Design and development of opto-neural processors for simulation of
  neural networks trained in image detection for potential implementation in
  hybrid robotics
Sanjana Shetty
Neural networks have been employed for a wide range of processing
applications like image processing, motor control, object detection and many
others. Living neural networks offer advantages of lower power consumption,
faster processing, and biological realism. Optogenetics offers high spatial and
temporal control over biological neurons and presents potential in training
live neural networks. This work proposes a simulated living neural network
trained indirectly by backpropagating STDP based algorithms using precision
activation by optogenetics achieving accuracy comparable to traditional neural
network training algorithms.