Our research group works on methods of transferring large amounts of information from observed data in neurobiological experiments and in geophysical observations to models of the systems originating the data.
We have now extended the statistical physics methods we use to the training and validation (generalization) procedures in AI Machine Learning problems.
In the neurobiological aspect of our work, we collaborate closely with the laboratories of Daniel Margoliash at the University of Chicago and Beth Stutzmann at the Rosalind Franklin University of Medicine and Science.
Each laboratory works with us to design experiments to estimate the electrophysiological properties of neurons, one in the birdsong system and the other in mammalian cortical regions.
When our collaborators complete the experiments, we work with them to use advanced methods of data assimilation (the process of transferring information in observed data to models of those systems) that we have developed employing innovative ideas from statistical physics.
These methods are applicable to many areas of complex systems science--for example, functional behavior of nervous systems--as in vocalization in song birds--and prediction in numerical weather models at the core of earth systems science.
Work in our laboratory is a mixture of developing and analyzing new algorithms in data assimilation and applying them in a systematic and efficient manner to biophysical and geophysical problems.
- E. O. Neftci, B. Toth, G. Indiveri, and H. D. I. Abarbanel, ``Dynamic State and Parameter Estimation Applied to Neuromorphic Systems,'' Neural Computation, 1669-1694 (2012)
- Whartenby, W., J. Quinn, and H. D. I. Abarbanel, ``The Number of Required Observations in Data Assimilation for a Shallow Water Flow,'' Monthly Weather Review 141, 2502-2518, (2013).
- Abarbanel, H. D. I., Predicting the Future: Completing Models of Observed Complex Systems, Springer-Verlag, June, 2013.
- Knowlton, C., C. D. Meliza, D. Margoliash, and H. D. I. Abarbanel, ``Dynamical Estimation of Neuron and Network Properties III: Network analysis using neuron spike times,''Biological Cybernetics 108 261-273 (2014).
- Rey, Daniel, Michael Eldridge, Mark Kostuk, Henry D. I. Abarbanel, Jan Schumann-Bischoff, Ulrich Parlitz, ``Accurate State and Parameter Estimation in Nonlinear Systems with Sparse Observations'', Physics Letters A 378, 869-873 (2014).
- Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D. and Abarbanel, H.D.I., ``Estimating parameters and predicting membrane voltages with conductance-based neuron models", Biological Cybernetics, 108, 495-516 (2014).
- Ye, Jingxin, Paul J. Rozdeba, Uriel I. Morone, Arij Daou, and Henry D. I. Abarbanel, ``Estimating the biophysical properties of neurons with intracellular calcium dynamics", Phys. Rev. E 89 , 062714 (2014).
- Rey, Daniel, M. Eldridge, U. Morone, Henry D. I. Abarbanel, U. Parlitz, and J. Schumann-Bischoff, ``Using Waveform Information in Nonlinear Data Assimilation", Physical Review E 90, 062916 (2014).
- Ye, Jingxin, N. Kadakia, P. J. Rozdeba, H. D. I. Abarbanel, and J. C. Quinn, ``Improved variational methods in statistical data assimilation", Nonlin. Processes Geophys. 22, 205-213 (2015).
- Nogaret, Alain, Erin L. O'Callaghan, Renata M. Lataro, Helio C. Salgado, C. Daniel Meliza, Edward Duncan, Henry D. I. Abarbanel, and Julian F. R. Paton, ``Silicon Central Pattern Generators for Cardiac Diseases", The Journal of Physiology Published online: 5 Jan. 2015, DOI: 10.1113/jphysiol.2014.282723, 593 763–774, 15 February 2015.
- Ye, Jingxin, Daniel Rey, Nirag Kadakia, M. Eldridge, U. Marone, P. Rozdeba, H. D. I. Abarbanel, J. C. Quinn, ``Systematic Nonlinear Statistical Variational Estimation of States and Parameters'', Phys. Rev. E 92, 052901 (2015). J. Schumann-Bischoff, U. Parlitz, Henry D. I. Abarbanel, Mark Kostuk, Daniel Rey, Michael Eldridge, and S. Luther, ``Basin structure of optimization based state and parameter estimation,'' Chaos 25, 053108 (2015); http://dx.doi.org/10.1063/1.4920942
- N. Kadakia, D. Rey, J. Ye and H. D. I. Abarbanel, ``Symplectic Methods in Statistical Data Assimilation'', Quarterly Journal of the Royal Meteorological Society, DOI:10.1002/qj.2962 (2017).
- Eve Armstrong and Henry D. I. Abarbanel, ``Model of the Songbird Nucleus HVC as a Network of Central Pattern Generators'', Journal of Neurophysiology, August 17, 2016; DOI: 10.1152/jn.00438.2016
- Zhe An, D. Rey, Jingxin Ye, and Henry D. I. Abarbanel, ``Estimating the State of a Geophysical System with Sparse Observations : Time-delay Methods to Achieve Accurate Initial States for Prediction'', Nonlinear Processes in Geophysics 23, 1-14 (2016). doi:10.5194/npg-23-1-2016.
- Nirag Kadakia, Eve Armstrong, Daniel Breen, Uriel Morone, Arij Daou, Daniel Margoliash, and Henry D.I. Abarbanel, ``Nonlinear Statistical Data Assimilation for HVCRA Neurons in the Avian Song System'', Biological Cybernetics, 29 September 2016 DOI: 10.1007/s00422-016-0697-3
- Henry D. I. Abarbanel, Eve Armstrong, Daniel Breen, Nirag Kadakia, Sasha Shirman, and Daniel Margoliash ``A Unifying View of Synchronization for Data Assimilation in Complex Nonlinear Networks'', Chaos 27, 126802, https://doi.org/10.1063/1.5001816 (2017).
- Daniel Breen, Abraham Akinin, Henry D.I. Abarbanel, and Gert Cauwenberghs, ``Data Assimilation of Membrane Dynamics and Channel Kinetics with a Neuromorphic Integrated Circuit,'' 12th IEEE BioMedical Circuits and Systems Conference, Oct. 17-19, 2016 Shanghai, China.
- Alain Nogaret, C. Daniel Meliza, Daniel Margoliash and Henry D. I. Abarbanel, ``Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data,'' Scientific Reports 6, Article number: 32749 doi:10.1038/srep32749 (2016); Published online: 08 September 2016
- Eve Armstrong, Amol V. Patwardhan, Lucas Johns, Chad T. Kishimoto, Henry D. I. Abarbanel, and George M. Fuller, ``An optimization-based approach to neutrino flavor evolution," Physical Review D 96, 083008 (2017).
- Jun Wang, Daniel Breen, Abraham Akinin, Fr\'ed\'eric Brocard, Henry D.I. Abarbanel, and Gert Cauwenberghs, ``Assimilation of Biophysical Neuronal Dynamics in Neuromorphic VLSI,'' IEEE Transactions on Biomedical Circuits and Systems, doi: 10.1109/TBCAS.2017.2776198, November, 2017.
- Henry D. I. Abarbanel, Paul J. Rozdeba, and Sasha Shirman, ``Machine Learning; Deepest Learning as Statistical Data Assimilation Problems'', accepted by Neural Computation, February, 2018.