Figure 5.

Computational model of reward-dependent synaptic plasticity is able to account for the development of item specificity in behavior-selective cells. (A) Network performance for item sampling events occurring before and after learning (labeled as “first” and “last trials”). (B, top) Mean firing rates of model Go and NoGo cells before and after learning for the rewarded and nonrewarded item. (Bottom) Mean firing rates associated with Go and NoGo responses. (C) Mean firing rates of model Go and NoGo cells on correct and error trials for the rewarded and nonrewarded items during item sampling events occurring before learning. (D) Mean synaptic weights (W_{Item,CellType}) changes as a function of the item sampling event number. G = Go cells; NG = NoGo cells. Results represent the mean over 100 simulations.