Keywords
Natural Scenes, nonlinear neural response, sparse coding network
Abstract
In artificial and biological networks, it is a common accepted practice to describe a neurons (biological or artificial) response properties by a two-dimensional feature map (receptive field). However, real neurons have nonlinear response properties which are not represented by their receptive fields. The efficient coding mechanisms such as sparse coding network or ICA, learn the response properties of V1 neurons from natural images using neural networks. These networks learn the receptive fields which are similar to the receptive fields of V1 neurons. These networks also produces some of the nonlinearities (such as end-stopping and non-classical surround effect), which are exhibited by V1 neurons. Here we provide a geometric characterization of these non-linearities in sparse coding networks. This geometric characterization provides more description about a neuron’s nonlinear response properties than its receptive field. We believe this approach can provide a deeper understanding of how and why sparse representation gives rise to nonlinear responses in V1 neurons.
Start Date
11-5-2016 2:25 PM
End Date
11-5-2016 2:50 PM
A geometric approach to sparse coding yields insight into nonlinear responses
In artificial and biological networks, it is a common accepted practice to describe a neurons (biological or artificial) response properties by a two-dimensional feature map (receptive field). However, real neurons have nonlinear response properties which are not represented by their receptive fields. The efficient coding mechanisms such as sparse coding network or ICA, learn the response properties of V1 neurons from natural images using neural networks. These networks learn the receptive fields which are similar to the receptive fields of V1 neurons. These networks also produces some of the nonlinearities (such as end-stopping and non-classical surround effect), which are exhibited by V1 neurons. Here we provide a geometric characterization of these non-linearities in sparse coding networks. This geometric characterization provides more description about a neuron’s nonlinear response properties than its receptive field. We believe this approach can provide a deeper understanding of how and why sparse representation gives rise to nonlinear responses in V1 neurons.