Newest papers

Model- and Layer-Level Closed-Loop Forecasting with SSM-Based Spiking Neural Networks

Erik Stolt, Sanja Karilanova, Ayça Özçelikkale · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28495 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

Deep State-Space Competition Models (Deep SSMs) は、複雑な時間依存性を持つタイムシリーズデータの処理における有望なパフォーマンスと計算上の利点を示している。 Deep State-Space Competition Models (SNNs) は、深いSSMの生物学的にインスピレーションされたクラスであるが、コンピューティングブロックがスピークを通じてコミュニケーションし、単一ビットのデータ処理機能ではなく継続的なアクティベーション機能を有効化している。これらのタイムシリーズ処理における特に重要な課題は、過去の観測から単独で提案されたセクションの将来の値を予測することである。しかし、どのように深いSSMと予測を最適に実行するかはオープンな質問であり、SNN

Machine translation (Meta M2M100); the English original follows.

Abstract

Deep State-Space Models (Deep SSMs) have shown promising performance and computational benefits in processing of time-series data with complex temporal dependencies. Spiking neural networks (SNNs) constitute a biologically inspired class of deep SSMs, where computing blocks communicate through spikes, discrete single-bit events rather than continuous activation functions. A particularly important task in time-series processing is forecasting, i.e. predicting future values of a sequence from past observations alone. However, how to best perform forecasting with SNNs is an open question, where the standard autoregressive nature of the data processing with SNNs requires values that are not available during the forecasting phase. In this article, we address this challenge. We explore two closed-loop forecasting strategies, in which the network's own generated values are fed back into the network as if they were observed past values: (i) model-level and (ii) layer-level closed-loop forecasting. We evaluate these approaches on the ETT-small datasets, a standard benchmark for time-series forecasting, under both spike-based and continuous activation functions. We compare the proposed closed-loop approaches against state-of-the-art methods such as SpaceTime, as well as the zero-masking baseline in S4. Both of the proposed approaches perform comparably to or better than zero-masking with S4, and in most cases match SpaceTime. These findings highlight the trade-off between continuous and low-bit quantized activations, and demonstrate that the proposed Deep SSM architectures provide a competitive framework for forecasting.

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Topics: Computer science, Benchmark (surveying), Spiking neural network, Artificial intelligence, Artificial neural network, Deep learning

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