A recurrent neural network framework for flexible and adaptive decision making based on sequence learning
Fig 7
a. The normalized subthreshold activities of 10 shape output units. We show the shape output units’ activities at the time step immediately before each shape onset for all epochs in all trials. The sum of the activities of all shape output units is normalized to 1. Data are divided into 10 groups by the total logLR before the shape onset, which is indicated by the color. b. The Kullback-Leibler (KL) divergence between the normalized subthreshold activities (as shown in Fig 7A) and the sampling distributions (shown in Fig 2B inset). Data are grouped by the total logLR. The error bars indicate the SE across runs.