Statistical modeling techniques, such as projection pursuit regression (PPR) and convolutional neural network (CNN)approaches, provide state-of-the-art performance in characterizing visual cortical neurons' receptive fields and predicting their responses to arbitrary input stimuli. However, the latent feature components recovered by these methods, particularly that of CNN , are often noisy and lack coherent structures , making it difficult to understand the underlying feature components that are used to construct the receptive fieldfeature tunings of the neurons . In this paper, we showed that using a dictionary of complex sparse codes,  learned from natural scenes based on efficient coding theory, as the front-end for these methods can improve their performance in neural response prediction. At the same time , this approach makes the component features of the models more transparent and interpretable. We consider using these learned dictionaries as the front-end that can effectively imposing priors on the constituent components. We found that models with  complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) . This observation affirms the recent discovery of complex feature selectivity of V1 neurons and also supports the predictions of efficient coding theory under over-complete conditions .
System identification techniques---projection pursuit regression models (PPR) and convolutional neural networks (CNNs)---provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods, particularly those of CNNs , are often noisy and lack coherent structure , making it difficult to understand the underlying component features of a neuron's receptive field . In this paper, we show that using a dictionary of complex sparse codes, which are learned from natural scenes based on efficient coding theory, as the front-end for PPR and CNNs can improve their performance in neuronal response prediction. More importantly , this approach makes the constituent kernels of these models substantially more coherent and interpretable. Extensive experimental results also indicate that these interpretable kernels provide important information on the component features of a neuron's receptive field. In addition, we find that models with a complex sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity . This observation adds further credence to the sparse coding theory as well as empirical findings of complex feature selectivity in V1 .
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Statistical modeling techniques, such as projection pursuit regression
System identification techniques---projection pursuit regression models
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network (CNN)approaches, provide
networks (CNNs)---provide
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characterizing
predicting
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receptive fields and predicting their
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latent feature components
constituent kernels
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that of CNN
those of CNNs
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structures
structure
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feature components that are used to construct the receptive fieldfeature tunings of the neurons
component features of a neuron's receptive field
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showed
show
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which are
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these methods
PPR and CNNs
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neural
neuronal
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At the same time
More importantly
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component features of the models more transparent
constituent kernels of these models substantially more coherent
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We consider using these learned dictionaries as the front-end that can effectively imposing priors on the constituent components. We found
Extensive experimental results also indicate that these interpretable kernels provide important information on the component features of a neuron's receptive field. In addition, we find
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a
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prior
front-end
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classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of
a standard orientation-selective Gabor filter front-end for modeling V1
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earlier identified to exhibit higher-order (HO)
found to exhibit
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but not better for modeling neurons that preferred orientation bars or grating (OT)
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affirms the recent discovery
adds further credence to the sparse coding theory as well as empirical findings
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of
in
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neurons and also supports the predictions of efficient coding theory under over-complete conditions