|Title||Neural Population Control via Deep Image Synthesis|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Bashivan, P, Kar, K, DiCarlo, JJ|
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.
- CBMM Funded