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Science. 2019 May 3;364(6439). pii: eaav9436. doi: 10.1126/science.aav9436.

Neural population control via deep image synthesis.

Author information

1
Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, and Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, MA, USA.
2
Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, and Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, MA, USA. dicarlo@mit.edu.

Abstract

Particular deep artificial neural networks (ANNs) are today's most accurate models of the primate brain's ventral visual stream. Using an ANN-driven image synthesis method, we found that luminous power patterns (i.e., images) can be applied to primate retinae to predictably push the spiking activity of targeted V4 neural sites beyond naturally occurring levels. This method, although not yet perfect, achieves unprecedented independent control of the activity state of entire populations of V4 neural sites, even those with overlapping receptive fields. These results show how the knowledge embedded in today's ANN models might be used to noninvasively set desired internal brain states at neuron-level resolution, and suggest that more accurate ANN models would produce even more accurate control.

PMID:
31048462
DOI:
10.1126/science.aav9436

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