Clark, R., Fuller, L., Platt, J. A., & Abarbanel, H. D. I. (2022). Reduced-Dimension, Biophysical Neuron Models Constructed From Observed Data. Neural Computation, 34(7), 1545–1587. https://doi.org/10.1162/neco_a_01515

Penny, S. G., Smith, T. A., Chen, T. C., Platt, J. A., Lin, H. Y., Goodliff, M., & Abarbanel, H. D. I. (2022). Integrating recurrent neural networks with data assimilation for scalable data-driven state estimation. Journal of Advances in Modeling Earth Systems, 14(3), 25. https://doi.org/10.1029/2021ms002843

Platt, J. A., Penny, S. G., Smith, T. A., Chen, T.-C., & Abarbanel, H. D. I. (2022). A systematic exploration of reservoir computing for forecasting complex spatiotemporal dynamics. Neural Networks, 153, 530–552. https://doi.org/10.1016/j.neunet.2022.06.025

Platt, J. A., Wong, A. D., Clark, R., Penny, S. G., & Abarbanel, H. D. I. (2021). Robust forecasting using predictive generalized synchronization in reservoir computing. Chaos, 31(12), 16. https://doi.org/10.1063/5.0066013

Abarbanel, H. D. I. (2021). A personal retrospective on the 60th anniversary of the journal Biological Cybernetics. Biological Cybernetics, 2. https://doi.org/10.1007/s00422-021-00878-6

Ty, A. J. A., Fang, Z., Gonzalez, R. A., Rozdeba, P. J., & Abarbanel, H. D. I. (2019). Machine learning of time series using time-delay embedding and precision annealing. Neural Computation, 31(10), 2004–2024. https://doi.org/10.1162/neco_a_01224

Abarbanel, H. D. I., Rozdeba, P. J., & Shirman, S. (2018). Machine learning: Deepest learning as statistical data assimilation problems. Neural Computation, 30(8), 2025–2055. https://doi.org/10.1162/neco_a_01094

Abarbanel, H. D. I., Shirman, S., Breen, D., Kadakia, N., Rey, D., Armstrong, E., & Margoliash, D. (2017). A unifying view of synchronization for data assimilation in complex nonlinear networks. Chaos, 27(12). https://doi.org/10.1063/1.5001816

Armstrong, E., Patwardhan, A. V., Johns, L., Kishimoto, C. T., Abarbanel, H. D. I., & Fuller, G. M. (2017). An optimization-based approach to calculating neutrino flavor evolution. Physical Review D, 96(8). https://doi.org/10.1103/PhysRevD.96.083008

Kadakia, N., Rey, D., Ye, J., & Abarbanel, H. D. I. (2017). Symplectic Structure Of Statistical Variational Data Assimilation. Quarterly Journal of the Royal Meteorological Society, 143(703), 756–771. https://doi.org/10.1002/qj.2962

An, Z., Rey, D., Ye, J. X., & Abarbanel, H. D. I. (2017). Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction. Nonlinear Processes in Geophysics, 24(1), 9–22. https://doi.org/10.5194/npg-24-9-2017

Kadakia, N., Armstrong, E., Breen, D., Morone, U., Daou, A., Margoliash, D., & Abarbanel, H. D. I. (2016). Nonlinear statistical data assimilation for HVCRA neurons in the avian song system. Biological Cybernetics, 110(6), 417–434. https://doi.org/10.1007/s00422-016-0697-3

Armstrong, E., & Abarbanel, H. D. I. (2016). Model of the songbird nucleus HVC as a network of central pattern generators. Journal of Neurophysiology, 116(5), 2405–2419. https://doi.org/10.1152/jn.00438.2016

Nogaret, A., Meliza, C. D., Margoliash, D., & Abarbanel, H. D. I. (2016). Automatic construction of predictive neuron models through large scale assimilation of electrophysiological data. Scientific Reports, 6. https://doi.org/10.1038/srep32749

Ye, J. X., Rey, D., Kadakia, N., Eldridge, M., Morone, U. I., Rozdeba, P., Abarbanel, H. D. I., & Quinn, J. C. (2015). Systematic variational method for statistical nonlinear state and parameter estimation. Physical Review E, 92(5). https://doi.org/10.1103/PhysRevE.92.052901

Schumann-Bischoff, J., Parlitz, U., Abarbanel, H. D. I., Kostuk, M., Rey, D., Eldridge, M., & Luther, S. (2015). Basin structure of optimization based state and parameter estimation. Chaos, 25(5). https://doi.org/10.1063/1.4920942

Nogaret, A., O’Callaghan, E. L., Lataro, R. M., Salgado, H. C., Meliza, C. D., Duncan, E., Abarbanel, H. D. I., & Paton, J. F. R. (2015). Silicon central pattern generators for cardiac diseases. Journal of Physiology-London, 593(4), 763–774. https://doi.org/10.1113/jphysiol.2014.282723

Ye, J., Kadakia, N., Rozdeba, P. J., Abarbanel, H. D. I., & Quinn, J. C. (2015). Improved variational methods in statistical data assimilation. Nonlinear Processes in Geophysics, 22(2), 205–213. https://doi.org/10.5194/npg-22-205-2015

Rey, D., Eldridge, M., Morone, U., Abarbanel, H. D. I., Parlitz, U., & Schumann-Bischoff, J. (2014). Using waveform information in nonlinear data assimilation. Physical Review E, 90(6). https://doi.org/10.1103/PhysRevE.90.062916

Meliza, C. D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., & Abarbanel, H. D. I. (2014). Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biological Cybernetics, 108(4), 495–516. https://doi.org/10.1007/s00422-014-0615-5

Ye, J. X., Rozdeba, P. J., Morone, U. I., Daou, A., & Abarbanel, H. D. I. (2014). Estimating the biophysical properties of neurons with intracellular calcium dynamics. Physical Review E, 89(6). https://doi.org/10.1103/PhysRevE.89.062714

Knowlton, C., Meliza, C. D., Margoliash, D., & Abarbanel, H. D. I. (2014). Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics, 108(3), 261–273. https://doi.org/10.1007/s00422-014-0601-y

Rey, D., Eldridge, M., Kostuk, M., Abarbanel, H. D. I., Schumann-Bischoff, J., & Parlitz, U. (2014). Accurate state and parameter estimation in nonlinear systems with sparse observations. Physics Letters A, 378(11–12), 869–873. https://doi.org/10.1016/j.physleta.2014.01.027

Whartenby, W. G., Quinn, J. C., & Abarbanel, H. D. I. (2013). The number of required observations in data assimilation for a shallow-water flow. Monthly Weather Review, 141(7), 2502–2518. https://doi.org/10.1175/mwr-d-12-00103.1

Neftci, E. O., Toth, B., Indiveri, G., & Abarbanel, H. D. I. (2012). Dynamic State and Parameter Estimation Applied to Neuromorphic Systems. Neural Computation, 24(7), 1669–1694.

Kostuk, M., Toth, B. A., Meliza, C. D., Margoliash, D., & Abarbanel, H. D. I. (2012). Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics, 106(3), 155–167. https://doi.org/10.1007/s00422-012-0487-5

Toth, B. A., Kostuk, M., Meliza, C. D., Margoliash, D., & Abarbanel, H. D. I. (2011). Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics, 105(3–4), 217–237. https://doi.org/10.1007/s00422-011-0459-1

Quinn, J. C., & Abarbanel, H. D. I. (2011). Data assimilation using a GPU accelerated path integral Monte Carlo approach. Journal of Computational Physics, 230(22), 8168–8178. https://doi.org/10.1016/j.jcp.2011.07.015

Haas, J. S., Kreuz, T., Torcini, A., Politi, A., & Abarbanel, H. D. I. (2010). Rate maintenance and resonance in the entorhinal cortex. European Journal of Neuroscience, 32(11), 1930–1939. https://doi.org/10.1111/j.1460-9568.2010.07455.x

Quinn, J. C., & Abarbanel, H. D. I. (2010). State and parameter estimation using Monte Carlo evaluation of path integrals. Quarterly Journal of the Royal Meteorological Society, 136(652), 1855–1867. https://doi.org/10.1002/qj.690

Abarbanel, H. D. I., Kostuk, M., & Whartenby, W. (2010). Data assimilation with regularized nonlinear instabilities. Quarterly Journal of the Royal Meteorological Society, 136(648), 769–783. https://doi.org/10.1002/qj.600

Kreuz, T., Chicharro, D., Andrzejak, R. G., Haas, J. S., & Abarbanel, H. D. I. (2009). Measuring multiple spike train synchrony. Journal of Neuroscience Methods, 183(2), 287–299. https://doi.org/10.1016/j.jneumeth.2009.06.039

Abarbanel, H. D. I. (2009). Effective actions for statistical data assimilation. Physics Letters A, 373(44), 4044–4048. https://doi.org/10.1016/j.physleta.2009.08.072

Gibb, L., Gentner, T. Q., & Abarbanel, H. D. I. (2009). Inhibition and recurrent excitation in a computational model of sparse bursting in song nucleus HVC. Journal of Neurophysiology, 102(3), 1748–1762. https://doi.org/10.1152/jn.00670.2007

Gibb, L., Gentner, T. Q., & Abarbanel, H. D. I. (2009). Brain stem feedback in a computational model of birdsong sequencing. Journal of Neurophysiology, 102(3), 1763–1778. https://doi.org/10.1152/jn.91154.2008

Quinn, J. C., Bryant, P. H., Creveling, D. R., Klein, S. R., & Abarbanel, H. D. I. (2009). Parameter and state estimation of experimental chaotic systems using synchronization. Physical Review E, 80(1). https://doi.org/10.1103/PhysRevE.80.016201

Muezzinoglu, M. K., Huerta, R., Abarbanel, H. D. I., Ryan, M. A., & Rabinovich, M. I. (2009). Chemosensor-driven artificial antennal lobe transient dynamics enable fast recognition and working memory. Neural Computation, 21(4), 1018–1037. https://doi.org/10.1162/neco.2008.05-08-780

Muezzinoglu, M. K., Vergara, A., Huerta, R., Rulkov, N., Rabinovich, M. I., Selverston, A., & Abarbanel, H. D. I. (2009). Acceleration of chemo-sensory information processing using transient features. Sensors and Actuators B-Chemical, 137(2), 507–512. https://doi.org/10.1016/j.snb.2008.10.065

Abarbanel, H. D. I., Creveling, D. R., Farsian, R., & Kostuk, M. (2009). Dynamical state and parameter estimation. SIAM Journal on Applied Dynamical Systems, 8(4), 1341–1381. https://doi.org/10.1137/090749761

Talathi, S. S., Abarbanel, H. D. I., & Ditto, W. L. (2008). Temporal spike pattern learning. Physical Review E, 78(3). https://doi.org/10.1103/PhysRevE.78.031918

Creveling, D. R., Gill, P. E., & Abarbanel, H. D. I. (2008). State and parameter estimation in nonlinear systems as an optimal tracking problem. Physics Letters A, 372(15), 2640–2644. https://doi.org/10.1016/j.physleta.2007.12.051

Creveling, D. R., Jeanne, J. M., & Abarbanel, H. D. I. (2008). Parameter estimation using balanced synchronization. Physics Letters A, 372(12), 2043–2047. https://doi.org/10.1016/j.physleta.2007.10.097

Abarbanel, H. D. I., Creveling, D. R., & Jeanne, J. M. (2008). Estimation of parameters in nonlinear systems using balanced synchronization. Physical Review E, 77(1). https://doi.org/10.1103/PhysRevE.77.016208

Kreuz, T., Haas, J. S., Morelli, A., Abarbanel, H. D. I., & Politi, A. (2007). Measuring spike train synchrony. Journal of Neuroscience Methods, 165(1), 151–161. https://doi.org/10.1016/j.jneumeth.2007.05.031

Shlens, J., Kennel, M. B., Abarbanel, H. D. I., & Chichilnisky, E. J. (2007). Estimating information rates with confidence intervals in neural spike trains. Neural Computation, 19(7), 1683–1719. https://doi.org/10.1162/neco.2007.19.7.1683

Borst, A., & Abarbanel, H. D. I. (2007). Relating a calcium indicator signal to the unperturbed calcium concentration time-course. Theoretical Biology and Medical Modelling, 4. https://doi.org/10.1186/1742-4682-4-7

Rabinovich, M. I., Varona, P., Selverston, A. I., & Abarbanel, H. D. I. (2006). Dynamical principles in neuroscience. Reviews of Modern Physics, 78(4), 1213–1265. https://doi.org/10.1103/RevModPhys.78.1213

Haas, J. S., Nowotny, T., & Abarbanel, H. D. I. (2006). Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. Journal of Neurophysiology, 96(6), 3305–3313. https://doi.org/10.1152/jn.00551.2006

Abarbanel, H. D. I., & Talathi, S. S. (2006). Neural circuitry for recognizing interspike interval sequences. Physical Review Letters, 96(14). https://doi.org/10.1103/PhysRevLett.96.148104

Briggman, K. L., Abarbanel, H. D. I., & Kristan, W. B. (2006). From crawling to cognition: analyzing the dynamical interactions among populations of neurons. Current Opinion in Neurobiology, 16(2), 135–144. https://doi.org/10.1016/j.conb.2006.03.014