MINIDETECTIONSIMULATION[OPT_THRESHOLD,DETECTOR,CONV_SCALE,STATS]=vlt.neuro.minis.minidetectionsimulationSolves,throughsimulation,theoptimumthresholdfordetectingminiatureexcitatoryorinhibitorypost-synapticpotentialsorcurrents.
Minisareassumedtoarriveonabackgroundofpuregaussiannoisewithastandarddeviationof1.(Onecandividethethresholdbytheactualnoiseencounteredinone's own data to obtain the equivalent value for real data.) OPT_THRESHOLD - the threshold that produced the fewest overall errors of detection, weighing false positives and false negatives equally (because both errors have the same impact on average mini frequency and amplitude). DETECTOR - The filter used for detection. It assumes a positively-going wave (simply multiply this by -1 to switch sign). CONV_SCALE - The amount by which the convolution of the data and the DETECTOR should be scaled to reproduce the detection values used in this simulation (depends on sampling rate and number of samples). STATS - a structure with error rates for different values of threshold. The behavior of the function can be altered by passing name/value pairs: Parameter (default) | Description ------------------------------------------------------------------------------ DT (0.001) | The time step between adjacent samples Tau_Onset (0.0025) | The time of the synaptic potential onset (in seconds) Tau_Offset (0.0250) | The time of the synaptic potential offset (in seconds) Simulation_Duration (100) | The simulation duration (seconds) expected_rate_of_events (10) | The expected rate of real events (Hz) noise_fraction (0.1) | The fraction that Tau_Offset and Tau_Onset should be | altered by noise in the simulation threshold_steps (50) | Threshold steps to examine refractory_period (0.020) | Refractory period (time between events can be no shorter | than this, in seconds) plotit (0) | Plot the threshold vs. errors curve