Fig. 3.

Cell-type–specific neuromodulation guides learning across multiple tasks. (A) Learning to produce a time-resolved target output pattern. (B) A delayed match to sample task, where two cue alternatives are represented by the presence/absence of input spikes. (C) An evidence-accumulation task (29, 36). (Lower) Addition of cell-type–specific modulatory signals improves learning outcomes across tasks. In line with these results, SI Appendix, Fig. S2 shows that gradients approximated by MDGL are more similar to the exact gradients than those approximated by e-prop. Solid lines/shaded regions: mean/SD of loss curves across runs (Methods).