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    Eve Armstrong

    TitlePostdoctoral Scholar
    SchoolUniversity of California, San Diego
    DepartmentBiocircuits Institute
    Address9500 Gilman Drive #0374
    CA La Jolla 92093
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      Columbia UniversityB.A.2002Astrophysics
      UC San DiegoPhD2013Physics

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      I am a postdoctoral scholar in the theoretical physics group of Henry Abarbanel at the BioCircuits Institute, UC San Diego. My work is motivated by a quest for fundamental organizing and/or dynamical principles governing the central nervous system. While I find this quest to be intrinsically interesting, the identification of such principles is also likely to offer insight into how to build machines - particularly for clinical purposes - that maximize efficiency. Of particular interest to me are: 1) pattern-generating networks that can be associated with a reliable macroscopic behavior, and 2) the interrelated processes of audition and vocalization for communication. Importantly, I also develop computational methods to test network models.

      My research is comprised of two complementary endeavours. First, I create small-scale computational models of nuclei and connectivity in the zebra finch brain that are associated with the generation of this species' highly stereotyped song "motif". Second, I develop methods to ascertain which experimental measurements are required to test these models. The technique we employ is a method of statistical data assimilation (D.A.). Via D.A., one may use information contained in experimental measurements to complete models of the systems from which the measurements came, where the models contain unknown parameters to be estimated. Both modeling and model-testing rely on a close collaboration with the laboratory of Daniel Margoliash at the University of Chicago. This group performs electrophysiological studies and whole-cell recordings of neurons in the zebra finch HVC (e.g. Daou et al. 2013). In addition, I have initiated a collaboration with the laboratory of Kevin Bender at UC San Francisco, to examine the potential of calcium imaging to infer network properties.

      The ability of D.A. to estimate properties of individual neurons has relevance to clinical neuroscience, for example, in the event that one seeks to identify a systematic difference between healthy and diseased cells, in order to develop an effective targeted therapy.


      I have created a small-scale model of the avian nucleus HVC (Armstrong & Abarbanel 2016), which contains a functional architectural element that can assume multiple modes of activity, depending on parameter values. The neurons are Hodgkin-Huxley-type, and the synapses deliver chemically-driven pulses. Many observations of HVC activity can be reproduced by the model dynamics. In particular, the model qualitatively reproduces population activity during singing and non-singing periods, where the former state is based on winnerless competition (WLC) Lotka-Volterra dynamics. I consider the model's main strength to be its demonstration of the versatility and robustness of a highly inter-connected web-like structure. In addition, the model had the un-intended consequence of explaining anomalous activity captured by Hahnloser et al. (2002) that had not previously been interpreted.

      Currently I am expanding the model beyond HVC. The aim now is to devise a connectivity among various CNS and motor structures that together are able to produce a "toy" motif. Specifics are as follows. Zebra finch song has been synthesized, via models of syringeal and respiratory function, where time series of air sac pressure and syringeal muscle activity from singing birds is used to complete the models that generate these synthetic songs. It has been shown that the response in HVC to these synthetic songs is similar to the response of HVC to playback of the bird's own song (Amador et al. 2013). My aim is to create a simple motor pathway that 1) reproduces fundamental aspects of the air sac pressure and EMG output from which the synthetic songs are derived, 2) is consistent with known experimental findings on connectivity, timing studies, and population electrical activity in the relevant CNS and motor structures, and 3) makes testable predictions regarding the relative contributions of various areas to the motor output.

      A long-term venture is to examine the role of auditory feedback in song generation. In addition, I plan to scale the model in terms of neuron number so that connectivities may be drawn from probability distributions, and to examine how network dynamics may vary with scale.


      Since joining Henry Abarbanel's group, I have come to appreciate the potential of D.A. to tackle fundamental questions in neuroscience (and in any scientific field). A critical asset of D.A. is that it permits one to ask: "Which quantities of a system must be measured experimentally to furnish sufficient information to complete the corresponding model?", where the test of success is the power of the completed model to predict the state of the system outside the times at which the measurements were obtained. For this reason, D.A. can provide a guiding force for experimental design.

      We perform D.A. via the minimization of an objective function that is constrained by partial differential equations that define a model. Each equation corresponds to the time evolution of one model state variable. In my work thus far, I write an objective function that can be derived via a path-integral formulation (Abarbanel 2013), and which we consider to represent the physical action on a system's path through its state space.

      I have been employing this formulation to the pattern-generating six-neuron architecture described in Armstrong & Abarbanel 2016, to simultaneously estimate: 1) individual cellular properties and 2) the strengths of synaptic connections among the cells. Results might address the following question: "Does there exist an underlying relation between the electrophysiological properties of individual cells in a network and the specific connectivity of those cells?"

      The current goal of my D.A. experiments is to ascertain precisely which *simulated* measurements are required to yield accurate estimates of the above-mentioned neuronal and network properties. The measurements I am working with are: time courses of membrane voltage and - independently - intracellular calcium concentration. The former measurement may become feasible for large numbers of neurons as voltage-sensitive dying techniques are developed. Regarding the latter measurement, I have recently begun a collaboration with a lab at UC San Francisco, to develop a transfer function between dendritic calcium concentration and an associated optical signal.

      Finally, I have been collaborating with an astrophysics group at UCSD, applying D.A. to a problem in neutrino evolution (Armstrong et al. 2016). In astrophysics, the available measurements are sparse and there often exists high degeneracy in model solutions. Meanwhile, the power of D.A. to systematically identify parameter sets corresponding to least-action paths is largely unknown. I am excited by the potentially transformative effect that D.A. may have in this arena.


      With currently-available experimental technologies and computational resources, together with powerful model-testing tools from various disciplines, we are posed to make significant advances in understanding how the CNS works on multiple scales. In addition to my research interests, my goals are twofold.

      First, I aim to compare various formulations of D.A. for their efficacy in completing models, and to investigate how D.A. may complement other statistical computational methods such as machine learning. Second: I aim to cultivate an academic environment built upon cross-disciplinary collaborations. This quest involves: 1) seeking new tools and theoretical constructs that may be applied across disciplines, 2) fostering collaborations across traditionally-segregated scientific communities, and 3) encouraging students to consider research opportunities outside traditional home departments.


      Armstrong et al. (2016): https://arxiv.org/abs/1612.03516; Armstrong & Abarbanel, J. Neurophysiol, doi:10.1152/jn.00438.2016; Abarbanel, H.D.I. Springer, NY (2013); Daou et al. J. Neurophysiol. 110, 5 (2013); Hahnloser, R.H. et al., Nature 419, 6902 (2002)

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      Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Researchers can login to make corrections and additions, or contact us for help.
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      1. Armstrong, E. . A Neural Networks Approach to Predicting How Things Might Have Turned Out Had I Mustered the Nerve to Ask Barry Cottonfield to the Junior Prom Back in 1997. 2017.
      2. Armstrong, E. An optimization-based approach to estimating the connectivity of a pattern-generating network. (To be submitted to J. Neurophysiol., ~ July 2017). 2017.
      3. Armstrong, E., Patwardhan, A.V., Johns, L., Kishimoto, C.T, Abarbanel, H.D.I., Fuller, G.M. An Optimization-Based Approach to Neutrino Flavor Evolution. Submitted to Phys. Rev. D. 2016.
      4. Kadakia N, Armstrong E, Breen D, Morone U, Daou A, Margoliash D, Abarbanel HD. Nonlinear statistical data assimilation for HVC[Formula: see text] neurons in the avian song system. Biol Cybern. 2016 Dec; 110(6):417-434. PMID: 27688218.
        View in: PubMed
      5. Armstrong E, Abarbanel HD. Model of the songbird nucleus HVC as a network of central pattern generators. J Neurophysiol. 2016 Nov 01; 116(5):2405-2419. PMID: 27535375.
        View in: PubMed
      6. Armstrong, E. . Pipe-cleaner Model of Neuronal Network Dynamics. 2016.
      7. Armstrong, E. . Non-detection of the Tooth Fairy at Optical Wavelengths. Journal of Irreproducible Results, 2014, 52, 3, 22-25. 2012.
      8. Abarbanel, H.D.I., Armstrong, E., Breen, D., Shirman, S., Margoliash, D. . A Unifying View of Synchronization for Data Assimilation in Complex Nonlinear Networks. Accepted by Chaos: March 2017.
      9. Abarbanel, H.D.I., Shirman, S., Armstrong, E., Dean D. Extracellular Potentials as Data Assimilation Measurement Functions for the Dynamics in Networks of Neurons (in preparation).
      10. Breen, D., Shirman, S., Armstrong, E., Daou, A., Margoliash, D., Abarbanel, H.D.I. . HVC Interneuron Properties from Statistical Data Assimilation.
      11. Armstrong, E., Patterson, J., Michelsen, E., Thorstensen, J., Uthas, H., Vanmunster, T., Hambsch, F.-J., Roberts, G., Dvorak, S. . Orbital, Superhump, and Superorbital Periods in the Cataclysmic Variables AQ Mensae and IM Eridani, 2013, Monthly Notices of the Royal Astronomical Society (MNRAS), 435, 707.
      12. Armstrong, E., Patterson, J., Kemp, J. . Two Photometric Periods in the AM CVn System CP Eridani, 2012, MNRAS, 421, 2310.
      13. Armstrong, E. . GRB 060102: MDM Observation, 2006, GRB Coordinates Network, Circular Service, 4427, 1.
      14. Skinner, J., Thorstensen, J., Armstrong, E., Brady, S. . The New Eclipsing Cataclysmic Variable SDSS 154453+255, 2011, Publications of the Astron. Soc. of the Pacific (PASP), 123, 901.
      15. Copperwheat, C.M., Marsh, T., Dhillon, V., Littlefair, S., Woudt, A., Warner, B., Patterson, J., Steeghs, D., Kemp, J., Armstrong, E., Rea, R. . The Photometric Period in ES Ceti, 2011, MNRAS, 413, 3068.
      16. Dai, X, Halpern, J., Morgan, N., Armstrong, E., Mirabal, N., Haislip, J., Reichart, D., Stanek, K. . Optical and X-Ray Observations of GRB 060526: A Complex Afterglow Consistent with an Achromatic Jet Break, 2007, Astrophysical Journal (Ap J), 658, 509.
      17. Thorstensen, J; Armstrong, E. . Is FIRST J102347.6+003841 Really a Cataclysmic Binary?, 2005, Astronomical Journal (AJ), 130, 759.
      18. Patterson, J., Thorstensen, J., Armstrong, E. . The Dwarf Nova PQ Andromedae, 2005, PASP, 117, 922.
      19. Patterson, J., Kemp, J., Harvey, D., Fried, R., Rea, R., Monard, B., Cook, L., Skillman, D., and 12 co-authors. Superhumps in Cataclysmic Binaries. XXV. qcrit, epsilon(q), and Mass-Radius, 2005, PASP, 117, 1204.
      20. Patterson, J., Thorstensen, J., Vanmunster, T., Fried, R., Martin, B., Campbell, T., Robertson, J., Kemp, J., Messier, D., Armstrong, E. . Rapid Oscillations in Cataclysmic Variables. XVI. DW Cancri, 2004, PASP, 116, 516.
      21. Pretorius, M.L. Woudt, P., Warner, B., Bolt, G., Patterson, J., Armstrong, E. . High-speed photometry of SDSS J013701.06 - 091234.9, 2004, MNRAS, 352, 1056.
      22. Mirabal, N. Halpern, J., Chornock, R., Filippenko, A., Terndrup, D., Armstrong, E., Kemp, J., Thorstensen, J., Tavarez, M., Espaillat, C. GRB 021004: A Possible Shell Nebula around a Wolf-Rayet Star Gamma-Ray Burst Progenitor, 2003, Ap J, 595, 935.
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