Nov 4, 2018

Neural Population Control via Deep Image Synthesis

BioRxiv : the Preprint Server for Biology
Pouya BashivanJames DiCarlo

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 a targeted 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, already 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 non-invasively set desired internal brain states at neuron-level resolution, and suggest that more accurate ANN models would produce even more accurate control.

  • References
  • Citations

References

  • We're still populating references for this paper, please check back later.
  • References
  • Citations

Citations

  • This paper may not have been cited yet.

Mentioned in this Paper

Superior Lingular Vein
Psilogramma anne
Neurons
Brain
Procedure on Brain
Neural Network Simulation
Site
Primates
Receptive Field
Depth

About this Paper

Related Feeds

Cell Adhesion Molecules in the Brain

Cell adhesion molecules found on cell surface help cells bind with other cells or the extracellular matrix to maintain structure and function. Here is the latest research on their role in the brain.

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.