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

• URL: http://arxiv.org/abs/2508.12702v1
• Date: Mon, 18 Aug 2025 08:00:24 GMT
• Title: A Unified Cortical Circuit Model with Divisive Normalization and Self-Excitation for Robust Representation and Memory Maintenance
• Authors: Jie Su, Weiwei Wang, Zhaotian Gu, Dahui Wang, Tianyi Qian,
• Abstract summary: We introduce a recurrent neural circuit that combines divisive normalization with self-excitation to achieve robust encoding.<n>We demonstrate the model's versatility in two canonical tasks.<n>This work establishes a unified mathematical framework that bridges noise suppression, working memory, and approximate Bayesian inference.
• Score: 2.705743343600661
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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