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Neural population control via deep image synthesis.

Pouya Bashivan1, Kohitij Kar2, James J DiCarlo2

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Researchers used artificial neural networks (ANNs) to control neural activity in the primate brain

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Deep artificial neural networks (ANNs) are advanced models of the primate brain's ventral visual stream.
  • Current models offer high accuracy in simulating visual processing.

Purpose of the Study:

  • To investigate the potential of ANNs for non-invasive control of neural activity.
  • To explore the application of ANN-driven image synthesis for precise neural manipulation.

Main Methods:

  • Utilized an artificial neural network (ANN)-driven image synthesis approach.
  • Applied luminous power patterns (images) to primate retinae.
  • Targeted specific V4 neural sites and their populations.

Main Results:

  • Achieved predictable control over spiking activity in targeted V4 neural sites.
  • Demonstrated unprecedented independent control of V4 neural populations, including those with overlapping receptive fields.
  • Showcased the ability to push neural activity beyond naturally occurring levels.

Conclusions:

  • Knowledge from ANNs can be used to non-invasively set desired brain states at the neuron level.
  • More accurate ANN models could lead to even finer control over neural activity.
  • This approach offers a novel method for understanding and potentially modulating brain function.