Fig. 13.

Signal detectability in afferent spike trains when spikes are added at random to baseline discharge. A, The detection strategy is based on a binary hypothesis test. In the presence of a signal, the baseline spike count distribution (Baseline) is shifted by an amount equal to the increase in number of spikes caused by signal (Baseline + Signal). A threshold (vertical dashed line) defines the probability of detection (gray area, P_d) and probability of false alarm (black area, P_fa). By constraining P_fa, P_d can be maximized.B, Spikes (abscissa) were added randomly to blocks of T = 100 EOD periods, and a signal detection algorithm (see Results) was given the task of determining whether signal was present subject to P_fa ≤ 0.001. Ordinate is P_d, and abscissa is number of extra spikes caused by signal. Detection experiments were simulated in afferent (○), and surrogate spike trains from binomial (B, □), zeroth-order Markov (M₀, ◊), and first order Markov (M₁, ▵) processes. For the afferent, signal detection performance at 90% (dashed line) is possible with as few as 2–3 spikes over the baseline of 35 spikes. Surrogate spike trains required more spikes to achieve the same level of performance (see Results).