These results expand our conception of the dictionary of features encoded in the cortex, and the approach can potentially reveal the internal representations of any system whose input can be captured by a generative model. This led to the evolution of rich synthetic images of objects with complex combinations of shapes, colors, and textures, sometimes resembling animals or familiar people, other times revealing novel patterns that did not map to any clear semantic category. A genetic algorithm searched this space for stimuli that maximized neuronal firing. N2 - What specific features should visual neurons encode, given the infinity of real-world images and the limited number of neurons available to represent them? We investigated neuronal selectivity in monkey inferotemporal cortex via the vast hypothesis space of a generative deep neural network, avoiding assumptions about features or semantic categories. We thank Richard Born, Shimon Ullman, and Christof Koch for comments. This work was supported by NIH grants R01EY16187, R01EY25670, R01EY011379, P30EY012196, R01EY026025 and NSF STC award CCF-1231216 to the Center for Brains, Minds and Machines at MIT. The authors declare no competing interests.

wrote the manuscript, all authors revised the manuscript, and M.S.L. adapted the algorithm to neurophysiology. implemented the network algorithm and did all the in silico experiments. T1 - Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences Publisher Copyright: 2019 Elsevier Inc.', Funding Information: This work was supported by NIH grants R01EY16187, R01EY25670, R01EY011379, P30EY012196, R01EY026025 and NSF STC award CCF-1231216 to the Center for Brains, Minds and Machines at MIT. Note = 'Funding Information: This work was supported by NIH grants R01EY16187, R01EY25670, R01EY011379, P30EY012196, R01EY026025 and NSF STC award CCF-1231216 to the Center for Brains, Minds and Machines at MIT.

Neurons guided the evolution of their own best stimuli with a generative deep neural network.',

Neurons guided the evolution of their own best stimuli with a generative deep neural network.Ībstract = 'What specific features should visual neurons encode, given the infinity of real-world images and the limited number of neurons available to represent them? We investigated neuronal selectivity in monkey inferotemporal cortex via the vast hypothesis space of a generative deep neural network, avoiding assumptions about features or semantic categories.

What specific features should visual neurons encode, given the infinity of real-world images and the limited number of neurons available to represent them? We investigated neuronal selectivity in monkey inferotemporal cortex via the vast hypothesis space of a generative deep neural network, avoiding assumptions about features or semantic categories.

Xdream evolving images for visual