Research

Encoding by Sensory Neurons
My primary research interest lies at the intersection of signal processing, information theory, and neuroscience. With my collaborators, we have developed a model of energy-constrained coding by sensory neurons, which predicts the neural activity required to minimize encoding error. This line of research aims to answer fundamental questions in neuroscience- how neurons represent and transmit information. Incorporating a metabolic energy-constraint, along with other biological constraints, is critical to understanding how neurons function in living organisms. Improving our understanding of neural encoding affects our entire understanding of the information processing chain in sensory systems had has important implications for artificial systems which interact with neurons.

Relevant work:

  • Kaloti A. S., Johnson E. C., Bresee C. S., Naufel S. N., Perich M. G., Jones D. L., and Hartmann M. J. Z. Representation of stimulus speed and direction in vibrissal-sensitive regions of the trigeminal nuclei: a comparison of single unit and population responses. PLoS ONE 11(7):e0158399 (2016)
  • Johnson E. C., Jones, D. L., Ratnam, R. A Minimum-Error, Energy-Constrained Neural Code is an Instantaneous Rate Code. Journal of Computational Neuroscience, 40.2 (2016).
  • Jones D. L., Johnson E. C., Ratnam R. A stimulus-dependent spike threshold is an optimal neural coder. Frontiers in Computational Neuroscience 9:61 (2015).
  • Johnson E. C., Jones D. L., Ratnam R. Minimum squared-error, energy-constrained encoding by adaptive threshold models of neurons. In Proc. of IEEE ISIT, 2015, pp. 1337–1341

Robotics
My work at Sprite Robotics involved modelling, analysis, and control of robotic systems. Our work was currently funded by an NSF SBIR grant to investigate motion of fast wheeled mobile robots through home and office environments. We developed models of small robots for rapid movement, online parameter learning, stochastic control techniques, and fusion with visual data.

Algorithms for Brain-Machine Interfaces
Another research interest is the design of brain machine interfaces and detection algorithms for brain machine interfaces. I am interested in improving stimulation and decoding in existing brain machine interfaces. Also, I’ve been interested in identifying new application areas where brain machine interfaces might be reasonable interface choices for any individual. This work involves neuroscience and engineering, but also questions of design and usability.

Relevant work:

  • Johnson, E. C., Norton, J. S., Jun, D. M., Bretl, T., Jones, D. L. Sequential Selection of Window Length for Improved SSVEP-Based BCI Classification. Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE., pp. 7060-7063.
  • Interactions Design and Engineering of Adaptive Systems Focal Point Program (2013-2014)- An interdisciplinary team of scientists, engineers, and designers funded by an internal University of Illinois grant program. This team aimed to develop new applications for brain machine interfaces given this interdisciplinary team.

Signal Processing with Sparsity Constraints
Previously, I’ve also explored algorithms for sparse reconstructions and compressed sensing. My work focused on parameterized measurement models with continuous valued parameters. Such models are found in common applications like spectral analysis and array signal processing. I developed recovery algorithms for this case, and I am generally interested in signal processing and machine learning which incorporates sparsity constraints.

Relevant work:

  • Johnson, E. C. Recovery of Sparse Signals and Parameter Perturbations from Parameterized Signal Models. M. sc. thesis, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, May 2013.
  • Johnson, E. C., Jones, D. L. Joint Recovery of Sparse Signals and Parameter Perturbations with Parameterized Measurement Models. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 5900-5904).