Recurrent dynamics in the cerebral cortex: Integration of sensory evidence with stored knowledge

Significance

This review attempts to unite three hitherto rather unconnected concepts of basic functions of the cerebral cortex, taking the visual system as an example: 1) feed-forward processing in multilayer hierarchies (labeled line coding), 2) dynamic association of features (assembly coding), and 3) matching of sensory evidence with stored priors (predictive coding). The latter two functions are supposed to rely on the high-dimensional dynamics of delay-coupled recurrent networks. Discharge rates of neurons (rate code) and temporal relations among discharges (temporal code) are identified as conveying complementary information. Thus, the new concept accounts for the coexistence of feed-forward and recurrent processing, accommodates both rate and temporal codes, and assigns crucial functions to the complex dynamics emerging from recurrent interactions.

Abstract

Current concepts of sensory processing in the cerebral cortex emphasize serial extraction and recombination of features in hierarchically structured feed-forward networks in order to capture the relations among the components of perceptual objects. These concepts are implemented in convolutional deep learning networks and have been validated by the astounding similarities between the functional properties of artificial systems and their natural counterparts. However, cortical architectures also display an abundance of recurrent coupling within and between the layers of the processing hierarchy. This massive recurrence gives rise to highly complex dynamics whose putative function is poorly understood. Here a concept is proposed that assigns specific functions to the dynamics of cortical networks and combines, in a unifying approach, the respective advantages of recurrent and feed-forward processing. It is proposed that the priors about regularities of the world are stored in the weight distributions of feed-forward and recurrent connections and that the high-dimensional, dynamic space provided by recurrent interactions is exploited for computations. These comprise the ultrafast matching of sensory evidence with the priors covertly represented in the correlation structure of spontaneous activity and the context-dependent grouping of feature constellations characterizing natural objects. The concept posits that information is encoded not only in the discharge frequency of neurons but also in the precise timing relations among the discharges. Results of experiments designed to test the predictions derived from this concept support the hypothesis that cerebral cortex exploits the high-dimensional recurrent dynamics for computations serving predictive coding.