Figure 3.

Comparison of the signal detection characteristics of single neurons versus ensembles. a) ACC single unit ROCs (gray curves) were constructed from hit and false-alarm rates during the ‘common-segment’ periods for each neuron. Hits and false-alarms were based on running correlations between a neural activity template derived from half of the trials in one sequence block and activities in the remaining half trials in the same sequence-block (‘hits’) or with activities in the alternate sequence-block (‘false alarms’). The ensemble ROC (solid black line) detected the correct sequence better than the majority of the single units or the time-bin shuffled ensemble controls (dashed black line). b) DS single unit ROCs (gray curves) were constructed from hit and false-alarm rates during the common-segments using the same methods as in (a). The ensemble ROC (solid black line) detected the correct sequence better than its shuffled control (dashed black line) but was eclipsed by many more single units than in the ACC. c) Ensemble decoding performance improved with increasing ensemble size in both the ACC and DS, but at a much higher rate in the ACC. Ensembles of different sizes were randomly drawn from ACC and DS neuronal populations, and the AUCs were calculated from the sequence signal-detection ROCs. This process was repeated 100 times at each ensemble size for each region, and the mean and s.e.m. are plotted (ACC: black circles, DS: gray circles). The best fitting trend lines were power functions (ACC: top, DS: bottom), which explained more than 99% of the variance.