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Publications of V. Centorrino
Thesis
  1. V. Centorrino. Contracting Dynamics for Biologically Plausible Neural Networks and Optimization. PhD thesis, Modeling and Engineering Risk and Complexity, Scuola Superiore Meridionale, December 2024. Keyword(s): Contraction Theory, Network Systems, Machine Learning, Neural Networks, Theoretical Neuroscience. [bibtex-entry]


Articles in journal, book chapters
  1. V. Centorrino, F. Bullo, and G. Russo. Similarity Matching Networks: Hebbian Learning and Convergence over Multiple Time Scales. Neural Computation, 38:725-764, 2026. Keyword(s): Neural Networks, Theoretical Neuroscience. [bibtex-entry]


  2. V. Centorrino, F. Rossi, F. Bullo, and G. Russo. Proximal Gradient Dynamics and Feedback Control for Equality-Constrained Composite Optimization. European Journal of Control, 2026. Note: To appear. [bibtex-entry]


  3. A. Davydov, V. Centorrino, A. Gokhale, G. Russo, and F. Bullo. Time-Varying Convex Optimization: A Contraction and Equilibrium Tracking Approach. IEEE Transactions on Automatic Control, 70(11):7446-7460, 2025. Keyword(s): Contraction Theory. [bibtex-entry]


  4. F. Rossi, V. Centorrino, F. Bullo, and G. Russo. Neural Policy Composition from Free Energy Minimization. Technical Report, 2025. Note: ArXiv:2512.04745. Keyword(s): Neural Networks. [bibtex-entry]


  5. V. Centorrino, F. Bullo, and G. Russo. Modelling and Contractivity of Neural-Synaptic Networks with Hebbian Learning. Automatica, 164:111636, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  6. V. Centorrino, A. Davydov, A. Gokhale, G. Russo, and F. Bullo. On Weakly Contracting Dynamics for Convex Optimization. IEEE Control Systems Letters, 8:1745-1750, 2024. Keyword(s): Contraction Theory. [bibtex-entry]


  7. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Positive Competitive Networks for Sparse Reconstruction. Neural Computation, 36(6):1163–1197, 2024. Keyword(s): Contraction Theory, Neural Networks, Theoretical Neuroscience. [bibtex-entry]


  8. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Euclidean Contractivity of Neural Networks with Symmetric Weights. IEEE Control Systems Letters, 7:1724-1729, 2023. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


Conference articles
  1. V. Centorrino, F. Rossi, F. Bullo, and G. Russo. Proximal Gradient Dynamics and Feedback Control for Equality-Constrained Composite Optimization. In European Control Conference, Reykjavík, Iceland, July 2026. Note: To appear. [bibtex-entry]


  2. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Biologically Plausible Neural Networks for Sparse Reconstruction: A Normative Framework. In Workshop “Mathematics for Artificial Intelligence and Machine Learning”, Milan, Italy, january 2024. Note: Oral Presentation. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  3. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Towards a Top/Down Normative Framework for a Biologically Plausible Explanation of Neural Circuits: Application to Sparse Reconstruction Problems. In 5th International Convention on the Mathematics of Neuroscience and AI, Rome, Italy, May 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  4. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Contractivity of Symmetric Neural Networks for Non-negative Sparse Approximation. In CCS/Italy 2023 Italian Regional Conference on Complex Systems, Naples, Italy, october 2023. Note: Poster Presentation. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]


  5. V. Centorrino, F. Bullo, and G. Russo. Contraction Analysis of Hopfield Neural Networks with Hebbian Learning. In IEEE Conf. on Decision and Control, Cancun, Mexico, December 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]



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