Cognition, Perception, and Cognitive Neuroscience
Campus AffiliationsComputer Science Department Dynamical Neuroscience
Michael Beyeler received a PhD in Computer Science from UC Irvine as well as a BS in Electrical Engineering and a MS in Biomedical Engineering from ETH Zurich, Switzerland. Prior to joining UCSB, he completed a postdoctoral fellowship in the labs of Ione Fine (Psychology, Institute for Neuroengineering) and Ariel Rokem (eScience Institute) at the University of Washington, where he developed computational models of bionic vision. Beyeler is recipient of the National Institutes of Health (NIH) Pathway to Independence Award.
My research focuses on the development of novel methods and algorithms to interface sight recovery technologies such as retinal implants ('bionic eye') with the human visual system, with the ultimate goal of restoring useful vision to the blind.
BW Brunton, M Beyeler (2019), Data-driven models in human neuroscience and neuroengineering, Current opinion in neurobiology 58, 21-29
M Beyeler, GM Boynton, I Fine, A Rokem (2019), Model-based recommendations for optimal surgical placement of epiretinal implants, Medical Image Computing and Computer Assisted Intervention (MICCAI)
M Beyeler, EL Rounds, KD Carlson, N Dutt, JL Krichmar (2019), Neural correlates of sparse coding and dimensionality reduction, PLoS computational biology 15 (6), e1006908
M Beyeler, D Nanduri, JD Weiland, A Rokem, GM Boynton, I Fine (2019), A model of ganglion axon pathways accounts for percepts elicited by retinal implants, Scientific Reports 9 (1), 9199
M Beyeler, GM Boynton, I Fine, A Rokem (2017), pulse2percept: A Python-based simulation framework for bionic vision, Scientific Computing with Python Conference (SciPy)
M Beyeler, A Rokem, GM Boynton, I Fine (2017), Learning to see again: Biological constraints on cortical plasticity and the implications for sight restoration technologies, Journal of Neural Engineering 14 (5)
M Beyeler, N Dutt, JL Krichmar (2016), 3D visual response properties of MSTd emerge from an efficient, sparse population code, Journal of Neuroscience 36 (32), 8399-8415
M Beyeler, N Oros, N Dutt, JL Krichmar (2015), A GPU-accelerated cortical neural network model for visually guided robot navigation, Neural Networks 72, 75-87
M Beyeler, KD Carlson, TS Chou, N Dutt, JL Krichmar (2015), CARLsim 3: A User-Friendly and Highly Optimized Library for the Creation of Neurobiologically Detailed Spiking Neural Networks, IEEE International Joint Conference on Neural Networks (IJCNN)
M Beyeler, ND Dutt, JL Krichmar (2013), Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule, Neural Networks 48, 109-124