Reservoir computing model of prefrontal cortex creates novel combinations of previous navigation sequences from hippocampal place-cell replay with spatial reward propagation
Fig 6
Longer paths are rejected (left), and stronger rewards are favored (right). Panels A and B illustrate snippet counts for T maze trajectories pictured in panels C and D. In Panel C, sequences begin at location A, and rewards are given at locations C and D. Based on the reward proximity and propagation, there is a higher probability of snippets being selected along path AC than path AD. This is revealed in panel A, a histogram of snippets for the sequences ABC (in Blue) and ABD (in Orange). Panels B and D illustrate how distance and reward intensity interact. By increasing the strength of the reward, a longer trajectory can be rendered virtually shorter and more favored, by increasing the probability that snippets will be selected from this trajectory, as revealed in Panel B. Panels C and D reveal the 2D trajectory histograms 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 E and F confirm a robust tendency to generate autonomously sequences significantly similar to the ABC and ABD sequence respectively (p-value = 0).