plotted these two distributions:  Original log(isi(n)) vs. log(isi(n+1)  Shuffled log(isi_shuf(n)) vs. log(isi_shuf(n+1)  Closeup of log(isi(n)) vs. log(isi(n+1) using alphablending for a channel that seems heavily corrupted with electrocauterizer noise.  
{1075}  
PMID19070616[0] Pathological synchronisation in the subthalamic nucleus of patients with Parkinson's disease relates to both bradykinesia and rigidity.
____References____
 
{1063}  
PMID11923450[0] Synchronized neuronal discharge in the basal ganglia of parkinsonian patients is limited to oscillatory activity.
____References____
 
{908}  
PMID4041789 Synchrony between cortical neurons during operant conditioning.
 
{712}  
PMID19245368[0] The influence of learning on sleep slow oscillations and associated spindles and ripples in humans and rats
____References____
 
{710}  
PMID19233172[0] Synchronisation in the beta frequencyband  The bad boy of parkinsonism or an innocent bystander?
____References____
