Design and development of opto-neural processors for simulation of neural networks trained in image detection for potential implementation in hybrid robotics
This work addresses the challenge of implementing low-power, biologically realistic neural networks in hybrid robotics, though it appears incremental as it builds on existing optogenetic and STDP techniques.
The paper tackled the problem of training living neural networks for image detection by proposing a simulated network trained indirectly with backpropagating STDP algorithms using optogenetic activation, achieving accuracy comparable to traditional neural network methods.
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.