Reservoir computing model of prefrontal cortex creates novel combinations of previous navigation sequences from hippocampal place-cell replay with spatial reward propagation
Fig 3
The Temporal Recurrent Network (TRN) is a model of the prefrontal cortex (PFC) that takes into account cortico-cortical loops by defining a fixed recurrent adjacency matrix for the leaky integrator neurons that model PFC neurons. Inputs of the TRN are modelled hippocampus (HIPP) place-cells. During the training phase, place-cells activations are provided by the algorithmic model of SWR replay (red pathway), and the striatum model learns to predict the next place-cell activation from the PFC model states by modifying the synaptic weights that project the PFC model into the striatum model according to the delta learning rule. During the generation phase, the model is no longer learning and the place-cell activation patterns result from the new position of the agent, reconstructed with a Bayesian algorithm from the next place-cell activation prediction of the modeled striatum (blue pathway).