Understanding Cortical Streams from a Computational Perspective.

The two visual cortical streams hypothesis, which suggests object properties (what) are processed separately from spatial properties (where), has a longstanding history, and much evidence has accumulated to support its conjectures. Nevertheless, in the last few decades, conflicting evidence has mounted that demands some explanation and modification. For example, existence of (1) shape activities (fMRI) or shape selectivities (physiology) in dorsal stream, similar to ventral stream; likewise, spatial activations (fMRI) or spatial selectivities (physiology) in ventral stream, similar to dorsal stream; (2) multiple segregated subpathways within a stream. In addition, the idea of segregation of various aspects of multiple objects in a scene raises questions about how these properties of multiple objects are then properly re-associated or bound back together to accurately perceive, remember, or make decisions. We will briefly review the history of the two-stream hypothesis, discuss competing accounts that challenge current thinking, and propose ideas on why the brain has segregated pathways. We will present ideas based on our own data using artificial neural networks (1) to reveal encoding differences for what and where that arise in a two-pathway neural network, (2) to show how these encoding differences can clarify previous conflicting findings, and (3) to elucidate the computational advantages of segregated pathways. Furthermore, we will discuss whether neural networks need to have multiple subpathways for different visual attributes. We will also discuss the binding problem (how to correctly associate the different attributes of each object together when there are multiple objects each with multiple attributes in a scene) and possible solutions to the binding problem. Finally, we will briefly discuss problems and limitations with existing models and potential fruitful future directions.