Figure 4
The characteristics of the trajectories of reservoir dynamics during stimulus presentation. (A) Results of projecting the activity inside the reservoir computing model for individual chunks (colored intervals) into the space from PC1 to PC3. Responses are self-organized for each chunk. The input contains a random sequence. Responses are color-coded according to their selectivity for chunks. Top: for the RC1 module. Bottom: for the RC2 module. (B) Distances between chunks of low-dimensional trajectories of reservoir dynamics. The distances \({d}_{XY}\left(t\right)\) were measured with respect to the “apple” trajectory (Y was set to “apple”). The shaded area indicates the deviation \({\sigma }^{\text{X}}(t)\). Because presentation times vary between chunks, the midpoint of the presentation time was set to 0, and the relative time from that point was used to display the data. (C) The degree of separation \({r}_{\text{XY}}\) between trajectories. The left and right sides indicate RC1 and RC2, respectively. The degree was defined in the Methods sections. The smaller the value is, the longer the distance between trajectories. (D1) Change in the cumulative contribution ratio before and during learning. The ratios for RC1 and RC2 are displayed on the left and right sides, respectively. (D2) Changes in the effective dimension trajectories for RC1 and RC2 after an increasing number of learning iterations. See the “Methods” section for the calculation of the effective dimension. PC principal component.
