Response time of lateral predictive coding and benefits of modular structures

TL;DR

This paper explores how modular structures in lateral predictive coding networks can significantly reduce response times without sacrificing accuracy, robustness, or energetic efficiency.

Contribution

It demonstrates that modular LPC networks can match the performance of fully connected networks while achieving faster response times and reduced complexity.

Findings

Response time can be minimized near the theoretical lower bound.

Modular LPC networks perform as well as fully connected networks in feature detection.

Modular organization reduces lateral interactions without compromising performance.

Abstract

Lateral predictive coding (LPC) is a simple theoretical framework to appreciate feature detection in biological neural circuits. Recent theoretical work [Huang et al., Phys.Rev.E 112, 034304 (2025)] has successfully constructed optimal LPC networks capable of extracting non-Gaussian hidden input features by imposing the tradeoff between energetic cost and information robustness, but the resulting dynamical systems of recurrent interactions can be very slow in responding to external inputs. We investigate response-time reduction in the present paper. We find that the characteristic response time of the LPC system can be minimized to closely approaching the lower-bound value without compromising the mean predictive error (energetic cost) and the information robustness of signal transmission. We further demonstrate that optimal LPC networks taking a modular structural organization with extensively reduced number of lateral interactions are equally excellent as all-to-all completely connected networks, in terms of feature detection performance, response time, energetic cost and information robustness.