20.500.12556/DiRROS-30786 Bio-inspired spiking recurrent networks with evolutionary optimization for non-stationary cryptocurrency forecasting Forecasting cryptocurrency prices remains difficult because market dynamics are highly volatile, non-stationary, and regime-dependent. This study investigates whether combining a spiking-inspired recurrent architecture with the Grey Wolf Optimizer (GWO) can improve one-step-ahead Bitcoin forecasting within a controlled model family. We compare four configurations, LSTM, SLSTM, GWO-LSTM, and GWO-SLSTM, on 4039 daily BTC–USD closing prices from 17 September 2014 to 9 October 2025 using Min–Max normalization, strict chronological splitting, windowed regime-based robustness analysis across three distinct market regimes, and repeated-run testing. The proposed SLSTM replaces the conventional hidden-state recurrence with leaky integrate-and-fire-inspired synaptic, membrane, and adaptive-threshold dynamics, functioning as a spiking-inspired recurrent model with thresholded event gating (reset = `none’, learnable threshold). On the primary hold-out split, GWO-SLSTM achieved a test RMSE of 1840.97 and a test MAPE of 1.76%, compared with 2217.24 and 2.46% for GWO-LSTM, 3501.48 and 3.86% for SLSTM, and 4030.10 and 4.40% for LSTM. Both GWO-optimized models exhibited substantial improvements over their non-optimized counterparts, while the SLSTM baseline also outperformed the plain LSTM, indicating gains from both spiking-inspired recurrence and evolutionary hyperparameter optimization. Both optimized models exhibited near-zero bias (PBIAS 0.11% for GWO-LSTM and 0.36% for GWO-SLSTM). Within the present implementation, GWO-SLSTM also trained faster than GWO-LSTM (39.71 s vs. 137.28 s), although this runtime difference should be interpreted as setup-specific because the model families were implemented in different frameworks and stopped after different numbers of epochs. Overall, within the expanded univariate BTC–USD setting, the results support GWO-SLSTM as a strong within-family candidate for one-step-ahead forecasting under non-stationary conditions. cryptocurrency bitcoin time-series forecasting kriptovalute napovedovanje vrednosti bitcoin evolucijski algoritmi true false true MDPI Švica Angleški jezik Slovenski jezik © 2026 by the authors. Neznano 2026-07-03 11:30:08 2026-07-03 11:30:09 2026-07-04 03:44:32 0000-00-00 00:00:00 2026 0 0 Nasl. z nasl. zaslona; Soavtorja iz Slovenije: Matej Mertik, Matjaž Gams; Opis vira z dne 2. 7. 2026; str. 1-32 iss. 7, [article no.] 200 Vol. 10 Jul. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2026-03-18 2026-06-03 2026-06-23 004.8 2504-2289 10.3390/bdcc10070200 283359491 https://www.mdpi.com/2504-2289/10/7/200 1 ccb46c5e-76c1-11f1-852a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=47062 RAZ_Walugembe_Francis_Noah_2026.pdf RAZ_Walugembe_Francis_Noah_2026.pdf 1 6AAA782851404C138BD4DB669D9DAEA5 c787a658400001f2048ed57cbe6760aae77789a6517ffdf969fd3280fd142f1e 18bae0a2-76c2-11f1-852a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=47063 Institut Jožef Stefan 0 0 0