Neural Population Control via Deep Image Synthesis

TitleNeural Population Control via Deep Image Synthesis
Publication TypeJournal Article
Year of Publication2019
AuthorsBashivan, P, Kar, K, DiCarlo, JJ
JournalScience
Volume364
Issue6439
Date Published05/2019
Abstract

Particular deep artificial neural networks (ANNs) are today’s most accurate models of the primate brain’s ventral visual stream. Here we report that, using an ANN-driven image synthesis method, new luminous power patterns (i.e. images) can be applied to the primate retinae to predictably push the spiking activity of targeted V4 neural sites beyond naturally occurring levels. More importantly, this method, while 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.

URLhttps://science.sciencemag.org/content/364/6439/eaav9436
DOI10.1126/science.aav9436
Download:  PDF icon Author's last draft

Associated Module: 

CBMM Relationship: 

  • CBMM Funded