A recurrent neural network framework for flexible and adaptive decision making based on sequence learning
Fig 5
Task 1: Network units’ responses.
a. An example unit that prefers the left target. Its activity increases when the evidence supporting the left target grows and decreases when it drops. The unit’s responses converge when the network chooses its preferred target. The trials are grouped into quartiles by the total logLR in each epoch. The colors indicate the quartiles, and the error bars indicate the SE across trials. b. Population responses of the units that are selective to the total logLR. The trials are grouped based on the total logLR supporting each unit’s preferred target in each epoch. The error bars in panels b, c, and d indicate the SE across units. c. Urgency units. Their activities ramp up (upper panel) or down (lower panel) regardless of choice. d. Network unit response variability. The neurons’ response variability increases initially (blue curve) but decreases before the choice, more so when the preferred target is chosen (black) than when the non-preferred target is chosen (grey). Only the trials with more than five shapes are included in panel a, b, and d.