7 A and D. p < 10'9 in each of the five sites, two-sample t-test of the difference of the linear regression coefficients in normalized data).

Positive connectivity decreased with Aori (p < 0.005 in each of the five sites, t-test on the logistic regression coefficient) whereas negative connectivity did not decrease (Fig.

7 G): The slope in the logistic model of connectivity with respect to Aori was significantly higher for positive than for negative interactions (p < 0.04 in each of the five sites, two-sample t-test of the difference of the logistic regression coefficient).

For repeated measures of means, statistical difference was determined using Tukey’s honestly significant criteria, while a Student t-test (one or two-sample) was used for comparison of one or two values.

Further, voltage fluctuations were not able to significantly reduce rheobase values in the presence of -5 nS conductance (Fig 11Di; one sample Student t-test, P = 0.58), while with 15 nS rheobase significantly increased (Fig 11Di; one sample Student t-test , P <0.001).

With -5 nS conductance voltage fluctuations were not able to significantly increase firing rate from zero (Fig 11Cii; one sample Student t-test, P = 0.79), while these changes were significant under control and with 15 nS (Fig 11Cii; one sample Student t-test , P <0.001).

The leftward shift in rheobase increased from 64.4 i 7.7 pA to 134 i 25 pA (Fig 9Ei; paired Student t-test, P = 0.001, n = 10), while the slope factor increased from 18.0 i 1.3 pA to 29.6 i 4.7 pA (Fig 9Eii; paired Student t-test , P = 0.02, n = 10).

As shown, voltage fluctuations induced a leftward shift in rheobase relative to conditions without fluctuations (Fig 1F; 56 i5.6 pA, P <0.001, Student t-test , n = 20) and resulted in spike-probability curves with an average slope of 26 i 8.4 pA (mean i s.e.m).

This was done by moving a sliding window (width 50 trials) across the normalised training extent and performing a t-test for each point in time.

The trial at which the t-test indicated that the normalised learning extent after that trial was significantly above zero (at an a-level of 0.000625 for Bonferroni correction) was taken as the time point at which participants realised the role of the shape and started to learn the mappings.

This difference in the amount of learning between the two conditions was significant ( t-test , p = 0.03) even with our relatively small sample size.

It turns out that even for this comparison, when the number of examples per pair are equalised across conditions, the learning extent in the 2-Pair Condition is significantly larger than the 5-Pair Condition ( t-test , p = 0.02).

Results were searched for the 175 ORFs with peak in 3’ UTR compared with all other ORFs; they show that these ORFs are characterized by significant lowering of both poly(A) halflife (t-test: p < 2.5 X 10—2) and overall halflife ( t-test : p < 1 x 10—2), indicating their production of unstable mRNAs (see Fig.

The statistical significance of properties and classifications has been assessed by means of Fish-er’s exact test and t-test .

A t-test is a statistical hypothesis test in which the test statistic follows a Student’s tdistri-bution if the null hypothesis is supported.

To estimate the error and sample size, we calculated the propagation of error in COOPu and COOPC, and used them in the student t-test to calculate statistical significance.

We calculated the t-value and degrees of freedom to complete the t-test .

We assumed significance for a p-value less than 0.05 for the two sample t-test .