Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 60,239 bioRxiv papers from 267,831 authors.

Neural Population Control via Deep Image Synthesis

By Pouya Bashivan, Kohitij Kar, James J DiCarlo

Posted 04 Nov 2018
bioRxiv DOI: 10.1101/461525 (published DOI: 10.1126/science.aav9436)

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.

Download data

  • Downloaded 4,027 times
  • Download rankings, all-time:
    • Site-wide: 599 out of 60,239
    • In neuroscience: 73 out of 10,662
  • Year to date:
    • Site-wide: 166 out of 60,239
  • Since beginning of last month:
    • Site-wide: 1,965 out of 60,239

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News