Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks

Shivang Rawat; Mirko Morello; Flaviano Morone; David J. Heeger

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arXiv · 2609.04134

Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks

Shivang Rawat·Mirko Morello·Flaviano Morone·David J. Heeger

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Abstract

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.

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Shivang Rawat, Mirko Morello, Flaviano Morone, David J. Heeger. 2026-09-03. Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks. https://arxiv.org/abs/2609.04134

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