By contrast, the MUA-LFP PPC implicitly weights each SUA that goes into the MUA mixture according to its firing rate: SUAs with higher firing rates will influence the MUA-PPC more than SUAs with lower firing rates. Consequently, the difference between the attentional effects on MUA and SUA PPC might be explained through one of the following scenarios or a combination of both: (1) with attention, SUAs with particularly high firing rate, and therefore particularly strong MUA contribution, might increase their gamma locking particularly strongly, and (2) with attention,

SUAs with particularly strong gamma locking might increase their firing rates particularly strongly and thereby contribute more to the MUAs. In both cases, the correlation between rates and gamma locking should increase with attention. To test this prediction, we calculated the Spearman rank correlation across SUAs, between the SUA rates and the PPC, and separately HDAC activation for the two attention conditions and show their difference between attention conditions in Figure 7A. We found that our prediction

held, selectively in the gamma-band (NS and BS, p < 0.05 and p < 0.01 respectively, bootstrap test). In fact, PPC-rate correlations were significantly greater than zero when attention was inside the neurons’ RF (NS: Spearman ρ = 0.50, p < 0.001; BS: 0.62, p < 0.001; NNS = 21, NBS = 39 for Figure 7; see Figures S1G–S1J and S6 for monkey M1 and Figures S1G–S1J and S7 for monkey M2), but not when it was outside the RF (NS: −0.07, Venetoclax purchase n.s.; BS: −0.02, n.s.). This analysis was done on the absolute SUA firing rates during sustained activation, which is a function of both baseline firing rate (defined here from fixation onset to stimulus onset), and the change in firing rate during visual stimulation relative to baseline. To investigate their relative contributions, we entered these two variables

4-Aminobutyrate aminotransferase into a multiple regression model (with every unit as one observation), predicting SUA PPC, separately for each attention condition. We show the difference in regression T-statistics between attention conditions in Figures 7B and 7C. The effect described above for the overall sustained firing rates held for both the baseline rate (BS and NS, p < 0.01 and p < 0.05 respectively, bootstrap test) and the rate change relative to baseline (BS and NS, p < 0.01 and p < 0.05 respectively, bootstrap test). The effect was again specific for the gamma-frequency band. In fact, a unit’s baseline firing rate (NS: T-stat = 2.71, p < 0.01; BS: 3.51, p < 0.001) positively predicted its gamma PPC when selective attention was directed inside its RF, but not when it was directed outside its RF (NS: −0.39; BS: −0.15, all n.s.). Similarly, a BS cell’s firing rate change relative to baseline (NS: T-stat = 1.59, n.s.; BS: 3.86, p < 0.01) positively predicted its gamma PPC when selective attention was directed inside its RF, but not when it was directed outside its RF (NS: −0.9, n.s.; BS: 0.06, n.s.).