Fig. 5: Lap-neurons and event-specific representations.
a A CSCG was trained on observations from four laps around a square maze similar to21. The training sequence consisted of one start state, followed by four repetitions of the sequence 1 → 2 → 3, .., 12, and then a goal/reward state at the end. It learned to predict the laps perfectly, including the reward at the end of the fourth lap, and planning to get the reward returned the correct sequence of actions. b Clone activations (see color map) for the four different laps. Rows correspond to clones. The activations show that there are different clones that are maximally active for different laps, but the other clones are partially active at their corresponding locations, similar to the neurophysiological observations in21 regarding event-specific-representations. c Place cell traces from21, included with permission. d The event-specific representations persist even when the maze is elongated by repeating the observations along the corridor. The CSCG is not trained on the elongated maze. e Visualization of the circuit learned by the CSCG including the transition graph, connections from the observations, and activation sequences for laps 1 and 2. The CSCG learned one clone per lap for each position. Smoothing in the CSCG explains why other clones of other laps are partially active. Each row shows how the clone activations transition from observation 1 (left) to observation 12 (right) for the corresponding lap. The active observations are colored in correspondence with a, and clone activations are graded in intensity with darker shades being stronger. Overall the visualizations show the circuit dynamics that give rise to the activity traces in b.
