Michael Beyeler
Advisors: Ione Fine, Geoffrey Boynton
Email: mbeyeler "at" uw.edu
CV: MichaelBeyeler_CV.pdfMy research focuses on developing and testing neurophysiologically inspired algorithms for improved stimulation protocols in patients implanted with retinal prostheses.
Publications
Beyeler M. (2019). Commentary: Detailed Visual Cortical Responses Generated by Retinal Sheet Transplants in Rats With Severe Retinal Degeneration. Front Neurosci, 13, 471.
Beyeler M., Rounds E.L., Carlson K.D., Dutt N. and Krichmar J.L. (2019). Neural correlates of sparse coding and dimensionality reduction. PLoS Comput Biol, 15(6), e1006908.
Brunton B.W. and Beyeler M. (2019). Data-driven models in human neuroscience and neuroengineering. Curr Opin Neurobiol, 58, 21-29.
Beyeler M., Nanduri D., Weiland J.D., Rokem A., Boynton G.M. and Fine I. (2019). A model of ganglion axon pathways accounts for percepts elicited by retinal implants. Sci Rep, 9(1), 9199. | Reprint 7.12MB pdf |
Beyeler M., Rokem A., Boynton G. and Fine I. (2017). Learning to see again: biological constraints on cortical plasticity and the implications for sight restoration technologies. J Neural Eng, . | Reprint 1.82MB pdf |
Beyeler M., Dutt N. and Krichmar J.L. (2016). 3D Visual Response Properties of MSTd Emerge from an Efficient, Sparse Population Code. J Neurosci, 36(32), 8399-415. | Reprint 2.00MB pdf |
Beyeler M., Oros N., Dutt N. and Krichmar J.L. (2015). A GPU-accelerated cortical neural network model for visually guided robot navigation. Neural Netw, 72, 75-87. | Reprint 6.31MB pdf |
Beyeler M., Richert M., Dutt N.D. and Krichmar J.L. (2014). Efficient spiking neural network model of pattern motion selectivity in visual cortex. Neuroinformatics, 12(3), 435-54. | Reprint 479KB pdf |
Beyeler M., Dutt N.D. and Krichmar J.L. (2013). Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule. Neural Netw, 48, 109-24. | Reprint 1.91MB pdf |