Reservoir computing model of prefrontal cortex creates novel combinations of previous navigation sequences from hippocampal place-cell replay with spatial reward propagation
Fig 7
A. Distribution of snippets drawn from the sequences illustrated in Fig 1B, 1C and 1D. Globally we observe snippet selection favors snippets from the beginning of sequence ABCED (blue), the middle of EBCDA (yellow), and the end of sequence BACDE (pink), which corresponds exactly to the efficient sub-sequences (ABC, BCD, and CDE) of these three sequences. This distribution of snippets is used to train the model. The results of the training are illustrated in panel B. Here we see a 2D histogram of sequences generated by the model in the ABCDE recombination experiment. The 2D trajectory histogram is generated by superposing the trajectories generated when 10 batches of 100 reservoirs each were trained and each model instance was evaluated 10 times with noise. Panel C displays the Frechet distance between the autonomously generated sequence and the four reference sequences. Kruskal-Wallis comparison confirms that the trajectories generated autonomously are significantly more similar to the target sequence ABCDE than to the experienced non-efficient sequences (p < 0.0001).