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.

翻译：侧向预测编码（LPC）是一种用于理解生物神经回路特征检测的简单理论框架。近期理论工作[Huang等人，《物理评论E》112, 034304 (2025)]通过施加能量成本与信息鲁棒性之间的权衡，成功构建了能够提取非高斯隐藏输入特征的最优LPC网络，但由此产生的循环交互动力学系统在响应外部输入时可能非常缓慢。本文研究了响应时间的缩减问题。我们发现，LPC系统的特征响应时间可被最小化至接近下限值，且不会损害平均预测误差（能量成本）和信号传输的信息鲁棒性。我们进一步证明，采用模块化结构组织并大幅减少侧向交互数量的最优LPC网络，在特征检测性能、响应时间、能量成本和信息鲁棒性方面，与全连接网络同样卓越。