I started out as an engineer wanting to build robots inspired by how brains work, and I ended up as a neuroscientist attempting to understand how nervous systems process information, inspired and guided by engineering principles. I first learned about neural networks as a student at Stanford, through Bernie Widrow’s course on Adaptive Filters and Misha Pavel and Ilan Vardi’s Connectionist Models seminar (1986/87). I then found my way to Pentti Kanerva’s Sparse Distributed Memory (SDM) research group at NASA/Ames, where I worked for two years as a research assistant to develop vision applications of SDM. During this time I learned about Charlie Anderson and David Van Essen’s work on ‘shifter circuits,’ which eventually led to my doing a Ph.D. under their joint supervision as a student in the CNS program at Caltech (1989-1994). My thesis was on dynamic routing circuits, essentially a generalization of shifter circuits which could serve as a neural mechanism for forming position and size (and rotation) invariant representations in the visual cortex. Toward the end of my thesis work I learned about David Field’s work on models of sensory coding based on natural image statistics, which seemed like a promising way to learn feature representations at various stages of the cortical hierarchy. One of my goals ever since has been to bring these two ideas together – dynamic routing and feature learning – to build a hierarchical model of the visual cortex. My first faculty job was at UC Davis, initially in Psychology and then Neurobiology, Physiology and Behavior, along with the Center for Neuroscience, from 1996-2005. Along with Pentti Kanerva and Fritz Sommer, I helped Jeff Hawkins to launch the Redwood Neuroscience Institute in 2002. This was incorporated into UC Berkeley’s program in 2005, where I have remained since. I am currently Professor in the Helen Wills Neuroscience Institute and the School of Optometry, and I direct the Redwood Center for Theoretical Neuroscience.