Learning receptive fields using predictive feedback.
Previously, it was suggested that feedback connections from higher- to lower-level areas carry predictions of lower-level neural activities, whereas feedforward connections carry the residual error between the predictions and the actual lower-level activities [Rao, R.P.N., Ballard, D.H., 1999. Nature Neuroscience 2, 79-87.]. A computational model implementing the hypothesis learned simple cell receptive fields when exposed to natural images. Here, we use predictive feedback to explain tuning properties in medial superior temporal area (MST). We implement the hypothesis using a new, biologically plausible, algorithm based on matching pursuit, which retains all the features of the previous implementation, including its ability to efficiently encode input. When presented with natural images, the model developed receptive field properties as found in primary visual cortex. In addition, when exposed to visual motion input resulting from movements through space, the model learned receptive field properties resembling those in MST. These results corroborate the idea that predictive feedback is a general principle used by the visual system to efficiently encode natural input.
Jehee
Janneke F M
JF
Center for Visual Science, Department of Computer Science, University of Rochester, 242 Meliora Hall, Rochester, NY 14627-0270, USA. jjehee@cvs.rochester.edu
Rothkopf
Constantin
C
Beck
Jeffrey M
JM
Ballard
Dana H
DH
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D005246:Feedback; D005544:Forecasting; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010775:Photic Stimulation; D014793:Visual Cortex / Q000502:physiology*; D014794:Visual Fields