Figure 17:

Principles of connectivity in DeepLeabra. a) Markov et al (2014) data showing density of retrograde labeling from a given injection in a middle-level area (d): most feedforward projections originate from superficial layers of lower areas (a,b,c) and deep layers predominantly contribute to feedback (and more strongly for longer-range feedback). b) Summary diagram showing most feedforward connections originating in superficial layers of lower area, and terminating in layer 4 of higher area, while feedback connections can originate in either superficial or deep layers, and in both cases terminate in both superficial and deep layers of the lower area (adapted from Felleman & Van Essen, 1991). c) Anatomical hierarchy as determined by percentage of superficial layer source labeling (SLN) by Markov et al (2014) — the hierarchical levels are well matched for our model, but we functionally divide the dorsal pathway (shown in green background) into the two separable components of a Where and a What * Where integration pathway. d) Superficial and deep-layer connectivity in the model. Note the repeating motif between hierarchically-adjacent areas, with bidirectional connectivity between superficial layers, and feedback into deep layers from both higher-level superficial and deep layers, according to canonical pattern shown in panels a and b. Special patterns of connectivity from TEO to V3 and V2, involving crossed super-to-deep and deep-to-super pathways, provide top-down support for predictions based on high-level object representations. e) Connectivity for deep layers and pulvinar in the model, which generally mirror the corticocortical pathways (in d). Each pulvinar layer (p) receives 5IB driving inputs from the labeled layer (e.g., V1p receives 5IB drivers from V1). In reality these neurons are more distributed throughout the pulvinar, but it is computationally convenient to organize them together as shown. Deep layers (d) provide predictive input into pulvinar, and pulvinar projections send error signals (via temporal differences between predictions and actual state) to both deep and superficial layers of given areas (only d shown). Most areas send deep-layer prediction inputs into the main V1p prediction layer, and receive reciprocal error signals therefrom. The strongest constraint we found was that pulvinar outputs (colored green) must generally project only to higher areas, not to lower areas, with the exceptions of DPp → V3 and LIPp → V2. V2p was omitted because it is largely redundant with V1p in this simple model.