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

2025-08-18

AI Summary

Existing cortical computational models treat noise robustness and information maintenance as separate processes, lacking a unified framework. This work proposes a continuous attractor network that integrates divisive normalization with self-excitatory recurrent connectivity, enabling—within a single circuit—robust neural coding, persistent working memory, and approximate Bayesian inference for the first time. Through dynamical systems modeling and stability analysis, the model provides a unified mechanistic account of cortical noise suppression, memory retention, and probabilistic reasoning. Evaluated on stochastic dot-motion perception and probabilistic card-sorting tasks, it demonstrates significantly enhanced noise robustness and memory persistence while implementing biologically plausible belief updating. This study bridges a critical gap in cortical computation by establishing a principled, unified framework that advances our understanding of the neural basis of higher cognition.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

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.

Problem

• Unified model for robust encoding and memory maintenance
• Combines divisive normalization with self-excitation mechanisms
• Bridges noise suppression, working memory, and Bayesian inference

Innovation

• Recurrent neural circuit with divisive normalization
• Self-excitation for robust encoding and retention
• Continuous attractor for noise suppression and memory