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Trained recurrent neural networks develop phase-locked limit cycles in a working memory task., , and . PLoS Comput. Biol., (February 2024)Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference., , , , , , , and . CoRR, (2022)Flow Matching for Scalable Simulation-Based Inference., , , , , and . CoRR, (2023)Variational methods for simulation-based inference., , and . ICLR, OpenReview.net, (2022)Empirical models of spiking in neural populations., , , , , and . NIPS, page 1350-1358. (2011)Intrinsic dimension of data representations in deep neural networks., , , and . NeurIPS, page 6109-6119. (2019)Statistical Analysis of Multi-Cell Recordings: Linking Population Coding Models to Experimental Data., , and . Frontiers Comput. Neurosci., (2011)Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations., , and . Frontiers Comput. Neurosci., (2010)Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations., , , , , , and . CoRR, (2024)sbi: A toolkit for simulation-based inference., , , , , , , and . J. Open Source Softw., 5 (52): 2505 (2020)