Oscillatory Dynamics of Perceptual to Conceptual Transformations in the Ventral Visual Pathway

Theta and gamma frequency oscillations occur in the same brain regions and interact with each other, a process called cross-frequency coupling. Here, we review evidence for the following hypothesis: that the dual oscillations form a code for representing multiple items in an ordered way. This form of coding has been most clearly demonstrated in the hippocampus, where different spatial information is represented in different gamma subcycles of a theta cycle. Other experiments have tested the functional importance of oscillations and their coupling. These involve correlation of oscillatory properties with memory states, correlation with memory performance, and effects of disrupting oscillations on memory. Recent work suggests that this coding scheme coordinates communication between brain regions and is involved in sensory as well as memory processes. Copyright © 2013 Elsevier Inc. All rights reserved.

An analysis of response latencies shows that when an image is presented to the visual system, neuronal activity is rapidly routed to a large number of visual areas. However, the activity of cortical neurons is not determined by this feedforward sweep alone. Horizontal connections within areas, and higher areas providing feedback, result in dynamic changes in tuning. The differences between feedforward and recurrent processing could prove pivotal in understanding the distinctions between attentive and pre-attentive vision as well as between conscious and unconscious vision. The feedforward sweep rapidly groups feature constellations that are hardwired in the visual brain, yet is probably incapable of yielding visual awareness; in many cases, recurrent processing is necessary before the features of an object are attentively grouped and the stimulus can enter consciousness.

Mounting evidence suggests that 'core object recognition,' the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains poorly understood. Here we review evidence ranging from individual neurons and neuronal populations to behavior and computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical subnetworks with a common functional goal. Copyright © 2012 Elsevier Inc. All rights reserved.