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

Quantitative third-harmonic generation imaging of mouse visual cortex areas reveals correlations between functional maps and structural substrates

Murat Yildirim, Ming Hu, Nhat M. Le, Hiroki Sugihara, Peter T. C. So, and Mriganka Sur
Biomed. Opt. Express 11(10) 5650-5673 (2020)

Hierarchical Bayesian inference in the visual cortex

Tai Sing Lee and David Mumford
J. Opt. Soc. Am. A 20(7) 1434-1448 (2003)

Characterizing contrast adaptation in a population of cat primary visual cortical neurons using Fisher information

Szonya Durant, Colin W. G. Clifford, Nathan A. Crowder, Nicholas S. C. Price, and Michael R. Ibbotson
J. Opt. Soc. Am. A 24(6) 1529-1537 (2007)

Hemispheric differences in electrical and hemodynamic responses during hemifield visual stimulation with graded contrasts

Juanning Si, Xin Zhang, Yujin Zhang, and Tianzi Jiang
Biomed. Opt. Express 8(4) 2018-2035 (2017)

Is Fourier analysis performed by the visual system or by the visual investigator

Alfred L. Ochs
J. Opt. Soc. Am. 69(1) 95-98 (1979)