研究人员提出前瞻性编码方法，解决了深度循环网络中底层信号延迟和顶层误差衰减问题，在多个模型上提升了性能。
Recursive Quadrature Filters (RQFs)是一种生物启发的复值时间滤波器，作为对角状态空间模型的特例。研究通过参数-free双抽头更新使每层的前馈输入具有前瞻性，在RQFs、S5和ORGaNICs模型中验证了该方法。在完整BPTT训练下，前瞻性变体在所有模型和配置中匹配或优于非前瞻性对照组。宽度为32的六层RQF模型在原始音频语音命令任务上达到96.09%准确率，仅使用31.9k参数。
Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks
Temporal integration gives continuous-time recurrent networks memory, but in deep stacks it also delays bottom-up signals and attenuates top-down errors. We develop Recursive Quadrature Filters (RQFs), biologically motivated complex-valued temporal filters that are a special case of diagonal state-space models (SSMs), and ask whether this failure mode can be addressed by making each layer's bottom-up input prospective. Starting from an energy model, we derive the RQF dynamics and show that each RQF is a band-pass filter whose learnable parameters control its tuning frequency and bandwidth. We then make each layer's bottom-up input prospective using a parameter-free two-tap update that leaves the recurrent transition and parallel scan unchanged. We extend this correction to general diagonal SSMs and show that it mitigates depth-dependent gradient attenuation when temporal gradients are truncated, i.e., spatial-only backpropagation. We evaluate the intervention in RQFs, S5, and ORGaNICs (a nonlinear gated RNN) trained using full backpropagation through time (BPTT) and spatial-only backpropagation. Under full BPTT, prospective variants match or outperform their non-prospective controls in every model and configuration. A non-residual width-32 six-layer RQF reaches 96.09% accuracy on raw-audio Speech Commands with 31.9k parameters; a width-64 six-layer RQF reaches 83.56% on the 16,384-step Path-X task. These results identify RQFs as a parameter-efficient recurrent substrate and prospective-input coding as an input-side correction for deep continuous-time recurrent networks.