Research

I develop and apply a bayesian statistical method formulated as non-convex optimization to estimate parameters in nonlinear dynamical systems. I am interested in finding patterns in estimated parameters of neuron models that would be of biophysical and medical interest, using tools such as clustering and classification algorithms. I have been at the core of two collaborations applying my research group's tools to different biomedical applications. In one case, understanding the causes of Alzheimer's disease. In another, to characterize the dynamic behavior of a neuromorphic VLSI circuit that emulates membrane dynamics and channel kinetics.

Publications

2016

  1. Breen, D., Shirman, S., Armstrong, E., Kadakia, N., & Abarbanel, H. (2016). HVC Interneuron Properties from Statistical Data Assimilation. ArXiv Preprint ArXiv:1608.04433. PDF
  2. Kadakia, N., Armstrong, E., Breen, D., Morone, U., Daou, A., Margoliash, D., & DI Abarbanel, H. (2016). Nonlinear Statistical Data Assimilation for HVC RA Neurons in the Avian Song System. Biological Cybernetics
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  4. Armstrong, E., Kadakia, N., Breen, D., & DI Abarbanel, H. State Variable and Parameter Estimations for Small Neural Network Models. In Submission.
  5. Wang, J., Breen, D., Akinin, A., Abarbanel, H. D., & Cauwenberghs, G. (2016, October). Data assimilation of membrane dynamics and channel kinetics with a neuromorphic integrated circuit. In Biomedical Circuits and Systems Conference (BioCAS), 2016 IEEE (pp. 584-587). IEEE.
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