Abstract

This paper introduces an application of artificial neural networks for visualization of functions of neurons in the higher visual areas of the brain. First, a model that enables the prediction of an evoked neural response was implemented. The model has a correlation coefficient of up to 0.82 for certain cortical columns. Then, an approach to explaining representations encoded by neurons was proposed. The approach is based on generating images maximizing an activation in the model. A comparison of the visualization results with the experimental data suggests that the approach can be used to study the properties of the higher-level areas of the visual cortex.

© 2018 Optical Society of America

PDF Article

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription