Can the powerful backpropagation of error  (backprop) learning algorithm be formulated in a manner suitable for implementation in neural circuitry ?
<coherence> Can the powerful backpropagation of error  (backprop) learning algorithm be formulated in a manner suitable for implementation in neural circuitry ?
Can the powerful backpropagation of error (backprop) has been instrumental in the recent success of deep learning. However, a key question remains as to whether backprop can be formulated in a manner suitable for implementation in neural circuitry ?