Active filtering: a predictive function of recurrent circuits of sensory cortex

TL;DR

This paper reviews evidence that recurrent circuits in sensory cortex serve as a substrate for sensory memories through active filtering, which involves predictive processing to selectively amplify relevant sensory inputs.

Contribution

It introduces the concept that cortical recurrent connectivity functions as an active filtering mechanism for sensory memories, highlighting its role in predictive processing.

Findings

Recurrent connectivity encodes natural sensory input structures

Active filtering transforms network inputs to enhance relevant signals

Recurrent networks perform predictive processing for sensory memory

Abstract

Our brains encode many features of the sensory world into memories: we can sing along with songs we have heard before, interpret spoken and written language composed of words we have learned, and recognize faces and objects. Where are these memories stored? Each area of the cerebral cortex has a huge number of local, recurrent, excitatory-excitatory synapses, as many as 500 million per cubic millimeter. Here I review evidence that cortical recurrent connectivity in sensory cortex is a substrate for sensory memories. Evidence suggests that the local recurrent network encodes the structure of natural sensory input, and that it does so via active filtering, transforming network inputs to boost or select those associated with natural sensation. This is a form of predictive processing, in which the cortical recurrent network selectively amplifies some input patterns and attenuates others, and a form of memory.