What question did this study set out to answer?

To resolve unstable state evolution and information decay in spiking neural P-system recurrent architectures by designing a compensation-driven state regulation mechanism.

August 16, 2026International Journal of Neural Systems

Compensation-Balanced Recurrent Neural Architecture Based on Nonlinear Spiking Neural Membrane Systems

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Authors

JFJun Fu HPHong Peng BLBing Li

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Overview

Computational study demonstrates improved state stability and time series forecasting in spiking neural architectures, indicating robust long-term temporal modeling.

Key Points

To resolve unstable state evolution and information decay in spiking neural P-system recurrent architectures by designing a compensation-driven state regulation mechanism.
Developed the Compensation-Balanced LSTM-SNP (CBLSTM-SNP) architecture featuring a regulation branch and an adaptive compensation branch for state restoration.
Conducted mathematical dynamical analyses to prove system boundedness and Lipschitz continuity.
Evaluated empirical forecasting performance and hidden state trajectory stability across five benchmark time series datasets.
Mathematical analysis confirmed recurrent state transitions maintain boundedness and Lipschitz continuity during propagation.
CBLSTM-SNP generated smoother hidden state trajectories and demonstrated competitive forecasting performance across five benchmark time series datasets relative to standard recurrent and hybrid models.

Cite This Study

Fu et al. (2026) studied this question.

synapsesocial.com/papers/6a8179abf2fb91fc834acf03 https://doi.org/10.1142/s0129065727500213

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August 16, 2026International Journal of Neural Systems

Compensation-Balanced Recurrent Neural Architecture Based on Nonlinear Spiking Neural Membrane Systems

View Full Paper

Ask AI

Bookmark

Authors

JFJun Fu HPHong Peng BLBing Li

Discussion

Member takes

Overview

Computational study demonstrates improved state stability and time series forecasting in spiking neural architectures, indicating robust long-term temporal modeling.

Key Points

To resolve unstable state evolution and information decay in spiking neural P-system recurrent architectures by designing a compensation-driven state regulation mechanism.
Developed the Compensation-Balanced LSTM-SNP (CBLSTM-SNP) architecture featuring a regulation branch and an adaptive compensation branch for state restoration.
Conducted mathematical dynamical analyses to prove system boundedness and Lipschitz continuity.
Evaluated empirical forecasting performance and hidden state trajectory stability across five benchmark time series datasets.
Mathematical analysis confirmed recurrent state transitions maintain boundedness and Lipschitz continuity during propagation.
CBLSTM-SNP generated smoother hidden state trajectories and demonstrated competitive forecasting performance across five benchmark time series datasets relative to standard recurrent and hybrid models.

Cite This Study

Fu et al. (2026) studied this question.

synapsesocial.com/papers/6a8179abf2fb91fc834acf03 https://doi.org/10.1142/s0129065727500213

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