Opiate receptor gradients in monkey cerebral cortex: correspondence with sensory processing hierarchies

M. Lewis; M Mishkin; E Bragin; R. Brown; C. Pert; A Pert

doi:10.1126/science.6258227

Opiate receptor gradients in monkey cerebral cortex: correspondence with sensory processing hierarchies

Science ◽

10.1126/science.6258227 ◽

1981 ◽

Vol 211 (4487) ◽

pp. 1166-1169 ◽

Cited By ~ 128

Author(s):

M. Lewis ◽

M Mishkin ◽

E Bragin ◽

R. Brown ◽

C. Pert ◽

...

Keyword(s):

Cerebral Cortex ◽

Sensory Processing ◽

Opiate Receptor

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Opiate receptor distribution in the cerebral cortex of the Rhesus monkey

Science ◽

10.1126/science.6289441 ◽

1982 ◽

Vol 218 (4570) ◽

pp. 387-389 ◽

Cited By ~ 68

Author(s):

S. Wise ◽

M Herkenham

Keyword(s):

Cerebral Cortex ◽

Rhesus Monkey ◽

Opiate Receptor ◽

Receptor Distribution

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

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2101043118 ◽

2021 ◽

Vol 118 (33) ◽

pp. e2101043118

Author(s):

Wolf Singer

Keyword(s):

Cerebral Cortex ◽

Sensory Processing ◽

Complex Dynamics ◽

Predictive Coding ◽

High Dimensional ◽

Learning Networks ◽

Feed Forward ◽

Weight Distributions ◽

The World ◽

Sensory Evidence

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.

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