A connectivity gradient in structured reservoir computing predicts a hierarchy for mixed selectivity in human cortex

One of the initial motivations for reservoir computing was the effort to understand how recurrent connectivity could explain observations of neural activity patterns in the prefrontal cortex of behaving primates. Recurrent connections provide for the projections of inputs into a high dimensional space. In individual reservoir units, this results in activation patterns that display non-linear mixtures of inputs and abstract internal states, referred to as mixed selectivity, which has been identified as a key component of reservoir activity. Interestingly it is also a key element in primate brain activity. An equally prominent characteristic of primate brain activity is a temporal processing gradient, with fast input driven responses in sensory areas, and progressively prolonged time constants in increasingly associative cortical areas. Recent research has explained a temporal integration hierarchy as a function of local connectivity within structured reservoirs. As in the primate brain, areas that receive sensory input have fast integration. Via local connections, this input driven activation flows through local connections to progressively distant areas, thus physically implementing the temporal integration gradient. In the current research we test the hypothesis that this physical hierarchy will also produce a gradient in mixed selectivity in the structured reservoirs. Indeed, simulations demonstrate that reservoirs constrained by a connection distance rule produce a gradient of mixed selectivity, with mixed selectivity progressively increasing from input-driven to more distant associative areas. This allows us to predict that the same kind of gradient for mixed selectivity should be observed in the human cortex. In order to test this prediction, we exploited human brain activation data that included a form of multidimensional narrative structure that was well suited for characterizing mixed selectivity. Applying the same analysis from the reservoir analysis, we observe the presence of mixed selectivity in human cortex, and evidence for a gradient from lower-level sensorimotor areas, to higher level integrative areas. This research contributes to the characterization of the principals of computation in anatomically structured reservoirs and the human brain.