Blind Deconvolution of Postsynaptic Currents Figure 2. Processing of EPSC records. A, Selections from three records of postsynaptic current display typical EPSCs. The bottom trace shows the deconvolution of the third record. The color coding of the records applies as well to the subsequent panels. B, Estimates of the EPSC impulse response for the three experimental records of which segments are shown in panel A reveal the variability in EPSC kinetics. For records 1, 2 and 3, we detected respectively 7799, 825, and 3800 EPSCs. C, The probability distributions display the EPSC amplitudes detected by the algorithm for the three records. D, Cumulative probability distributions of inter-EPSC intervals for the three records (continuous lines) are adequately fit by single exponential functions (dotted lines). Perhaps because of a lack of true stationarity of the process, the magenta curve deviates most from a single exponential. E– G, Scatter plots relate the time delay to the amplitude ratio of successive pairs of EPSCs for the three records. No significant correlation between these variables is apparent. doi:10.1371/journal.pone.0038198.g002 10 pA. The significance of coloring the noise is that the typical noise correlation time was about 1 ms, close in timescale to the typical time constant of EPSC decay, making the assay more challenging. For each test, the algorithm was run to recover the EPSC impulse response as well as the times and amplitudes of events. To determine the temporal resolution of our algorithm, we tested its ability to detect individual EPSCs embedded in a series of paired EPSCs separated by increasing time delays. The separation at which individual events can be reliably distinguished depends on the signal-to-noise ratio, the event kinetics and amplitudes, and the algorithm. In this test paradigm, we achieved accuracy–that is, the fraction of test cases classified correctly–greater than 0.8 when EPSCs were separated by 250 ms (Fig. 4A). In contrast, EPSCs must be separated by at least 800 ms to obtain a similar accuracy with MiniAnalysis (Fig. S1). To assess the amplitude sensitivity of our algorithm, we generated a series of individual EPSCs with amplitudes ranging from –1 pA to –30 pA (Fig. 4B). As with the temporal-resolution assay, the accuracy curves of the amplitude-sensitivity assay have a sigmoidal shape. Because EPSCs are non-overlapping in this assay, we were not analyzing a complex record. Nevertheless, our algorithm improved upon MiniAnalysis by achieving an accuracy of 0.8 at –14 pA; MiniAnalysis reached the same level of accuracy at –19 pA. The standard deviation of the error in amplitudes was 5 pA for our algorithm and 7 pA for MiniAnalysis. The jitter in timing estimates had a standard deviation of 50 ms for our algorithm and of 90 ms for MiniAnalysis. To simulate a more realistic scenario, we synthesized complex records containing EPSCs with amplitudes drawn from a gamma distribution and intervals drawn from an exponential distribution (Fig. 4C). We compared the algorithm’s estimate with the true distributions of amplitudes and intervals (Fig. 4D and F). For each of the assays, we asked the algorithm to retrieve the EPSC impulse-response function. We compared the results to the ideal function obtained by low-pass filtered, mean AMPA kinetics PLoS ONE | www.plosone.org 5 June 2012 | Volume 7 | Issue 6 | e38198