Response time of lateral predictive coding and benefits of modular structures
This work addresses a speed limitation in neural circuit models, which is incremental as it builds on prior theoretical frameworks.
The paper tackles the slow response time of optimal lateral predictive coding networks by minimizing it to near the theoretical lower bound without sacrificing predictive error or information robustness, and shows that modular networks with fewer lateral connections perform as well as fully connected ones.
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.