A Unified Cortical Circuit Model with Divisive Normalization and Self-Excitation for Robust Representation and Memory Maintenance

TL;DR

This paper presents a unified cortical circuit model combining divisive normalization and self-excitation, enabling robust information encoding and stable memory retention, with applications demonstrated in noise-robust perception and Bayesian inference tasks.

Contribution

It introduces a novel recurrent neural circuit model that unifies noise-resistant processing and memory maintenance within a single framework, bridging multiple cognitive functions.

Findings

The model forms a continuous attractor with input-proportional stabilization and persistent memory states.

It effectively performs noise-robust encoding in a random-dot kinematogram paradigm.

It approximates Bayesian belief updating in a probabilistic Wisconsin Card Sorting Test.

Abstract

Robust information representation and its persistent maintenance are fundamental for higher cognitive functions. Existing models employ distinct neural mechanisms to separately address noise-resistant processing or information maintenance, yet a unified framework integrating both operations remains elusive -- a critical gap in understanding cortical computation. Here, we introduce a recurrent neural circuit that combines divisive normalization with self-excitation to achieve both robust encoding and stable retention of normalized inputs. Mathematical analysis shows that, for suitable parameter regimes, the system forms a continuous attractor with two key properties: (1) input-proportional stabilization during stimulus presentation; and (2) self-sustained memory states persisting after stimulus offset. We demonstrate the model's versatility in two canonical tasks: (a) noise-robust encoding in a random-dot kinematogram (RDK) paradigm; and (b) approximate Bayesian belief updating in a probabilistic Wisconsin Card Sorting Test (pWCST). This work establishes a unified mathematical framework that bridges noise suppression, working memory, and approximate Bayesian inference within a single cortical microcircuit, offering fresh insights into the brain's canonical computation and guiding the design of biologically plausible artificial neural architectures.