  With high forward gain, a negative feedback system has the ability to perform
the inverse of a linear or non linear function that is in the feedback path.
This property of negative feedback systems has been widely used in analog
circuits to construct precise closed-loop functions. This paper describes how
the property of a negative feedback system to perform inverse of a function can
be used for training neural networks. This method does not require that the
cost or activation functions be differentiable. Hence, it is able to learn a
class of non-differentiable functions as well where a gradient descent-based
method fails. We also show that gradient descent emerges as a special case of
the proposed method. We have applied this method to the MNIST dataset and
obtained results that shows the method is viable for neural network training.
This method, to the best of our knowledge, is novel in machine learning.
