Abstract
Efficient coding has been proposed to play an essential role in early visual processing. While several approaches used an objective function to optimize a particular aspect of efficient coding, such as the minimization of mutual information or the maximization of sparseness, we here explore how different estimates of efficient coding in a model with nonlinear dynamics and Hebbian learning determine the similarity of model receptive fields to V1 data with respect to spatial tuning. Our simulation results indicate that most measures of efficient coding correlate with the similarity of model receptive field data to V1 data, that is, optimizing the estimate of efficient coding increases the similarity of the model data to experimental data. However, the degree of the correlation varies with the different estimates of efficient coding, and in particular, the variance in the firing pattern of each cell does not predict a similarity of model and experimental data.
Highlights
Efficient coding has been proposed to play an essential role in early visual processing
While several approaches used an objective function to optimize a particular aspect of efficient coding, we explore here how different estimates of efficient coding in a model with non-linear dynamics and Hebbian learning determine the similarity of model receptive fields to V1 data with respect to spatial tuning
Other efficient coding properties like independence, as estimated by mutual information or the linear correlation of the cell responses lead to the same result, namely an increase in the similarity of the receptive fields (RFs) to V1 data
Summary
Efficient coding has been proposed to play an essential role in early visual processing. Our simulation results indicate that most measures of efficient coding correlate with the similarity of model receptive field data to V1 data, optimizing the estimate of efficient coding increases the similarity of the model data to experimental data
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