For a given neuron, I'm unclear as to how to take a partial derivative of its error and the partial derivative of its weight.

Working from this __web page__, it's clear how the propagation works (although I'm dealing with Resilient Propagation). For a Feedforward Neural Network, we have to 1) while moving forward through the neural net, trigger neurons 2) from the output layer neurons, calculate a total error. Then 3) moving backward, propagate that error by each weight in a neuron, then 4) coming forwards again, update the weights in each neuron.

Precisely though, these are the things I don't understand.

**A)** For each neuron, how do you calculate the partial derivative (__definition__) of the error over the partial derivative of the weight? My confusion is that, in calculus, a partial derivative is computed in terms of an n variable function. And I even understand the chain rule. But it doesn't gel when I think, precisely, of how to apply it to the results of i) linear combiner and ii) sigmoid activation function.

**B)** Using the Resilient propagation approach, how would you change the bias in a given neuron? Or is there no bias or threshold in a NN using Resilient Propagation training?

**C)** How do you propagate a total error if there are two or more output neurons? Does the total-error * neuron weight happen for each output neuron value?