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Learning receptive fields using predictive feedback.

Janneke F M Jehee¹, Constantin Rothkopf, Jeffrey M Beck

¹Center for Visual Science, Department of Computer Science, University of Rochester, 242 Meliora Hall, Rochester, NY 14627-0270, USA. jjehee@cvs.rochester.edu

Journal of Physiology, Paris

|October 28, 2006

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This study demonstrates predictive feedback in the visual system. A new algorithm explains how the brain efficiently processes visual information, learning receptive fields in primary visual cortex and medial superior temporal area (MST).

Area of Science:

Computational neuroscience
Visual system modeling

Background:

Previous models suggested feedback connections carry predictions and feedforward connections carry errors.
A prior computational model learned simple cell receptive fields using this hypothesis.

Purpose of the Study:

To investigate predictive feedback as a mechanism for explaining tuning properties in the medial superior temporal area (MST).
To implement a biologically plausible algorithm based on matching pursuit to model predictive feedback.

Main Methods:

Developed a new algorithm based on matching pursuit to implement the predictive feedback hypothesis.
Trained the model using natural images and visual motion input simulating movements through space.

Main Results:

Related Experiment Videos

The model successfully learned receptive field properties similar to those in the primary visual cortex when presented with natural images.
The model developed receptive field properties resembling those in the medial superior temporal area (MST) when exposed to visual motion input.

Conclusions:

Predictive feedback is a general principle employed by the visual system for efficient encoding of natural input.
The developed algorithm is biologically plausible and effectively models visual processing in different brain areas.