Dylan M Paiton, PhD
|2007-2011||Electrical Engineering||New Mexico Institute of Mining and Technology|
|2011-2013||Los Alamos National Laboratory, Los Alamos, NM, United States|
|2013-2019||Vision Science||University of California, Berkeley, Berkeley, CA, United States|
|2019-2021||Theoretical Physics||Eberhard Karls Universität Tübingen, Tübingen, Baden-Württemberg, Germany|
Area:vision science, artificial intelligence, machine learning, visual neuroscience
How does our visual system convert noisy signals into what we perceive? How does the brain represent the world in a way that is both efficient and useful? These questions are what motivate my research. I analyze model neural networks to develop hypotheses for the functional roles of feedback and lateral connectivity in cortex. I study how the class of nonlinearities used for artificial neurons shape their response properties and expressive power. I also develop probabilistic hierarchical models of natural images to advance theoretical frameworks for how we see.
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|Paiton DM, Frye CG, Lundquist SY, et al. (2020) Selectivity and robustness of sparse coding networks. Journal of Vision. 20: 10|
|Lundquist SY, Paiton DM, Nowers BM, et al. (2013) Biologically inspired distributed sensor networks: Collective signal amplification via ultra-low bandwidth spike-based communication Proceedings of the International Joint Conference On Neural Networks|
|Paiton DM, Brumby SP, Kenyon GT, et al. (2012) Combining multiple visual processing streams for locating and classifying objects in video Proceedings of the Ieee Southwest Symposium On Image Analysis and Interpretation. 49-52|