Reservoir computing model of prefrontal cortex creates novel combinations of previous navigation sequences from hippocampal place-cell replay with spatial reward propagation
Fig 5
Illustration of snippet integration in reservoir state space.
Here we visualize the high dimensional reservoir space in a low (2D) PCA space, in order to see how pieces (snippets) of the overall sequence are consolidated. In this experiment, the sequence ABCDE is broken into snippets, which are then used to train the model. The challenge is that only local structure is presented to the model, which must consolidate the global structure. Panels A-C represent the state trajectory of reservoir activation after 100, 1000 and 10000 snippets. While each snippet represents part of the actual trajectory, each is taken out of its overall spatial context in the sequence. Panel D represents the trajectory of reservoir state during the complete presentation of the intact sequence. Panel C reproduces this trajectory, but in addition we see “ghost” trajectories leading to the ABCDE trajectory. These ghost elements represent the reservoir state transitions from an initial random state as the first few elements of each snippet take the reservoir from the initial undefined state onto the component of the ABCDE trajectory coded by that snippet.