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Publications about 'Neural Networks'

Books and proceedings

  1. F. Bullo. Lectures on Neural Dynamics. Unpublished Manuscript, 2025. PDF Keyword(s): Neural Networks, Theoretical Neuroscience. [bibtex-entry]

Thesis

  1. S. Betteti. Associative Memory and Recurrent Neural Networks: A Dynamical Systems Approach. PhD thesis, Information Engineering Department, Universitŕ di Padova, Italy, February 2026. PDF Keyword(s): Contraction Theory, Network Systems, Machine Learning, Neural Networks, Theoretical Neuroscience. [bibtex-entry]
  2. A. Davydov. Contraction Theory in Control, Learning, and Optimization. PhD thesis, Mechanical Engineering Department, University of California at Santa Barbara, June 2025. PDF Keyword(s): Contraction Theory, Network Systems, Machine Learning, Neural Networks. [bibtex-entry]
  3. V. Centorrino. Contracting Dynamics for Biologically Plausible Neural Networks and Optimization. PhD thesis, Modeling and Engineering Risk and Complexity, Scuola Superiore Meridionale, December 2024. PDF Keyword(s): Contraction Theory, Network Systems, Machine Learning, Neural Networks, Theoretical Neuroscience. [bibtex-entry]
  4. S. Jaffe. Dynamical Systems and Neural Networks: From Implicit Models to Memory Retrieval. PhD thesis, Computer Science Department, University of California at Santa Barbara, September 2024. PDF Keyword(s): Contraction Theory, Network Systems, Machine Learning. [bibtex-entry]

Articles in journal or book chapters

  1. F. Bullo. Predictive Coding with Bayesian Priors via Proximal Gradients. Technical Report, 2026. Note: ArXiv:2606.08374. Keyword(s): Neural Networks. [bibtex-entry]
  2. 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]
  3. A. Gokhale, A. V. Proskurnikov, Y. Kawano, and F. Bullo. A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning. IEEE Transactions on Automatic Control, April 2026. Note: Submitted. [bibtex-entry]
  4. S. Betteti, G. Baggio, F. Bullo, and S. Zampieri. Firing Rate Models as Associative Memory: Synaptic design for robust retrieval. Neural Computation, 37(10):1807--1838, 2025. Keyword(s): Neural Networks, Theoretical Neuroscience. [bibtex-entry]
  5. S. Betteti, G. Baggio, F. Bullo, and S. Zampieri. Input-Driven Dynamics for Robust Memory Retrieval in Hopfield Networks. Science Advances, 11(17), 2025. Keyword(s): Neural Networks, Theoretical Neuroscience. [bibtex-entry]
  6. S. Betteti, W. Retnaraj, A. Davydov, J. Cortes, and F. Bullo. Competition, Stability, and Functionality in Excitatory-Inhibitory Neural Circuits. Technical Report, 2025. Note: ArXiv:2512.05252. Keyword(s): Neural Networks. [bibtex-entry]
  7. A. Davydov, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Contraction Analysis of Continuous-Time Neural Networks. IEEE Transactions on Automatic Control, 70(1):235-250, 2025. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  8. 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]
  9. 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]
  10. 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]
  11. A. Davydov and F. Bullo. Perspectives on Contractivity in Control, Optimization and Learning. IEEE Control Systems Letters, 8:2087-2098, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  12. A. Davydov, S. Jafarpour, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Monotone Operator Theory and Applications. Journal of Machine Learning Research, 25(307):1-33, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  13. A. Gokhale, A. Davydov, and F. Bullo. Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications. IEEE Control Systems Letters, 8:2853-2858, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  14. 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]
  15. S. Jafarpour, A. Davydov, and F. Bullo. Non-Euclidean Contraction Theory for Monotone and Positive Systems. IEEE Transactions on Automatic Control, 68(9):5653-5660, 2023. PDF Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]

Conference articles

  1. A. Gokhale, A. V. Proskurnikov, Y. Kawano, and F. Bullo. Contracting Neural Networks: Sharp LMI Conditions with Applications to Integral Control and Deep Learning. In IEEE Conf. on Decision and Control, Honolulu, Hawaii, 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. S. Jaffe, A. K. Singh, and F. Bullo. IDKM: Memory Efficient Neural Network Quantization via Implicit Differentiable $k$-Means. In ICLR Workshop on Sparsity in Neural Networks, May 2023. Keyword(s): Neural Networks. [bibtex-entry]
  6. F. Seccamonte, A. K. Singh, and F. Bullo. Inference of Infrastructure Network Flows via Physics-Inspired Implicit Neural Networks. In IEEE Conf. on Control Technology and Applications, Bridgetown, Barbados, August 2023. Keyword(s): Neural Networks. [bibtex-entry]
  7. 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. PDF Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  8. A. Davydov, S. Jafarpour, M. Abate, F. Bullo, and S. Coogan. Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks. In IEEE Conf. on Decision and Control, Cancun, Mexico, 2022. PDF Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  9. A. Davydov, S. Jafarpour, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Monotone Operator Theory with Applications to Recurrent Neural Networks. In IEEE Conf. on Decision and Control, Cancun, Mexico, December 2022. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  10. A. Davydov, A. V. Proskurnikov, and F. Bullo. Non-Euclidean Contractivity of Recurrent Neural Networks. In American Control Conference, Atlanta, USA, pages 1527-1534, May 2022. PDF Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  11. S. Jafarpour, M. Abate, A. Davydov, F. Bullo, and S. Coogan. Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach. In Learning for Dynamics and Control Conference, June 2022. PDF Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]
  12. S. Jafarpour, A. Davydov, M. Abate, F. Bullo, and S. Coogan. Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach. In ICML Workshop on Formal Verification of Machine Learning, July 2022. Keyword(s): Contraction Theory. [bibtex-entry]
  13. S. Jafarpour, A. Davydov, A. V. Proskurnikov, and F. Bullo. Robust Implicit Networks via Non-Euclidean Contractions. In Advances in Neural Information Processing Systems, volume 34, pages 9857--9868, December 2021. PDF Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]

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