Abstract
We propose models and a method to qualitatively explain the receptive field properties of complex cells in the primary visual cortex. We apply a learning method based on the information maximization principle in a feedforward network, which comprises an input layer of image patches, simple cell-like first-output-layer neurons, and second-output-layer neurons (Model 1). The information maximization results in the emergence of the complex cell-like receptive field properties in the second-output-layer neurons. After learning, second-output-layer neurons receive connection weights having the same size from two first-output-layer neurons with sign-inverted receptive fields. The second-output-layer neurons replicate the phase invariance and iso-orientation suppression. Furthermore, on the basis of these results, we examine a simplified model showing the emergence of complex cell-like receptive fields (Model 2). We show that after learning, the output neurons of this model exhibit iso-orientation suppression, cross-orientation facilitation, and end stopping, which are similar to those found in complex cells. These properties of model neurons suggest that complex cells in the primary visual cortex become selective to features composed of edges to increase the variability of the output.
Highlights
A fundamental question that is often raised in neuroscience is how to determine the principle that underlies neural information coding in the brain
The independent component analysis (ICA) models have revealed that ICA of natural images generates output units with simple celllike receptive field properties. These results suggest that the assumption of the statistical independence of output neurons is a promising principle of neural information coding
To generate sign-insensitive complex cells, Model 1 does not require inputs to be insensitive to the signs of pixels
Summary
A fundamental question that is often raised in neuroscience is how to determine the principle that underlies neural information coding in the brain. The primary visual cortex (V1) is an ideal subject for this type of investigation because experimental results regarding the receptive field properties, single-cell electrophysiology, and topographic selectivity map are accumulated in V1. These experimental results allow us to screen the proposed principles of neural information coding by comparing the behavior of the model on the basis of each principle with the receptive field properties of neurons in V1. This screening provides us with a way to deal with the general principle of neural information coding in the cerebral cortex. If the activity of neurons is independent and uncorrelated, the number of neurons firing simultaneously decreases, and the neuronal activity becomes sparse
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