|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Learning transformational invariants from natural movies
If you have a question about this talk, please contact Rachel Fogg.
A key attribute of visual perception is the ability to extract invariances from visual input. Here we focus on transformational invariants – i.e., the dynamical properties that are invariant to form or spatial structure (e.g., motion). We show that a hierarchical, probabilistic model can learn to extract complex motion from movies of the natural environment. The model consists of two hidden layers: the first layer produces a sparse representation of the image that is expressed in terms of local amplitude and phase variables. The second layer learns the higher-order structure among the time-varying phase variables. After training on natural movies, the top layer units discover the structure of phase-shifts within the first layer. We show that the top layer units encode transformational invariants: they are selective for the speed and direction of a moving pattern, but are invariant to its spatial structure (orientation/spatial-frequency). The diversity of units in both the intermediate and top layers of the model provides a set of testable predictions for representations that might be found in V1 and MT. In addition, the model demonstrates how feedback from higher levels can influence representations at lower levels as a by-product of inference in a graphical model.
Joint work with Charles Cadieu.
This talk is part of the Signal Processing and Communications Lab Seminars series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsPharmacology Tea Club Michaelmas 2015 Cambridge Energy Forum Occasional Talks in Biochemistry
Other talksThe Red Queen: The Evolution of Sex People, pathogens and parks: ecosystem health research in Tanzania Strategies for managing social media research data – A digital methods development workshop organised by Cambridge University Library and the Digital Humanities Network. Bioinformatics 'A day of comparatively small things': spatial anxiety in the high British Empire Non-linear parametrically excited systems